data analysis in a case study

Data Analysis Case Study: Learn From Humana’s Automated Data Analysis Project

Lillian Pierson, P.E.

Lillian Pierson, P.E.

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Got data? Great! Looking for that perfect data analysis case study to help you get started using it? You’re in the right place.

If you’ve ever struggled to decide what to do next with your data projects, to actually find meaning in the data, or even to decide what kind of data to collect, then KEEP READING…

Deep down, you know what needs to happen. You need to initiate and execute a data strategy that really moves the needle for your organization. One that produces seriously awesome business results.

But how you’re in the right place to find out..

As a data strategist who has worked with 10 percent of Fortune 100 companies, today I’m sharing with you a case study that demonstrates just how real businesses are making real wins with data analysis. 

In the post below, we’ll look at:

  • A shining data success story;
  • What went on ‘under-the-hood’ to support that successful data project; and
  • The exact data technologies used by the vendor, to take this project from pure strategy to pure success

If you prefer to watch this information rather than read it, it’s captured in the video below:

Here’s the url too:

3 Action Items You Need To Take

To actually use the data analysis case study you’re about to get – you need to take 3 main steps. Those are:

  • Reflect upon your organization as it is today (I left you some prompts below – to help you get started)
  • Review winning data case collections (starting with the one I’m sharing here) and identify 5 that seem the most promising for your organization given it’s current set-up
  • Assess your organization AND those 5 winning case collections. Based on that assessment, select the “QUICK WIN” data use case that offers your organization the most bang for it’s buck

Step 1: Reflect Upon Your Organization

Whenever you evaluate data case collections to decide if they’re a good fit for your organization, the first thing you need to do is organize your thoughts with respect to your organization as it is today.

Before moving into the data analysis case study, STOP and ANSWER THE FOLLOWING QUESTIONS – just to remind yourself:

  • What is the business vision for our organization?
  • What industries do we primarily support?
  • What data technologies do we already have up and running, that we could use to generate even more value?
  • What team members do we have to support a new data project? And what are their data skillsets like?
  • What type of data are we mostly looking to generate value from? Structured? Semi-Structured? Un-structured? Real-time data? Huge data sets? What are our data resources like?

Jot down some notes while you’re here. Then keep them in mind as you read on to find out how one company, Humana, used its data to achieve a 28 percent increase in customer satisfaction. Also include its 63 percent increase in employee engagement! (That’s such a seriously impressive outcome, right?!)

Step 2: Review Data Case Studies

Here we are, already at step 2. It’s time for you to start reviewing data analysis case studies  (starting with the one I’m sharing below). I dentify 5 that seem the most promising for your organization given its current set-up.

Humana’s Automated Data Analysis Case Study

The key thing to note here is that the approach to creating a successful data program varies from industry to industry .

Let’s start with one to demonstrate the kind of value you can glean from these kinds of success stories.

Humana has provided health insurance to Americans for over 50 years. It is a service company focused on fulfilling the needs of its customers. A great deal of Humana’s success as a company rides on customer satisfaction, and the frontline of that battle for customers’ hearts and minds is Humana’s customer service center.

Call centers are hard to get right. A lot of emotions can arise during a customer service call, especially one relating to health and health insurance. Sometimes people are frustrated. At times, they’re upset. Also, there are times the customer service representative becomes aggravated, and the overall tone and progression of the phone call goes downhill. This is of course very bad for customer satisfaction.

Humana wanted to use artificial intelligence to improve customer satisfaction (and thus, customer retention rates & profits per customer).

Humana wanted to find a way to use artificial intelligence to monitor their phone calls and help their agents do a better job connecting with their customers in order to improve customer satisfaction (and thus, customer retention rates & profits per customer ).

In light of their business need, Humana worked with a company called Cogito, which specializes in voice analytics technology.

Cogito offers a piece of AI technology called Cogito Dialogue. It’s been trained to identify certain conversational cues as a way of helping call center representatives and supervisors stay actively engaged in a call with a customer.

The AI listens to cues like the customer’s voice pitch.

If it’s rising, or if the call representative and the customer talk over each other, then the dialogue tool will send out electronic alerts to the agent during the call.

Humana fed the dialogue tool customer service data from 10,000 calls and allowed it to analyze cues such as keywords, interruptions, and pauses, and these cues were then linked with specific outcomes. For example, if the representative is receiving a particular type of cues, they are likely to get a specific customer satisfaction result.

The Outcome

Customers were happier, and customer service representatives were more engaged..

This automated solution for data analysis has now been deployed in 200 Humana call centers and the company plans to roll it out to 100 percent of its centers in the future.

The initiative was so successful, Humana has been able to focus on next steps in its data program. The company now plans to begin predicting the type of calls that are likely to go unresolved, so they can send those calls over to management before they become frustrating to the customer and customer service representative alike.

What does this mean for you and your business?

Well, if you’re looking for new ways to generate value by improving the quantity and quality of the decision support that you’re providing to your customer service personnel, then this may be a perfect example of how you can do so.

Humana’s Business Use Cases

Humana’s data analysis case study includes two key business use cases:

  • Analyzing customer sentiment; and
  • Suggesting actions to customer service representatives.

Analyzing Customer Sentiment

First things first, before you go ahead and collect data, you need to ask yourself who and what is involved in making things happen within the business.

In the case of Humana, the actors were:

  • The health insurance system itself
  • The customer, and
  • The customer service representative

As you can see in the use case diagram above, the relational aspect is pretty simple. You have a customer service representative and a customer. They are both producing audio data, and that audio data is being fed into the system.

Humana focused on collecting the key data points, shown in the image below, from their customer service operations.

By collecting data about speech style, pitch, silence, stress in customers’ voices, length of call, speed of customers’ speech, intonation, articulation, silence, and representatives’  manner of speaking, Humana was able to analyze customer sentiment and introduce techniques for improved customer satisfaction.

Having strategically defined these data points, the Cogito technology was able to generate reports about customer sentiment during the calls.

Suggesting actions to customer service representatives.

The second use case for the Humana data program follows on from the data gathered in the first case.

In Humana’s case, Cogito generated a host of call analyses and reports about key call issues.

In the second business use case, Cogito was able to suggest actions to customer service representatives, in real-time , to make use of incoming data and help improve customer satisfaction on the spot.

The technology Humana used provided suggestions via text message to the customer service representative, offering the following types of feedback:

  • The tone of voice is too tense
  • The speed of speaking is high
  • The customer representative and customer are speaking at the same time

These alerts allowed the Humana customer service representatives to alter their approach immediately , improving the quality of the interaction and, subsequently, the customer satisfaction.

The preconditions for success in this use case were:

  • The call-related data must be collected and stored
  • The AI models must be in place to generate analysis on the data points that are recorded during the calls

Evidence of success can subsequently be found in a system that offers real-time suggestions for courses of action that the customer service representative can take to improve customer satisfaction.

Thanks to this data-intensive business use case, Humana was able to increase customer satisfaction, improve customer retention rates, and drive profits per customer.

The Technology That Supports This Data Analysis Case Study

I promised to dip into the tech side of things. This is especially for those of you who are interested in the ins and outs of how projects like this one are actually rolled out.

Here’s a little rundown of the main technologies we discovered when we investigated how Cogito runs in support of its clients like Humana.

  • For cloud data management Cogito uses AWS, specifically the Athena product
  • For on-premise big data management, the company used Apache HDFS – the distributed file system for storing big data
  • They utilize MapReduce, for processing their data
  • And Cogito also has traditional systems and relational database management systems such as PostgreSQL
  • In terms of analytics and data visualization tools, Cogito makes use of Tableau
  • And for its machine learning technology, these use cases required people with knowledge in Python, R, and SQL, as well as deep learning (Cogito uses the PyTorch library and the TensorFlow library)

These data science skill sets support the effective computing, deep learning , and natural language processing applications employed by Humana for this use case.

If you’re looking to hire people to help with your own data initiative, then people with those skills listed above, and with experience in these specific technologies, would be a huge help.

Step 3: S elect The “Quick Win” Data Use Case

Still there? Great!

It’s time to close the loop.

Remember those notes you took before you reviewed the study? I want you to STOP here and assess. Does this Humana case study seem applicable and promising as a solution, given your organization’s current set-up…

YES ▶ Excellent!

Earmark it and continue exploring other winning data use cases until you’ve identified 5 that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that.

NO , Lillian – It’s not applicable. ▶  No problem.

Discard the information and continue exploring the winning data use cases we’ve categorized for you according to business function and industry. Save time by dialing down into the business function you know your business really needs help with now. Identify 5 winning data use cases that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that data use case.

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Exploring Data Analysis Case Study: Use Cases Across 8 Diverse Industries

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Top 8 Industry-specific Data Analysis Case Studies

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Data analysis case study example

  • October 5, 2023 November 8, 2023

Data science is a dynamic field, constantly evolving with the promise of reshaping industries. As we move into the next decade, innovative data collection methods and analytical techniques are set to revolutionize workflow efficiencies. Examining real-life data analysis case study examples is indispensable to grasp the ever-changing landscape of data science.  

Whether you’re considering a career in data science or a seasoned professional keeping an eye on industry trends, we’ve got you covered. In the following sections, we’ll delve into a curated selection of data analysis case study examples, offering insights into how data science drives business success and advances research endeavors.

How to Maximize the Value of Data Analysis Case Studies 

Wrapping up , why learning data analysis case studies is essential .

Data analytics has become indispensable in today’s business landscape, promoting informed decision-making, and uncovering valuable insights across various industries. Case studies in data analytics play a crucial role in illustrating real-world applications and their impacts.  

One of the main reasons to study data analytics case studies is the opportunity to learn from those who have embarked on the field of data-driven decision-making, whether successes or failures.

data analysis in a case study

These case studies provide a rich archive of best practices, strategies, and approaches that have proven effective in different situations. These real-world case studies provide insight into how experts solved challenges in the past, providing inspiration and guidance for students and entry-level data scientists’ input level. 

The case studies also shed light on cases where data analysis efforts failed. These “lessons learned” stories help organizations anticipate potential risks and failures, helping them avoid costly mistakes. Understanding why specific data analytics projects fail to deliver expected results can be as instructive as studying successful projects.

1. E-commerce & Retail

Walmart’s Data-Driven Retail Revolution  

Walmart , a retail giant with a global presence, has embraced data analysis wholeheartedly. With over 10,500 stores across 24 countries and a substantial e-commerce footprint, their fiscal year revenue in 2021 reached a staggering $559 billion. Walmart’s data science and analytics arm, Walmart Labs, plays a pivotal role in its success. They operate the world’s largest private cloud, capable of managing a mind-boggling 2.5 petabytes of data every hour. 

  • Personalized Customer Shopping Experience: 

Walmart employs data analytics to gain deeper insights into customer preferences and shopping behaviours. They optimize merchandise stocking and display strategies in their stores by analyzing big data. This analysis also guides decisions on product discontinuation and brand performance assessment.

data analysis in a case study

  • Order Sourcing and On-Time Delivery Promise: attracts millions of customers, each receiving a real-time estimated delivery date for their purchases. This estimation is powered by a sophisticated backend algorithm that considers customer location, inventory levels, and available shipping methods. The supply chain management system plays a pivotal role in determining the ideal fulfillment center for each order, all while minimizing transportation costs to meet delivery promises. 

  • Packing Optimization:  

Packing items efficiently is a daily challenge in retail and eCommerce. Walmart tackles this by utilizing a recommender system that suggests the most suitable box size to minimize space wastage while accommodating all ordered items within a fixed timeframe. This recommendation system addresses the classic NP-Hard problem known as the Bin Packing Problem. 

In essence, Walmart’s data-driven journey demonstrates how they harness data science and visualization to optimize supply chains, tailor the shopping experience, and drive business growth. These applications showcase how effective data analysis and visualization are essential to Walmart’s commitment to better serving its customers. 

These applications highlight how data science and visualization enable Walmart to improve supply chains, tailor shopping experiences, and drive growth. Explore real-world data science projects for more insights.

Amazon’s Data-Driven Retail Dominance  

The Seattle-based multinational giant Amazon has evolved from an online bookseller into an eCommerce, cloud computing, digital streaming, and AI juggernaut. With over 1,400,000 servers housing an estimated 1,000,000,000 gigabytes of data, Amazon’s relentless innovation in data science sets the gold standard for understanding customers. 

  • Recommendation Systems: 

Amazon’s mastery of data science shines through its recommendation systems. By leveraging customer purchase data, collaborative filtering anticipates users’ needs and suggests products even before they search. Amazon’s Recommendation Based Systems (RBS) generate 35% of annual sales, enhancing user experiences and boosting revenue. 

  • Retail Price Optimization: 

Amazon’s product pricing is a testament to data-driven precision. Predictive models calculate optimal prices that won’t deter customers, determining their purchase likelihood and its potential impact on future buying patterns. This intricate pricing strategy considers diverse variables, including website activity, competitor pricing, product availability, preferences, order history, profit margins, and more.

data analysis in a case study

  • Fraud Detection: 

Operating as a colossal eCommerce entity exposes Amazon to significant retail fraud risks. The company meticulously collects historical and real-time order data as a preemptive measure. Employing machine learning algorithms, Amazon identifies transactions more likely to be fraudulent. This proactive approach curbs potential abuse, such as excessive product returns, safeguarding the business. 

Amazon’s data analytics prowess exemplifies how leveraging vast data volumes can revolutionize eCommerce. From personalized recommendations to precise pricing and fraud prevention, Amazon’s innovative data science applications continue to set industry benchmarks.

Boody: Centralizing Customer Data for Informed Decisions   

In a different retail landscape, Boody represents a notable global apparel business committed to environmentally conscious practices. Managing data from over 2,500 retailers in 15+ countries, both online and offline, posed significant challenges to the Boody’s management board. The influx of customer information, online transactions, and product data was overwhelming. 

To address this, Boody came to us, and Synodus has assisted them with building a comprehensive data integration strategy. We helped them centralize over 150GB of data from ten different sources into a single database, each updated real-time every five minutes. This transformation empowered Boody to gain deeper insights from their data, facilitating faster and more informed decision-making. 

In the ever-evolving e-commerce and retail sector, these case studies illustrate the transformative potential of data analysis, from optimizing operations to enhancing customer experiences and driving sustainable growth. 

#2 Entertainment 


From its humble DVD rental origins, Netflix has evolved into a global streaming giant, boasting 208 million paid subscribers worldwide and 3 billion monthly hours watched. A sophisticated data analytics and recommendation system is central to its meteoric rise, processing a staggering 100 billion daily events. Here’s how they apply data analysis: 

  • Personalized Recommendation Engine: 

Netflix thrives on data, employing over 1300 recommendation clusters that analyze user viewing patterns, viewing times, search queries, and content interactions. With this data, Netflix deploys algorithms like Personalized Video Ranking, Trending Now Ranker, and the Continue Watching Now Ranker to provide each user with a personalized watchlist. The result? A tailored viewing experience that keeps subscribers engaged.   

  • Data-Driven Content Development: 

Netflix leverages data science to decode user behavior, uncovering thematic and genre preferences. This wealth of insights drives content creation, spawning hits like “The Umbrella Academy,” “Orange Is the New Black,” and “The Queen’s Gambit.” By basing their decisions on data, Netflix takes calculated creative risks, confident their audience will embrace these offerings. 

data analysis in a case study

  • Precision Marketing Campaigns: 

Netflix doesn’t leave marketing to chance. They employ data analytics to pinpoint the optimal launch times for shows and ad campaigns, ensuring maximum impact on target audiences. With the help of marketing analytics, Netflix crafts tailored trailers and thumbnails for distinct viewer groups. For instance, they strategically launched the “House of Cards” Season 5 trailer featuring a giant American flag during the American presidential elections, resonating powerfully with their audience. 

In a world flooded with content, Netflix’s data-driven approach enables them to stand out by personalizing recommendations, developing hit shows, and orchestrating impactful marketing campaigns. Through data visualization and analytics, they master the art of entertainment. 


In a world dominated by music streaming, Spotify stands out with 320 million monthly users, 4 billion playlists, and 2 million podcasts. Their success hinges on robust data analytics. Case studies illuminate their data-driven approach: 

  • Real-time Music Recommendations:

Spotify uses Bayesian Additive Regression Trees (Bart) to provide real-time, personalized music recommendations. Bart adapts daily and incorporates audio signals, gender, age, and accent to enhance suggestions. 

  • Tailored Playlists:

‘Daily Mixes’ are Spotify’s answer to personalization. They create daily playlists based on users’ song choices and artist preferences, introducing fresh tracks for an enriched experience. ‘Release Radar’ weekly playlists introduce users to new releases from followed or liked artists.   

data analysis in a case study

  • Precision Targeted Marketing:

Spotify leverages its massive dataset to fine-tune ad campaigns. Machine learning models analyze user behavior, including music preferences, age, gender, and ethnicity. Notably, meme-inspired ads achieved global success.

  • Song Classification:

Spotify employs Convolutional Neural Networks (CNNs) for song and audio track evaluation. This enables precise song recommendations and playlist curation based on lyrics, rhythms, and similarity to other tracks. 

  • Textual Analysis:

Natural Language Processing (NLP) comes into play as Spotify scans articles and blogs for insights into song descriptions and artist details. This analytical approach aids in identifying similar artists and songs for better recommendations. 

These case studies underscore Spotify’s data-driven approach, demonstrating how data visualization and analytics enhance user experiences and drive business growth.

#3 Travel Industry 

A global ride-hailing leader, Uber harnesses data analytics to optimize operations and enhance customer experiences. With 91 million monthly users and 3.8 million drivers as of 2018, Uber handles a staggering 14 million daily trips. Key data-driven applications include: 

  • Dynamic Pricing and Demand Forecasting:

Uber adapts pricing in real-time based on demand, using surge pricing during busy times. The ‘Geosurge’ model, which predicts pricing based on ride demand and location, ensures passengers and drivers know surges, maximizing efficiency. 

  • One-Click Chat (OCC):

Uber simplifies driver-passenger communication with OCC, a machine learning and natural language processing solution. OCC anticipates responses to common queries, enabling drivers to address customer messages with a single click efficiently. This enhances user experience and support. 

  • Customer Retention through Data Insights:

Uber bridges supply-demand gaps using machine learning models. Predictive models anticipate demand across locations, ensuring Uber remains a convenient choice. A tier-based reward system categorizes customers by usage, with higher levels yielding more fabulous perks. Personalized destination recommendations based on users’ travel histories elevate the ride experience. 

Uber leverages data analysis for dynamic pricing, streamlined communication, and enhanced customer retention. These applications highlight data’s transformative role in the travel industry, enabling Uber to offer worldwide efficient, personalized transportation services. 

#4 Healthcare 

Pfizer, a global pharmaceutical giant headquartered in New York, has embraced data analytics to revolutionize healthcare. Known for its breakthroughs in immunology, oncology, cardiology, and neurology, Pfizer gained worldwide recognition with the first FDA-approved COVID-19 vaccine in 2010, later authorized for children aged 5 to 11. Here’s how Pfizer employs data analysis: 

  • Clinical Trial Optimization:

Pfizer leverages artificial intelligence (AI) and machine learning (ML) to enhance clinical trials. Natural language processing and data exploration scrutinize patient records, pinpointing ideal candidates. AI identifies individuals with specific symptoms and predicts potential drug interactions, sidestepping complications. For their 44,000-candidate COVID-19 clinical trial, Pfizer’s AI swiftly discerned signals amid the data deluge. 

  • Efficient Supply Chain:

Data science and ML drive Pfizer’s drug manufacturing and distribution. Advanced forecasting optimizes vaccine and drug demand, while ML models automate and refine production processes. Customized drug supply to distinct patient groups and predictive maintenance further economize operations. 

  • Drug Discovery:

Pfizer capitalizes on data analytics to expedite drug development. Computer simulations and interaction tests expedite drug trials. Collaborating with IBM Watson in 2016, Pfizer harnessed AI for immuno-oncology research. Deep learning models predict bioactivity, synthesis, and potential toxic reactions, saving millions in trials. 

In conclusion, Pfizer’s data-driven approach has redefined healthcare. From clinical trials and supply chains to drug discovery, data analytics empowers Pfizer to innovate, develop life-saving drugs, and combat diseases efficiently. This showcases the profound impact of data analysis in the healthcare sector. 

#5 Oil & Gas 

Shell, a global energy and petrochemical conglomerate operating in over 70 countries with 80,000 employees, is at the forefront of shaping a sustainable energy future. Striving to become a clean energy company by 2050, Shell harnesses digital technologies, including AI and Machine Learning, to drive a significant industry shift. Critical applications of data analytics in the petrochemical sector include: 

  • Precision Drilling:

Shell employs reinforcement learning to enhance drilling processes. This AI-driven approach guides drilling equipment, considering historical drilling data, bit sizes, temperatures, pressures, and seismic activity. By optimizing drilling operations, Shell improves efficiency, reduces machinery wear, and achieves superior results. 

  • Efficient Charging Terminals:

In response to the global push for electric vehicles, Shell utilizes AI to monitor and predict demand for charging terminals. This proactive approach ensures an efficient supply of charging infrastructure, addresses grid load challenges posed by multiple vehicles charging simultaneously, and encourages the adoption of electric cars. 

data analysis in a case study

  • Safety and Monitoring:

Shell pioneers computer vision systems, enhancing security at service stations. These systems can detect risky behaviors such as smoking near fuel pumps and alerting staff to prevent potential hazards. Furthermore, these AI models can be expanded to identify unsafe driving practices and deter theft. 

In summary, Shell’s strategic integration of data analytics, AI, and Machine Learning ushers in a transformative era for the petrochemical industry. By optimizing drilling, promoting electric vehicle adoption, and enhancing safety, Shell exemplifies how data analysis drives innovation and sustainability in the oil and gas sector. 

#6 Supply Chain & Logistics 

In a rapidly evolving business landscape, supply chain analytics has become a game-changer, enhancing operational efficiency and strategic decision-making. Here, we delve into six pivotal examples of supply chain analytics that transform how businesses manage their operations: 

  • Capacity Planning:

Efficient supply chains align procurement and manufacturing capacity with fluctuating sales demands. Prescriptive analytics, powered by mathematical models, guides optimal capacity planning, whether proactive, reactive, or incremental.   

  • Advanced S&OP:

Evolving beyond traditional Sales and Operations Planning (S&OP), the advanced version integrates financial considerations using prescriptive analytics. This enhances S&OP strategies, making them more profitable and agile. 

  • Simulation and Scenario Analysis:

Strategic planning involves envisioning diverse scenarios and strategies. Prescriptive analytics enables optimization-based scenario planning, allowing organizations to simulate multiple scenarios and identify optimal solutions to complex “what-if” inquiries. 

  • Optimization:

Inventory management in omnichannel retail requires precision. Prescriptive analytics leads inventory optimization, crafting accurate models and utilizing non-linear solvers to identify optimal inventory strategies. Solutions may include last-mile distribution warehousing and optimized shipping methods. 

These supply chain analytics examples empower businesses to thrive in a dynamic marketplace. By embracing data-driven decision-making and harnessing prescriptive analytics, organizations enhance their ability to navigate complexity and drive growth and success. 

#7 Finance & Banking 

Finance and banking rely on data analytics for critical functions like risk management, customer data management, and fraud detection. 

  • Risk Analysis Management: 

Cutting-edge algorithms, fueled by machine learning and data science, analyze extensive data, refining risk assessment models independently. This leads to increased responsiveness and profitability for financial institutions. 

  • Customer Data Management: 

Accumulating comprehensive customer information enables the development of behavioral profiles, facilitating personalized sales promotion strategies. Data science automates this process, freeing up employees for higher-value tasks.  

Machine learning algorithms act as vigilant sentinels, rapidly identifying and preventing fraud related to bank cards, accounts, and transactions. For example, they can flag suspiciously expensive purchases from new accounts. Banks also implement systems to monitor abnormal transactions, prompting additional confirmation for unusual activities.   

A Data Analysis Case Study Example from one of the biggest Vietnam’s joint-stock banks  

Our fomer client is one of the largest joint stock banks in Vietnam, and they needed to tackle the challenges in managing vast transaction data volumes, leading to slow response times for managers. Additionally, they lacked Power BI expertise for migration, and their reporting mechanism took up to a month to deliver data to managers, hampered by data silos. 

We has collaborated with them to implement a two-pronged approach, deploying an on-premises Power BI Report Server and providing Power BI training to bank analysts. This empowered efficient data management and analysis, resulting in over 15 automated weekly reports spanning various bank areas, including lending, KYC, and wealth management. This greatly enhanced monitoring and decision-making capabilities, fostering Techcombank’s digital transformation and competitive edge. 

#8 Agriculture 

Traditionally unpredictable agriculture now benefits from data science, enabling farmers to optimize operations, reduce waste, and boost productivity. Technology empowers data-driven decisions on crop selection, livestock, and resource management. 

  • IBM’s Watson Decision Platform: 

IBM is at the forefront of enhancing farm productivity through AI and machine learning. The Watson Decision Platform for Agriculture empowers farmers with crucial data on crops and soil conditions, enabling informed decisions. 

  • Versatility Across Locations: 

This machine learning model is adaptable to diverse locations, regardless of weather or growth conditions. It retrospectively assesses past growing seasons, a vital aspect for validating agriculture insurance claims, managing risk, optimizing supply chains, and predicting commodity prices. 

data analysis in a case study

  • Weather-Based Risk Analysis: 

The platform utilizes weather forecasts to predict risks such as corn pests and disease outbreaks, spore transport, and the likelihood of their occurrence. Farmers can reduce pesticide use and implement preventive measures to safeguard yields with this information. 

Data science’s integration into agriculture offers unprecedented efficiency, sustainability, and profitability opportunities, transforming an age-old industry into a modern, data-driven success story. 

To maximize the value of data analysis case studies, organizations should clearly define their objectives and identify the specific business challenges or opportunities they aim to address. Ensuring alignment between the case study focus and the organization’s overall strategic goals is essential. 

Next, organizations should invest in data quality and diversity. Access to a wide range of data sources, both internal and external, enables a more comprehensive analysis and the discovery of meaningful insights. Data cleansing and preparation are equally crucial to ensure the accuracy and reliability of results. 

Interdisciplinary collaboration is a critical factor in extracting maximum value from case studies. Organizations can gain diverse perspectives and make more informed decisions based on the findings by involving data scientists, domain experts, and decision-makers. 

Furthermore, organizations should view data analysis as an ongoing process. Regularly updating case studies with fresh data allows monitoring progress and adapting strategies as needed. Data security and compliance with privacy regulations must also be a top priority to protect sensitive information. 

Effective communication of the case study findings is essential. Organizations can use clear visualizations and explanations to ensure that the insights are accessible and actionable for all stakeholders. In conclusion, by following these steps, organizations can unlock the full potential of data analysis case studies, driving informed decisions and achieving sustainable growth. 

In closing, data analysis case studies are powerful tools for driving informed decisions and facilitating sustainable growth. At Synodus, we are dedicated to helping organizations harness the complete potential of data analytics. Our expertise spans various industries, including finance, healthcare, agriculture, logistics, and more. 

If you’re ready to embark on a data-driven journey that will propel your organization to new heights, we invite you to contact us today. Explore the comprehensive data solutions offered by Synodus by visiting our website at . Discover a world of possibilities as you leverage data analytics to gain a competitive edge and achieve your business objectives. 

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  • What Is Behavioral Analytics? Definition, Examples And Tools
  • Data Analytics For Marketing: Benefits, Tools And Top Brand Examples

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10 Real World Data Science Case Studies Projects with Example

Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.

10 Real World Data Science Case Studies Projects with Example

BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses.  We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.


Walmart Sales Forecasting Data Science Project

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Table of Contents

Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.

data science case studies

So, without much ado, let's get started with data science business case studies !

With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.

ProjectPro Free Projects on Big Data and Data Science

Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science  at Walmart:

i) Personalized Customer Shopping Experience

Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.

ii) Order Sourcing and On-Time Delivery Promise

Millions of customers view items on, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.

iii) Packing Optimization 

Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .

Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.

Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.

Get Closer To Your Dream of Becoming a Data Scientist with 70+ Solved End-to-End ML Projects

Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:

i) Recommendation Systems

Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.

Here is a Recommender System Project to help you build a recommendation system using collaborative filtering. 

ii) Retail Price Optimization

Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.

Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.

iii) Fraud Detection

Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.

You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.

New Projects

Let us explore data analytics case study examples in the entertainment indusry.

Ace Your Next Job Interview with Mock Interviews from Experts to Improve Your Skills and Boost Confidence!

Data Science Interview Preparation

Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :

i) Personalized Recommendation System

Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.

ii) Content Development using Data Analytics

Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.

iii) Marketing Analytics for Campaigns

Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.

Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.

Get FREE Access to Machine Learning Example Codes for Data Cleaning, Data Munging, and Data Visualization

In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:

i) Personalization of Content using Recommendation Systems

Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.

Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.

ii) Targetted marketing through Customer Segmentation

With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.

iii) CNN's for Classification of Songs and Audio Tracks

Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.

Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.

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Below you will find case studies for data analytics in the travel and tourism industry.

Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience. 

i) Recommendation Systems and Search Ranking Algorithms

Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.

ii) Natural Language Processing for Review Analysis

Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .

Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.

iii) Smart Pricing using Predictive Analytics

The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.

Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics. 

Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:

i) Dynamic Pricing for Price Surges and Demand Forecasting

Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.

ii) One-Click Chat

Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.

iii) Customer Retention

Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.

You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.

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7) LinkedIn 

LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:

i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems

LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.

ii) Recommendation Systems Personalized for News Feed

The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.

iii) CNN's to Detect Inappropriate Content

To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.

Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.

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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :

i) Identifying Patients for Clinical Trials

Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.

ii) Supply Chain and Manufacturing

Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.

iii) Drug Development

Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.

You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.

Access Data Science and Machine Learning Project Code Examples

9) Shell Data Analyst Case Study Project

Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:

i) Precision Drilling

Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used. 

ii) Efficient Charging Terminals

Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.

iii) Monitoring Service and Charging Stations

Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.

Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.

10) Zomato Case Study on Data Analytics

Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:

i) Personalized Recommendation System for Homepage

Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato. 

You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.

ii) Analyzing Customer Sentiment

Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.

iii) Predicting Food Preparation Time (FPT)

Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time. 

Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.

FAQs on Data Analysis Case Studies

A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.

To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.

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Case Study Research in Software Engineering: Guidelines and Examples by Per Runeson, Martin Höst, Austen Rainer, Björn Regnell

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5.1 introduction.

Once data has been collected the focus shifts to analysis of data. It can be said that in this phase, data is used to understand what actually has happened in the studied case, and where the researcher understands the details of the case and seeks patterns in the data. This means that there inevitably is some analysis going on also in the data collection phase where the data is studied, and for example when data from an interview is transcribed. The understandings in the earlier phases are of course also valid and important, but this chapter is more focusing on the separate phase that starts after the data has been collected.

Data analysis is conducted differently for quantitative and qualitative data. Sections 5.2 – 5.5 describe how to analyze qualitative data and how to assess the validity of this type of analysis. In Section 5.6 , a short introduction to quantitative analysis methods is given. Since quantitative analysis is covered extensively in textbooks on statistical analysis, and case study research to a large extent relies on qualitative data, this section is kept short.


5.2.1 introduction.

As case study research is a flexible research method, qualitative data analysis methods are commonly used [176]. The basic objective of the analysis is, as in any other analysis, to derive conclusions from the data, keeping a clear chain of evidence. The chain of evidence means that a reader ...

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What is Data Analytics? Definition, Types, case study, and more.

  • What is Data Analytics?
  • Types of Data Analytics
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This article explores different aspects of data analytics and the skills required to excel in this field. The information will take you through the basics of data analytics to get you started and highlight the pros and cons of each topic.

Data analytics is the process of turning unprocessed data into insightful knowledge that can be put to use. Consider it a type of business intelligence employed to address particular issues and difficulties a firm faces. Finding patterns in a dataset that might inform you about a specific aspect of the business—for example, the behavior of particular client groups or why sales decreased during a specific period—is vital.

To glean valuable insights, a data analyst examines the raw data. Then, to help stakeholders comprehend and act on these insights, they display them in visualizations, such as graphs and charts. Finally, the analysis used will determine the insights that can be drawn from the data. Experts in data utilize four primary forms of research:

  • Descriptive
  • Prescriptive

Descriptive analytics investigates what happened in the past, while diagnostic analytics concentrates on potential causes. Finally, analytics that makes predictions and then recommends the best course of action is known as predictive and prescriptive analytics.

Consequently, data analytics helps analyze historical data and the projection of future trends and behaviors. As a result, instead of basing your strategies and decisions on conjecture, you are making educated decisions based on what the data tells you.

Businesses and organizations can make decisions, plan, and compete in their target markets with a data-driven approach. This allows them to gain a much deeper understanding of their audience, industry, and firm.

The field of data analytics is vast. The four main categories of data analysis are descriptive, diagnostic, predictive, and prescriptive analytics. Each category has a distinct objective and function during the data analysis process. These are also the main applications of data analytics in business.

Descriptive analytics aids in providing explanations for events. These methods condense big datasets into concise summaries that stakeholders can understand. These tactics enable the development of key performance indicators (KPIs), which aid in monitoring success or failure. Many sectors employ metrics like return on investment (ROI). Performance in particular industries is tracked using technical measures. This procedure calls for gathering pertinent data, data processing, data analysis, and data visualization. This procedure offers crucial knowledge about previous performance.

Finding the causes of occurrences is aided by the diagnostic analysis. These techniques support more detailed descriptive analytics. To identify the underlying cause, they carefully evaluate the descriptive analytics results. More investigation is done to understand why the performance indicators changed for the better or worse. Usually, there are three processes involved in this:

  • Find unusual patterns in the data. These adjustments could be sudden shifts in a statistic or a specific market.
  • Data on these anomalies is gathered.
  • Finding links and patterns that explain these anomalies requires applying statistical tools.

Future-related issues can be answered with predictive analytics. These methods use historical data to spot trends and assess their likelihood of repetition. Predictive analytical tools, which use various statistical and machine learning techniques, such as neural networks, decision trees, and regression, offer insight into potential future events.

Prescriptive analytics assists in providing recommendations for action. Predictive analytics insights can be used to make data-driven decisions. In the face of uncertainty, this enables firms to make wise judgments. The foundation of prescriptive analytics tools is machine learning algorithms that can identify trends in massive datasets. Studying previous decisions and events is possible to assess the chance of various outcomes.

These kinds of data analytics give organizations the knowledge they need to make wise decisions. Together, they offer a comprehensive insight into a company’s needs and potential.

types of data analytics

Data analytics can be utilized by any company that collects data, and its application will vary based on the situation. In general, data analytics is employed to facilitate wiser business choices. This aids in lowering total corporate expenses, creating better goods and services, and streamlining organizational procedures and operations.

For example, data analytics may be used to predict future sales and purchase behaviors by detecting past trends. It could be used for security objectives, such as identifying, anticipating, and stopping fraud, particularly in the insurance and financial sectors. It can be applied to improve audience targeting and personalization and analyze the success of marketing campaigns. Data analytics is also used in the healthcare industry to discover the best course of therapy or care for each patient and to produce faster, more accurate diagnoses. Data analytics also improves everyday business processes by locating and removing bottlenecks in particular workflows.

Almost every industry uses data analytics, including marketing, advertising, healthcare, travel, logistics, finance, insurance, media, and entertainment. For example, consider the individualized recommendations you receive from services like Netflix and Spotify, which results from data analytics.

Before we go through some fundamental data analytics methodologies, let’s quickly distinguish between the two types of data you might deal with: quantitative and qualitative. Quantitative data includes anything that can be measured, such as the total sales for a particular year or the percentage of survey respondents who said “yes.” Comparatively, qualitative data is unmeasurable and includes things like what people say in interviews or the content of emails.

Data analysts often work with quantitative data, but some positions also call for you to gather and analyze qualitative data, so it’s beneficial to be familiar with both. In light of this, the following are some of the most popular data analytics methods:

  • Regression analysis : This technique is used to “model” or estimate the relationship between different variables. This analysis can be used to determine certain variables and accurately predict other variables. Regression analysis is mainly used to make predictions. However, regressions alone cannot provide any information on cause and effect; instead, they can only be used to determine whether a group of variables is connected.
  • Factor analysis : This method, also known as dimension reduction, aids data analysts in identifying the underlying factors that influence people’s behavior and decision-making. In the end, it reduces the data from numerous variables into a small number of “super-variables,” making the data more manageable. You might use factor analysis to combine, for instance, three separate variables that each represent a different aspect of consumer satisfaction into a single, comprehensive score.
  • Cohort analysis : A cohort is described as a collection of users who share a particular attribute throughout a specific period; for instance, all consumers who made purchases in March using a mobile device may be grouped as a single cohort. Cohort analysis divides customer data into smaller cohorts so businesses can identify trends and patterns across time that pertain to specific affiliates rather than considering all customer data equally. Therefore, companies can provide a more specialized service after spotting these trends.
  • Cluster analysis : This method aims to locate structures in a dataset. In essence, cluster analysis divides the data into groups that are internally homogeneous and outwardly diverse; in other words, the items in a cluster must be more similar to one another than they are to the items in different sets. When there are no predetermined groups or categories of the data, cluster analysis allows you to see how the data is spread across the dataset. Cluster analysis can be used in marketing, for instance, to pinpoint specific target markets into a broader client base.
  • Time-Series Analysis : Time-series data are data points that measure the same variable over time. Therefore, time-series analysis is gathering data at regular intervals to spot trends and cycles that help data analysts make predictions for the future. For example, time-series analysis can examine how the demand for a given product appears typically at different times to forecast the future market for that product.

We’ve only touched the surface regarding what each technique entails and how it’s applied; these are only a handful of the many strategies that data analysts will employ.

Let’s now look at some equipment that a data analyst might use.

  • Microsoft Excel : Using formulae in a spreadsheet, Microsoft Excel is software that lets you arrange, format, and calculate data. Data analysts can use Microsoft Excel to conduct simple queries and make pivot tables, graphs, and charts. Visual Basic for Applications is a macro programming language included with Excel (VBA).
  • Power BI is a business analytics tool that lets you share insights throughout your organization and visualize your data. Power BI is primarily used for data visualization, much like Tableau. However, while Power BI is a more all-purpose business intelligence application, Tableau is designed with data analysts in mind. Now you can compare powerBI and Tableau .

The list above is some of the tools used by data analysts but don’t let that deter you from assuming they are the only ones used. Like other things getting the hang of the tools is a part of the learning curve.

The use of data analytics happens in various industries. Some of the examples can be as below:

  • Transportation : Data analytics is used to enhance transportation systems and the surrounding intelligence. The research’ predictive methodology aids in identifying transportation issues, including network or traffic congestion. It assists in synchronizing the enormous amount of data and uses them to create and design plans and strategies to plan alternate routes and lessen traffic, minimizing accidents and mishaps.
  • Web : Web search engines like Yahoo, Bing, DuckDuckGo, and Google will return results when you search a data set. Every time you click the search button, search engines use data analytics algorithms to quickly give the most relevant results. Data analytics is used to obtain the data displayed whenever we perform an information search.

And so on. While these are some examples of applications, the benefits of data analysis are very helpful in the business world.

Some of the benefits can be described as follows-

  • Customer Acquisition and Retention : Big Data applications are used by businesses to examine consumer trends and then modify their products and services in response to specific customer needs. This strategy significantly increases revenue and ensures client happiness and loyalty. Couple it with  data collaboration  between various departments to get even better results.
  • Focused and Targeted Promotions : Big Data is used by businesses to provide their target market with customized offerings. For example, companies can learn about customer patterns by tracking point-of-sale and internet purchases. Utilizing these insights, businesses may develop customer-focused, customized marketing that helps them meet client expectations.
  • Potential Risks Identification : Big Data applications are essential for creating efficient risk management strategies and procedures. Risks are reduced by using Big Data in Data Analytics and tools by optimizing complex results for unforeseen events and prospective threats.

A group of disciplines used to mine massive datasets is collectively referred to as data science. Data analytics software provides a more specialized form, which can even be considered a component of the overall procedure. Analytics aims to produce quickly usable, actionable insights based on current inquiries.

Here is a table comparing data analytics and data science:

It is worth noting that these are generalizations and there is overlap between the two fields. Some data analysts may work with machine learning algorithms, and some data scientists may work with structured data.

Unstructured and raw data can be found in big data. Big data’s primary goal is to transform unstructured data into valuable data sets that may be used to derive insightful conclusions or address challenging business issues. Data analytics, however, primarily uses structured data.

Here is a table comparing data analytics and big data analytics:

Again, these are generalizations, and there is an overlap between the two fields. Some data analytics projects may involve working with very large datasets, and big data analytics may involve using traditional statistical analysis techniques.

One of the most interesting case studies is of Netflix. And the same is elaborated on in detail below-

Netflix collects data from its 163 million global subscribers, including what viewers watch and when, what devices they use, whether they pause and resume a show, how the platform rates certain content, and precisely what users search for when looking for new content to watch.

Then, using data analytics, Netflix can link all these different data points to create a comprehensive viewing profile for each user. Then, based on notable trends and patterns within each user’s viewing activity, the recommendation system offers personalized (and amazingly accurate) choices for what the user might want to watch next.

This personalized service significantly impacts the user experience; according to Netflix, personalized recommendations account for over 75% of viewing engagement. Looking at their income and usage numbers, you’ll notice that Netflix constantly dominates the global streaming market—and they’re increasing yearly. This effective use of data analytics also dramatically adds to the company’s success.

how to become data analyst

Who is a data analyst

To find the solution to a problem or provide an answer to a question, a data analyst gathers, purifies, and analyses data sets. They work in various fields, including government, business, finance, law enforcement, and science.

What do data analysts do?

Some of the typical tasks and responsibilities of a data analyst can be as follows:

  • Utilize data visualization software to oversee the distribution of customer satisfaction surveys and report on the outcomes.
  • Develop needs, provide success criteria, manage and carry out analytical projects, and assess outcomes in collaboration with business line owners.
  • Keep an eye on procedures, systems, and practices to spot areas for improvement.
  • To define concepts and assess needs and functional requirements, proactively interact with stakeholders, business units, technical teams, and support teams.
  • Convert crucial inquiries into specific analytical tasks.
  • assemble fresh information to address client inquiries, compiling and organizing data from many sources
  • Using reports and interactive dashboards, use analytical techniques and tools to extract and deliver fresh insights to clients.

The above pointers are to give you a glimpse of the responsibilities of a data analyst, but they aren’t the same. The typical work process of a data analyst would also look like this:

  • Define the questions
  • Collect data
  • Clean the data
  • Visualize and share findings

Skills required to become a data analyst

Some of the most crucial hard and soft talents you’ll need to work as a data analyst are listed below:

  • You’ll need a mathematical mind since data analysts greatly work with numbers!
  • Understanding of programming languages like Python, SQL, and Oracle: As we’ve seen, data analysts use various programming languages to do their tasks. At first, this might seem not easy, but everything can be learned with practice.
  • Data analysts must be able to comprehend what is happening and go further as needed; it is not enough to calculate the facts and present your conclusions. An analytical approach is essential—all it’s in the name!
  • Knowing which method to employ when among the many tools and approaches available to data analysts is essential to their work. Keep in mind that the entire purpose of data analytics is to provide answers to questions and address business difficulties, which calls for sharp problem-solving abilities.
  • Once you’ve gleaned insightful information from your data, it’s critical to communicate your findings in a way that helps the company. In addition to working closely with important business stakeholders, data analysts may also be expected to share and present their findings to the entire organization.

Some of the other important skills are

  • Programming skills: Data analysts often need to write code in languages such as Python or R to clean, manipulate, and analyze data.
  • Statistical analysis: Data analysts should have a strong understanding of statistical concepts and be able to use statistical analysis tools to find trends and patterns in data.
  • Data visualization: Data analysts should be able to use tools such as Excel, Tableau, or D3 to create clear and effective visualizations of data.
  • Data management: Data analysts should be able to work with large datasets and be proficient in using tools such as SQL to extract and manipulate data.
  • Communication: Data analysts should be able to clearly communicate their findings to both technical and non-technical audiences through both written reports and oral presentations.
  • Problem-solving: Data analysts should be able to approach problems in a logical and analytical way and be able to find creative solutions to data-related challenges.
  • Attention to detail: Data analysts should have strong attention to detail and be able to spot errors or anomalies in data.
  • Time management: Data analysts often work on multiple projects at once and should be able to manage their time effectively to meet deadlines.

After reading about the requirements for a job in data analytics and the competencies you’ll need to develop, you might wonder: How can I become a data analyst?

It’s difficult to determine the best data analytics course, as it depends on an individual’s goals and needs. However, here are a few factors to consider when evaluating data analytics courses :

  • Content: Ensure the course covers the topics most relevant to your goals.
  • Instructor: Consider the instructor’s background and expertise in data analytics.
  • Format: Determine whether you prefer a self-paced online course or a more structured, in-person class.
  • Cost: Compare the costs of different courses to find one that fits your budget.
  • Reviews: Read reviews from past students to get a sense of the course’s strengths and weaknesses.

Here is one of the most popular data analytics courses that you can consider:

Data Analytics Program

In today’s market, data has emerged as one of the most plentiful—and valuable—commodities. Big data is a topic that is frequently discussed due to its significance. However, data is only valid when refined, although it is commonly asserted that it is the “new oil.” The value of a company’s data depends on how it is used, which is why the function of the data analyst is becoming more and more crucial.

Here are a few trends and developments that are shaping the future of data analysis:

  • Increasing volume and variety of data: The amount of data being generated is expected to continue to grow exponentially, and this data is becoming more diverse in terms of sources and formats. This presents both challenges and opportunities for data analysts.
  • Machine learning and artificial intelligence: The use of machine learning and artificial intelligence in data analysis is expected to increase, with these technologies being used to automate and enhance many of the tasks currently performed by data analysts.
  • Streaming analytics : The ability to analyze real-time data as it is being generated is becoming increasingly important. This requires the use of specialized tools and techniques, such as streaming analytics.
  • Cloud computing: The use of cloud computing platforms for data analysis is expected to continue to grow, as these platforms offer scalable and cost-effective solutions for storing and processing large amounts of data.
  • Data literacy: As the importance of data grows, there is an increasing need for individuals across all industries to have at least a basic understanding of data analysis. This is leading to a focus on data literacy, with the goal of empowering more people to understand and work with data.

The article gives a thorough overview of the exciting field of data analytics. We’ve covered many topics, from essential tools and methods to some of the most crucial abilities you’ll need to devise to work as a data analyst. It may seem intimidating if you’re brand-new to the profession with all these abilities and prerequisites (not to mention the technical jargon), but it’s crucial not to let that discourage you!

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Case Studies

Data analysis is a process



Ch01a finding a good deal among hotels: data collection.

Vienna, Austria is a popular tourist destination for business and leisure. From the hundreds of places that offer accommodation, we want to pick a hotel that is underpriced relative to its location and quality for a weekday in November 2017. Can we use data to help this decision? What kind of data would we need, and how could we get it?

This case study illustrates how to collect appropriate data from the web on multiple offers. It describes what we want from such data and what data source we would need. The data is collected by web scraping , and it results in a single data table. The case study discusses the data quality from the perspective of the question to answer and how data quality is determined by the way the data was born. There is no dataset to analyze in this case study in this chapter. Subsequent case studies (2A, 3A, 7A, 8A, 9B, 10B) will use the data desctibed here to illustrate steps of data analysis that lead to ultimately answering the main question.


CH01B Comparing online and offline prices: data collection

Do online and offline prices of the same products tend to be the same? To answer that question, we need data on both the online and offline (in store) price of many products. Such data was collected as part of the Billion Prices Project (BPP;, an umbrella of multiple projects that collect price data for various purposes using various methods.

This case study illustrates how to combine different data collection methods and what the challenges are with such data collection. It discusses how products were selected and how prices were measured, and what those methods imply for coverage of observations and reliability of variables. There is no dataset to analyze in this case study. Case study 6A will use the data described here to investigate whether online and offline prices tend to be the same.

CH01C Management quality: data collection

How different are firms and other organizations in the terms of their management practices? Is the quality of management related to how large the firms are? Is it affected by whether the owners are the company founders or their families? To answer these, and many related, questions, we need data on management quality. Such data was collected by the World Management Survey (WMS;, an international research intitative to measure the differences in management practices across organizations and countries.

This case study illustrates how to collect data by surveys . It discusses sampling and its practical issues, and how to use a set of survey questions to measure and abstract concept such as the quality of management. This case study, similarly to the other case studies in this chapter, illustrates the choices and trade-offs data collection involves, practical issues that may arise during implementation, and how all that may affect data quality. There is no dataset to analyze in this case study. Case studies 4A and 21A will use the data described here to investigate how management quality is related to firm size and how it is affected by ownership.

CH02A Finding a good deal among hotels: data preparation

Continuing with our search for a hotel that is underpriced relative to its location and quality in Vienna, we have scraped data from the web, and we’ve got a data table. But how should we start working with this data? In particular, how should we identify hotels, how should we make sure each hotel features only once in the data, and how should we select the variables we would consider for our future analysis?

This case study uses the hotels-vienna dataset to illustrate how to find problems with observations and variables. It illustrates the various types of variables . It shows how to create a tidy data table and how to deal with missing values and duplicates . It allows instructors to demonstrate the importance of data cleaning and the common steps of data wrangling . We described data collection and quality in case study 1A, and we will use the data in case studies 3A, 7A, 8A, 9B, and 10B to illustrate steps of data analysis that lead to finding good deals.

Code : Stata or R or Python or ALL . Data : hotels-vienna . Graphs : .png or .eps

CH02B Displaying immunization rates across countries

Immunization against measles is an effective way to prevent the disease and may save the lives of children. But how do various countries fare in terms of their immunization rates? In particular, how should we structure and use data from many countries and many years to analyze immunization rates across countries and years?

This short case study illustrates how to store multi-dimensional data . It uses the world-bank-immunization dataset with data from the World Development Indicators data website maintained by the World Bank to look at countries’ annual immunization rate and GDP per capita. The case study illustrates the structure of xt panel data data with a cross-sectional and time series dimension (country and year), with two corresponding ID variables and two other variables (immunization rate and GDP per capita). It allows instructors to demonstrate xt panel data tables in long format and wide format . Case study 23B will use the data described here to investigate the effect of immunization on the survival chances of children.

Code : Stata or R or Python or ALL . Data : world-bank-immunization . Graphs : .png or .eps

Ch02C Identifying successful football managers

The English Premier League (EPL) is the top football (soccer) division in England. Team managers, as coaches are known in football, arguably play a very important role in the success of their teams. How can we use two separate data tables on games and managers to identify the most successful football manager in the EPL?

This case study uses the football dataset that covers all games played in the EPL and data on managers, including which team they worked at and when. We create a data table by joining two different data tables, define the measure of success as average points per game, and identify the most successful managers. This case study illustrates how to prepare data for analysis and illustrates linking data tables with different kinds of observations and common problems that can arise while doing so. It is a good example of entity resolution , and how to work with relational data . Case study 24B will use this data to uncover the effect of replacing managers of underperfoming teams on subsequent team performance.

Code : Stata or R or Python or ALL Data : football ). Graphs : .png or .eps


Ch03A Finding a good deal among hotels: data exploration

Further continuing our search for a good deal (a hotel in Vienna that is underpriced for its location and quality), we’ve got a clean data table and identified the variables we want to analyze. How should we start the analysis? In particular, how should we explore the most important variables, why should we do that, and what conclusions can we draw from such exploratory analysis?

This case study uses the hotels-vienna dataset to illustrate how to describe the distribution of variables and how to use the findings to identify potential problems in the data, such as extreme values . The case study also illustrate how to make decisions about extreme values , guided by the ultimate question of the analysis. Along the way, it introduces guidelines for data visualization in general, and the design of histograms in particular. Case studies 1A and 2A describe data collection and cleaning, and we will use the data in case studies 7A, 8A, 9B, and 10B to illustrate further steps of data analysis that lead to finding good deals.

Ch03B Comparing hotel prices in Europe: Vienna vs London

How can we compare hotel markets over Europe and learn about characteristics of hotel prices? Can we visualize two distributions on one graph? What descriptive statistics would best describe each distribution and their differences? Can we visualize descriptive statistics?

This case study uses the hotels-europe dataset and selects 3-4 star hotels in Vienna and London to compare the distribution of prices for a weekday in November 2017. It illustrates the comparison of distributions and the use of histograms and density plots . It illustrates the use of some of the most important descriptive statistics for quantitative variables and their visualizations, box plots and violin plots .

Code : Stata or R or Python or ALL . Data : hotels-europe . Graphs : .png or .eps

Ch03C Measuring home team advantage in football

Is there such a thing as home team advantage in professional football (soccer)? That is, do teams that play in their home stadium tend to perform better? And how should we measure better performance?

This case study uses the football dataset, with data on the games played in the English Premier League (EPL) during the 2016/17 season. The case study shows the use of exploratory data analysis to answer a substantive question and introduces guidelines to present statistics in a good table.

Code : Stata or R or Python or ALL . Data : football ). Graphs : .png or .eps

Ch03D Distributions of body height and income

Are the distributions of body heigh and family income well approximated by theoretical distributions? Answering these questions can help characterize their distributions and provide guidance for future analysis on how to use these variables.

In this very short case study, we examine survey data collected by the Health and Retirement Study in the U.S.A. in 2014 ( height-income-distributions dataset). We show that the height of women aged 55-60 can be described by the normal distribution , whereas the income of their households is reasonably well characterized by the lognormal distribution .


Code : Stata or R or Python or ALL . Data : height-income-distributions . Graphs : .png or .eps

Ch03U1 Size distribution of Japanese cities

What is the size distribution of Japanese cities? Looking at cities with at least 150,000 inhabitants, it follows a power law.

Code : Stata or R or Python or ALL . Data : height-income-distributions .

Ch04A Management quality and firm size: describing patterns of association

Are larger companies better managed? We want to explore the association between management quality and firm size in a particular country (Mexico). To answer this question we need to define the y and x variables in this comparison. In particular, we need to assess how the variables in the dataset correspond to the abstract concepts of management quality and firm size.

This case study uses the Mexican subsample of the World Management Survey dataset ( wms-management-survey ) from 2013. It illustrates how we can measure latent variables by proxy variables in the data and uncover patterns of association betewen those variables. It also illustrates the concepts of conditional probability , conditional distribution , and joint distribution . The case study introduces informative ways to visualize various aspects of patterns of association, such as the stacked bar chart , the scatterplot , the bin scatter , and comparing box plots and violin plots . We have introduced the data used here in case study 1C.


Code : Stata or R or Python or ALL . Data : wms-management-survey . Graphs : .png or .eps

CH05A What likelihood of loss to expect on a stock portfolio?

Can we find out the future likelihood of a large loss on a stock portfolio based on data from the past? We choose the S&P 500 stock market index as our investment portfolio, and we defining a large loss as an at least 5% drop in returns from one day to another. We can easily calculate the proportion of such days in the data, but we are interested in future losses not past ones. To answer our question we need to make generalizations from our data. Such generalizations are bound to bring uncertainty, and we would like to quantify that uncertainty, too.

This case study uses the sp500 dataset that covers day-to-day returns on the S&P 500 stock market index for 11 years to illustrate how we can generalize an estimated statistic from a particular dataset to the population , or general pattern , it represents, and beyond, to the general pattern we are interested in. The case study illustrates the concept of repeated samples . It shows how to estimate the standard error by bootstrap or using a formula, and how to construct and interpret a confidence interval . It also illustrates how to think about external validity . Case study 6B will use the same data to answer a related, but slightly different question.


Code : Stata or R or Python or ALL . Data : sp500 . Graphs : .png or .eps

CH06A Comparing online and offline prices: testing the difference

Do online and offline prices of the same products tend to be the same? Answering this question can help make better purchase choices, understand the business practices of retailers, and it can inform whether we can use online data in approximating offline prices for policy analysis.

This case study uses the billion-prices dataset. We examine online and offline prices of retail products in the U.S. in 2015-16. The case study illustrates how to translate a more abstract question into an inquiry about a statistic (here the average difference). It shows how to formulate a null hypothesis and an alternative hypothesis and how to carry out a hypothesis test in two ways, by calculating the t-statistic and comparing it to an appropriate critival value , or, alternatively, by using the p-value . The case study also illustrates the perils of testing multiple hypotheses and p-hacking . We have introduced the data used here in case study 1B.

Code : Stata or R or Python or ALL . Data : billion-prices . Graphs : .png or .eps {:target=”_blank”}

CH06B Testing the likelihood of loss on a stock portfolio

Will our investment portfolio suffer a large loss with a higher chance than what we can accept? When we want to know what’s the likelihood of large future losses on our portfolio, we can use the confidence interval to quantify the uncertainty from estimating it from data on past returns. But we can ask a more pointed question, too: whether our stock portfolio is will suffer large future losses more often than we can accept. To answer that question we need a different procedure: testing a hypothesis.

This case study uses the sp500 dataset that covers day-to-day returns for 11 years to illustrate how we can test whether a likelihood is greater or less than a specified value. It illustrates testing proportions and how to formulate and carry out a one-sided hypothesis test . The case study is a continuation of case study 5A, using the same data.


Ch07a finding a good deal among hotels with simple regression.

How can we find the hotels that are underpriced relative to their distance from the city center? Continuing the previous case studies that resulted in a clean data table ready for analysis and explored the main variables, we need to uncover how hotel price is related to distance to the city center to know what price to expect at what distances. Then can we identify hotels that are the most underpriced compared to their expected price.

This case study uses the hotels-vienna dataset to illustrate regression analysis with one right-hand-side variable. It shows the use of bin scatters and lowess non-parametric regressions that reveal qualitative patterns of association. In order to find out the quantitative relationship between distance and average price, we apply simple linear regression . The case study illustrates the use of predicted values and regression residuals

CH08A Finding a good deal among hotels with non-linear function

Continuing our search for the best hotel deals in Vienna, we would like to uncover the shape of the price-distance association to get at the best estimates of expected prices at various distances. But what’s the best way to compare prices? Should we compare their absoulte values, or should we aim for a relative comparison, such as percent differences? And how can we do the latter in a regression using cross-sectional data?

This short case study again uses the hotels-vienna dataset, to illustrate linear regression analysis with the use of logarithms . It shows whether and why it may make sense to take logs of the variables in the regression, and how to estimate, and interpret the results of, and choose from level-log regressions, log-level regressions, and log-log regressions.

CH08B How is life expectancy related to the average income of a country?

People tend to live longer in richer countries. How long people live is usually measured by life expectancy; how rich a country is usually captured by its yearly income, measured by GDP. But should we use total GDP or GDP per capita? And what’s the shape of the patterns of association? Is the same percent difference in income related to the same difference in how long people live among richer countries and poorer countries? Finding the shape of the association helps benchmarking life expectancy among countries with similar levels of income and identify countries where people tend to live especially long or especially short lives for their income.

This case study uses the worldbank-lifeexpectancy dataset based on the World Development Index database available at the World Bank webside. It examines cross-sectional data from a single year, 2017, for 182 countries. The case study illustrates the choice between total and per capita measures (here GDP), regressions with variables in logs , and two ways to model nonlinear patterns in the framework of the linear regression: piecewise linear splines , and polynomials . It also illustrates whether and how to use weights in regression analysis, and what that choice implies for the correct interpretation of the results. The case study also shows how to use informative visualization to present the results of regressions.

Code : Stata or R or Python or ALL . Data : worldbank-lifeexpectancy . Graphs : .png or .eps

CH08C Measurement error in hotel ratings

When we search for a good deal among hotels, we care about hotel quality as well as distance to the city center. Online price comparison websites collect customer ratings and publish the average of those ratings, which can serve as a measure of quality. But some averages are based on very few ratings while others are based on hundreds or thousands of ratings. Should we be concerned about ratings coming from very few customers? In particular, what are the consequences of that feature of the data on the results of regression analysis?

This short case study again uses the hotels-vienna dataset, to illustrate the consequences of measurement error for regression analysis. In particular, it shows the effect of classical measurement error in the right-hand-side variable on the estimated slope of a simple linear regression.

CH09A Estimating gender and age differences in earnings

Do women working in the same occupation tend to earn the same as men? And what are the differences in earnings by age? Understanding these differences may help students know what to expect when choosing a particular career.

This case study uses the cps-morg dataset, a cross-section based on the Current Population Survey (CPS) of the U.S. in 2014. It focuses on a single occupation potentially relevant for many students of data analysis, “Market research analysts and marketing specialists”. The case study illustrates how to estimate the standard error of regression coefficients and how to construct and interpret confidence intervals . It also shows how to test hypotheses about regression coefficients and the standard way of presenting regression results in tables. We will ues a larger subsample of the same data in case study 10A to uderstand the sources of gender difference in earnings.

Code : Stata or R or Python or ALL . Data : cps-morg . Graphs : .png or .eps

CH09B How stable is the hotel price–distance to center relationship?

We have uncovered the average price - distance association among hotels in a particular city on a particular date. How generalizable is this pattern to other dates, to other cities, and to other types of accommodations?

This case study uses the hotels-europe data from Vienna, Amsterdam and Barcelona. It illustrates the various kinds of issues with external validity , first focusing on time (different dates), then space (different cities), and groups of observations (different kinds of accommodations).

CH10A Understanding the gender difference in earnings

Women earn less, on average, than man with similar qualifications. How large is that difference among employees with a graduate degree? How does that difference vary with age? And how much do characteristics of the employers and family circumstances of the employees explain of the difference? Understanding the magnitude, patterns, and causes of gender differences in earnings is important from the viewpoint of social equity as well as efficient allocation of labor.

This short case study uses the cps-morg dataset to illustrate the use of multiple regression analysis to help understand the sources of differences between groups of observations. The data is a cross-section based on the Current Population Survey (CPS) of the U.S. in 2014, and the sample is restricted to employees with a graduate degree. The case study illustrates how to estimate and intepret the results of a multiple regression . It shows how to include qualitative right-hand-side variables and interactions in the regression, how to interpret their results, and how to use visualization to present estimtes of nonlinear patterns. The case study illustrates the difficulty of uncovering causal relationships from the results of multiple regression analysis using cross-sectional observational data.

CH10B Finding a good deal among hotels with multiple regression

We return to estimating a good deal among hotels for the last time. We want to find the hotels that are underpriced for their quality and distance to the city center. To do so we first need to uncover expected prices at various levels of distance and quality in a way that reflects all important patterns in the data. Then can we look for hotels that are the most underpriced relative to their expected price.

This case study uses hotels-vienna dataset to illustrate the use of multiple regression analyis for prediction within a sample and residual analysis . It uses the susample of 3-4 star hotels for a single night in Vienna in November 2017. It illustrates the use of a nonlinear specification within a multiple regression and how to identify observations with the largest negative residuals . It also illustrates the use of the y -hat - y plot to visualize the prediction within the sample and the residuals from the predicted values.

CH11A Does smoking pose a health risk?

Are smokers less likely to remain healthy than non-smokers? How about former smokers who quit?

This case study uses the share-health data from the SHARE survey (Survey for Health, Aging and Retirement in Europe). We focus on people who were 50 to 60 years old and said to be in good health in 2011. We look at how they rated their health in 2015 and see who remained healthy ahd who changed their answer to not healthy. This case study illustrates probability models. It shows how to estimate and interpret the results of a linear probability model and the uses of logit and probit models. It compares the linear probability estimates to the estimated marginal differences from logit and probit. Finally, it illustrates when and how the different models may result in different predicted probabilities and how to compare their fit using Brier-score and other measures of fit.

Code : Stata or R or Python or ALL . Data : share-health . Graphs : .png or .eps

CH11B Are Australian weather forecasts well-calibrated?

Should we take an umbrella when weather forecast predicts rain? In particular, how should we trust the weather forecast when it predicts a certain the likelihood of rain? For example, is it true that it rains on 20 percent of the days when it says the likelihood is 20 percent?

This short case study uses the australia-weather-forecast data covering 350 days in 2015/16 and looks at rain forecast and actual rain for the Northern Australian city of Darwin. The case study illustrates how to construct and interpret a calibration curve .

Code : Stata or R or Python or ALL . Data : australia-weather-forecast . Graphs : .png or .eps

CH12A Returns on a company stock and market returns

How do monthly returns on a company stock move together with monthly market returns? The strength of this association is a good measure of how risky the company stock is.

This case study uses the stocks-sp500 dataset covering 21 years of daily data of many company stocks, focusing on the Microsoft stock and the S&P 500 stock market index. We construct monthly time series of percent returns as the percent change in closing price on the last day of each month. The case study illustrates the use of a simple time series regression in changes, focusing on the interpretation and visualization of the results.

Code : Stata or R or Python or ALL . Data : stocks-sp500 . Graphs : .png or .eps

CH12B Electricity consumption and temperature

How does temperature affect residential electricity consumption? Answering this question can help planning for electricity production and assess the potential effects of climate on electricity use.

This case study uses the arizona-electricity dataset that that covers 17 years of monthly electricity consumption data from the state of Arizona in the USA and monthly temperature data from a weather station in its largest city, Phoenix. Using transformed variables of average “cooling degrees” and average “heating degrees” per month, we estimate time series regressions in changes and with and without season dummies. This case study illustrates how to estimate and intepret the results of times series regressions specified in changes . It shows how to handle and interpret seasonality and lagged associations , and how to use Newey-West standard errors or include lagged dependent variables to estimate standard errors that are tobust to serial correlation in time series regressions.

Code : Stata or R or Python or ALL . Data : arizona-electricity . Graphs : .png or .eps


CH13A Predicting used car value with linear regressions

For how much can we expect to sell our used car? And what could price we expect if we waited a year or more? With appropriate data on similar used cars we can estimate various regression models to predict expected price as a function of its features. But how should we select the best regression model for prediction?

This case study uses the used-cars dataset with data from classified ads of used cars from various cities of the U.S.A. in 2018. We select a single model and a single city. The variables include the ask price and various features (age, odometer, cylinders, condition, etc.). We specify several linear regression models to predict the expected price as a function of car features. This case study illustrates the basic logic of carrying out predictive data analysis and model selection , emphasizing the need to achieve a good fit in the live data by selecting a model using the original data and avoiding both underfitting and overfitting the data. It illustrates the use of a loss function such as mean squared error (MSE) as a measure of fit, and it discusses alternative model selection strategies such as the BIC , the training-test split , and its improved version, k-fold cross-validation .

Code : Stata or R or Python or ALL . Data : used-cars . Graphs : .png or .eps

CH14A Predicting used car value: log prices

Continuing with our example of predicting used car prices, how should we decide on whether to transform our target variable? In particular, we can speficy regression models with log price instead of price as the target variable. How to make predictions about price when the target variable is in logs, and how to choose between models with log price versus price as the target variable?

This short case study uses the same used-cars dataset as case study 13A with used car data from several cities in the USA in 2018. The case study illustrates prediction with a target variable in logs . In particular, it shows how to apply log correction to predict a y variable when the model is specified in ln(y) and how to construct appropriate prediction intervals . The case study is a continuation of case study 13A, using the same data, and case study 15A uses the same data, too, to illustrate an alternative predictive model.

CH14B Predicting AirBnB apartment prices: selecting a regression model

London, UK is a popular tourist destination for business and leisure. We want to predict the rental price of an apartment offered by AirBnB in Hackney, a London borough. The results of this prediction can help tourists choose an offer that is underpriced for its features or apartment owners to deciding on what price they could expect if they rented out their apartment on AirBnB.

This case study uses the airbnb dataset that includes rental prices for one night in March 2017 in greater London, and selects a specific borough. After sample design, we specify linear regressions of varing complexity and a model with LASSO. The case study illustrates the various methods of building regression models , including LASSO , and the use of a holdout sample for evaluating the prediction using the best model.

Code : Stata-prep , Stata-study or R-prep , R-study or Python or ALL . Data : airbnb . Graphs : .png or .eps

CH15A Predicting used car value with regression trees

Further continuing with our example of predicting used car prices, is there a better method for prediction than regression? Ideally, such a method would be better than linear regression at capturing the most important nonlinear patterns and interactions between feature variables and arrive at better predictions. The regression tree promises to be such an alternative, but how does it compare to linear regression in an actual prediction?

This case study uses the used-cars dataset from 2018 and its combined Chcicago and Los Angeles subsamples on a specific model, to illustrate regression trees. We grow several regression trees and compare their predictive performance with the performance of linear regressions. This case study illustrates how we can grow a regression tree with the help of the CART algorithm , why we can think of a regression tree as a nonparametric regression , and how such a regression tree could overfit the original data even with stopping rules or pruning . The case study is a continuation of case studies 13A and 14a, using the same data source but a larger subsample of the observations.

CH16A Predicting apartment prices with random forest

Continuing with our question of how to predict AirBnB apartment prices in London, UK, we want to build the best model for prediction. In particular, we want to see how two different methods that combine many regression trees compare to each other, to the single regression tree, and to linear regressions.

We use the airbnb dataset that includes rental prices for one night in March 2017 from the area of Greater London. Using apartment location and various features of accommodation as predictors, we carry out feature engineering and build random forest models and gradient boosting machine method (GBM) models, both ((ensemble methods** that use many regression trees . This case study illustrates prediction with random forest and boosting and the evaluation of such predictions. It shows how to carry out necessary feature engineering , how to set various tuning parameters for the different methods and how those affect the predictions. It also illustrates the use of variance importance plots and partial dependence plots to help understand the patterns of association that drive the predicitons in these black box models . The case study is a continuation of case study 14B, using the same data source but the entire London sample instead of a single borough.

Code : Stata or R-prep , R-study or Python or ALL . Data : airbnb . Graphs : .png or .eps

CH17A Predicting firm exit: probability and classification

Many companies have relationships with other companies, as suppliers or clients. Whether those other companies stay in business in the future or exit is an important question for them. How can we use data on many companies across the years to predict the probability of their exit? And can we classify them into two groups, companies that are likely to exit and companies that are likely to stay in business?

This case study uses the bisnode-firms dataset, a panel dataset with a large number of companies from specific industries in a European country, to illustrate probability prediction and classification. After a good deal of feature engineering we estimate several logit models to predict the probablity of firm exit and compare their performance by 5-fold cross-validation, choose the best model to describe how well it predicts the probabilities on a holdout sample, and use the predicted probabilities and two alternative methods for classification. This case study illustrates how to carry out probability predictions , how to evaluate their goodness of fit and other aspects of predictive performance, how to find an optimal classification threshold with the help of a loss function usign a formula or model-dependent cross-validation, and how to use expected loss and the confusion table to evaluate classifications. It illustrates how the ROC curve visualizes the trade-offs of false positive and negative decisions at various classification thresholds, and how to use random forest for probaility prediction and classification . The case study is also a good example of potential issues with external validity of predictions and how we may detect the possibility of such issues in the original data.

Code : Stata or R-prep , R-study or Python or ALL . Data : bisnode-firms . Graphs : .png or .eps

CH18A Forecasting daily ticket sales for a swimming pool

How can we use transaction data to predict the daily volume of sales? In particular, how can we use data on sales terminal data on tickets sold to a swimming pool to predict the number of tickets sold on each day next year?

This case study uses the swim-transactions dataset with transaction-level data from all swimimng pools for many years in Albuquerque, New Mexico, USA, and selects a single swimming pool. The case study illustrates long-term forecasts. We aggregate the data to daily frequency, discuss data issues and how to solve them, specify several regression models, and select the best by cross-validation. The case study illustrates the use of transaction data in predictive analytics, cross-validation with time series data , the use of trend and, especially, seasonality in making long-term predictions and the use of the autmated Prophet algorithm. It is an example of how evaluating predictions can detect problems that further data work and analysis may solve.

Code : Stata or R or Python or ALL . Data : swim-transactions . Graphs : .png or .eps

CH18B Forecasting a house price index

How can we use data on past home prices, and possibly other variables, to predict how home prices will change in a particular city in the next months?

This case study uses the case-shiller-la dataset with monthly observations on the Case-Shiller home price index for the city of Los Angeles, California, USA between 2000 and 2017. The dataset also contains monthly time series of the unemployment rate and employment rate. After exploratory data analysis we estimate various ARIMA time series models that use the price index, as well as VAR models that use the unemployment and employment rates as well, and we use appropriate cross-validation to select the best model. The case study illustrates how to make use of serial correlation to make short-term forecasts with the help of ARIMA models , how to use other variables and their forecasted values in a vector autoregression (VAR) model, and how to select the best model by cross-validation with time series data that preserves the serial correlation in the data.

Code : Stata or R or Python or ALL . Data : case-shiller-la . Graphs : .png or .eps


Ch19a food and health.

Does eating a lot of fruit and vegetables helps remain healthy? Can we use available data on people’s eating habits and health to uncover those effects? What are the most important problems with using such data to answer our question, and can we do anything about them?

This case study uses the food-health dataset, cross-sectional data collected on the health and eating habits of people as part of the National Health and Nutrition Examination Survey (NHANES, USA); we use data from years 2009-2013. We focus on the subsample of people aged 30-59 years old. The case study illustrates how to define an effect using the potential outcomes framework , how to use causal maps to visualize our assumptions about the causal relationships between variables, how to translate latent variables into their measured proxy variables that can be used in actual analysis, how to think about the sources of variation in the causal variable, and what variables we should condition on in an analysis that attempts to uncover the effect. The case study also illustrates the difficulty of uncovering effects from cross-sectional observational data.

Code : Stata-prep , Stata-study or R-prep , R-study or Python-prep , Python-study or ALL . Data : food-health . Graphs : .png or .eps

CH20A Working from home and employee performance

What is the effect of working from home on employee performance? How can we design an experiment that could measure this effect? Once the data is collected from the experiment, how should we assess its quality, estimate the effect, and evaluate the internal and external validity of the results?

This case study uses the working-from-home data, from an experiment that was carried out at a large travel agency in China. The case study illustrates how to design a field experiment , what are potential issues with internal validity and how to address them in the design or the analysis of the experiment, and how to analyze experimental data. It shows how to check covariate balance and how to interpret its results, how to assess compliance , and how to use regression analysis to estimate the effects of the experiment. The case study also illustrates how the results of the experiment can be used in business decisions , and what issues may arise with the external validity of the results.

Code : Stata or R or Python or ALL . Data : working-from-home . Graphs : .png or .eps

CH20B Fine tuning social media advertising

There are many choices to make when designing an online advertisement, inlcuding text content and details of appearance. Having alternative versions of these details, how can we select the version that would yield the most return?

This case study describes an A/B testing that we carried out on a social media platform. We tested two versions of a text advertising a data analysis program and measured the number of clicks on the ad and the number of actions (leaving one’s email address). The case study illustrates the steps of designing an A/B test in general, and power calculation or sample size calculation in particular. There is no dataset for this case study.

Code : Stata or R or Python or ALL . Data : ab-test-social-media .

CH21A Founder/family ownership and quality of management

Many firms are owned by their founder or family members of their founder. Are such founder/family owned firms as well managed as other kinds of firms and, if there is a difference, how much of that that is due to their ownership as opposed to something else? Can we uncover that effect using cross-sectional observational data on firms and their management practices?

This case study uses the wms-survey-management dataset that we introduced in case study 1C. It is a large multi-country multi-sector survey of companies, measuring their management practices and other company characteristics. We use the cross-sectional sample collected from 24 countries between 2004 and 2015. The case study illustrates the use of thought experiments to clarify what effect we want to measure, how to think about what variables to condition on , and how we may sign the omitted variables bias . Besides multiple regression , it illustrates exact matching and matching on the propensity score , discussing their feasibility, advantages and disadvantages, and comparing their results. The case study is another example illustrating the difficulty to uncover an effect using cross-sectional observational data.

Code : Stata or R-prep , R-study or Python-prep , Python-study or ALL . Data : wms-survey-management . Graphs : .png or .eps

CH22A How does a merger between airlines affect prices?

When two companies merge, the new firm has more market power, and it may use that power to increase price or decrease quality. How can we measure the effect of a merger between two firms on the price they charge? How can we use panel data from many markets to uncover this effect?

This case study uses the US-airlines dataset that is based on 10 percent of all tickets sold on the U.S. market, collected and maintained by the U.S. Department of Transportation. We use this data to evaluate the efect of the merger of American Airlines and US Airways. We define markets and aggregate the data to market-year level and compare price changes across markets with and without the two airlines before the merger. The case study illustrates the use of transaction data to carry out a market-level analysis, the difficulties of defining markets , and using difference-in-differences analysis to estimate an effect. It shows how to examine pre-intervention trends to assess the parallel trends assumption , and how to estimate generalized versions of difference-in-differences analysis adding covariates or using a quantitative treatment variable .

Code : Stata or R-prep , R-study or Python-prep , Python-study or ALL . Data : US-airlines . Graphs : .png or .eps

CH23A Import demand and industrial production

How does import demand of a large country affect the industrial production of a medium-sized open economy? With time series data on imports of the large receiving country and indistrual production of the smaller country, we can estimate a time series regression to uncover the effect. But the the typical time series we can use are not very long, leading to uncertain estimates with wide confidence intervals. How can we use comparable data from other, similar countries to get more precise estimates?

This case study uses the asia-industry dataset with monthly time series of imports to the USA and industrial production in several Asian countries. The case study illustrates the use of time series regression to uncover an effect, including contemporaneous effects , lagged effects and their sum, cumulative effects . It then shows how we can use pooled time series , time series of the same varables from similar subjects (here countries), to arrive at more precise estimates of the same effect.

Code : Stata or R or Python or ALL . Data : asia-industry . Graphs : .png or .eps

CH23B Immunization against measles and saving children

Immunization against measles is an effective way to prevent the disease and may save the lives of children. How can we use data from many countries and several years with immunization and child mortality rates to uncover the effect of immunization on the survival chances of children?

This case study uses the world-bank-immunization dataset with data from the World Development Indicators data website maintained by the World Bank to look at countries’ annual immunization rate and GDP per capita. The case study illustrates panel data regressions with fixed-effects (FE) and estimated in first differences (FD) . It shows how the inclusion of time dummies can condition on aggregate trends of any form, the need to estimate clustered standard errors that are robust to heteroskedasticity as well as serial correlation. It shows that the inclusion of lagged right-hand-side variables can help capture lagged effects and, in the case of FD models, estimate cumilative effects , and the inclusion of lead terms of the right-hand-side variables can capture pre-intervention trends . It also shows how including unit-specific cosntants in an FD model can help capture time trends specific for cross-sectional units . The case study compares the results of FE and FD regressions and discusses their differences.

CH24 Estimating the effect of the 2010 Haiti earthquake on GDP

In January 2010, a strong earthquake hit the Caribbean island country Haiti, with an epicenter very close to the country’s capital. What was the effect of the earthquake on the Haitian economy in the short and the longer run? We can easily measure how total GDP changed in the year of the earthquake and how it evolved in the following years. However, to estimate the effect of the earthquake we need to estimate the counterfactual: how total GDP would have changed if Haiti hadn’t experienced an earthquake. How van we estimate such a counterfactual?

This case study uses the haiti-earthquake dataset with yearly observations of several macro variables for many countries. The case study illustrates comparative case studies and how to construct a synthetic control observation (here country) from data from other countries to estimate the counterfactual. It shows how to select donor pool of observatons similar to the case study observation (Haiti), how to select the variables whose pre-intervention values we want to be similar between the case study observation and the synthetic control observation, and how to use the algorightm of the synthetic control method to assign weights to each observation in the donor pool to construct the synthetic control observation. The case study also illustrates the visualization of the results of synthetic control analysis and the potential issues with the method to uncover the counterfactual.

Code : Stata or R or Python or ALL . Data : haiti-earthquake . Graphs : .png or .eps

CH24 Estimating the impact of replacing football team managers

Success in team sports depends on many things, and the work of the coach, or manager, is likely one of them. When a team performs below expectations, replacing the manager is one of the options teams can consider. How can we use data on all games for several seasons from a professional football (soccer) league and their managers to show how team performance tends to change after a manager is replaced? And how can we use the same data to estimate the counterfactual: how how the performance of low-performing teams would have changed if the manager hadn’t been replaced?

This case study uses the football dataset with all games of the English Premier League (EPL) in 11 seasons and who the team manager was at each game. It illustrates the event study method to estimate contemporaneous and lagged effects with xt panel data . It shows how we can select a control group from all observations that is similar, on average, in pre-intervention variables (here team performance) to estimate the counterfactual post-intervention outcomes, and how to define and select pseudo-interventions that are necessary to define the control group. We used the same dataset in case study 2B.


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