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Top Data Science Case Studies For Inspiration

Last updated:
6th Mar, 2023
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Top Data Science Case Studies For Inspiration

A data science case study refers to a process comprising a practical business problem on which data scientists work to develop deep learning or machine learning algorithms and programs. These programs and algorithms lead to an optimal solution to the business problem. Working on a data science case study involves analysing and solving a problem statement.

Data Science helps to boost businesses’ performance and helps them to sustain their performance. Various case studies related to data science help companies to progress significantly in their fields. These case studies help companies to effectively fulfil customers’ requirements by deeply assessing data for valuable insights. Let’s go through the topmost data science case studies for inspiration.

1) A leading biopharmaceutical company uses Machine Learning and AI to forecast the used medical equipment’s maintenance cost: Healthcare industry

Pfizer employs Machine learning to forecast the maintenance cost of the equipment used in patients’ treatment. The following effective approach the pharmaceutical companies should take to decrease expenses is implementing predictive maintenance using machine learning and AI.

Artificial Intelligence has significantly contributed to this sector’s growth. Multiple advanced tools in this sector are created to develop insights for providing the best treatment to patients. The tools used by the healthcare data science case studies help in specifying treatments as per the patients’ physical conditions. Consequently, these tools help hospitals to save on the expenses incurred in their services.

In medical imaging, data science assists healthcare personnel with productive medications for patients. These case studies help biotech companies to redesign better experiments and modernise the process of developing innovative medicines. They ensure that healthcare companies can spot the problems and avoid them from moving forward. 

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2) The use of Big Data Analytics to monitor student requirements: Education

Data Science has revolutionised how instructors and students interact and improve students’ performance assessment. It helps the instructors to evaluate the feedback obtained from the students and enhance their teaching methods accordingly.

Advanced big data analytics techniques help teachers to analyse their students’ requirements depending on their academic performance.

For example, online education platforms use data science-based python case study to track student performance. Hence, it systematises the assignment evaluation and improves the course curriculum depending on students’ opinions. This case study helps instructors prepare predictive modelling to forecast students’ performance and make the required amendments to teaching methods.

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3) Airbnb uses data science and realised 43,000% growth in five years: Hospitality Industry

Data analytics case study in hospitality helps hotels provide customers with the best possible costs. It helps hotel management to effectively endorse their business, understand the customers’ needs, determine the latest trends in this industry, and more.

This strategy proved very effective for Airbnb because the company realised 43,000% growth in only five years. This case study aims to share a few critical issues Airbnb faced during its development journey. It also expresses information about how the data scientists resolved those issues. Moreover, it adopted data science techniques to process the data, better interpret customers’ opinions, and make reasonable decisions based on customer needs.

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4) Bin Packing Problem uses data science for package optimisation: E-commerce industry

When people search for any product over the internet, the search engine provides suggestions for similar products. The companies selling those products use data science for marketing their products based on the user’s interest via the recommendation system. The suggestions involved in this data analytics case study are typically dependent on the users’ search history.    

Bin Packing problem is a common NP-Hard problem on which data scientists work for optimising packages.

In this sector, big data analytics helps analyse customers’ needs, check prices, determine ways to boost sales and ensure customer satisfaction.

Another best example of this case study is Amazon. It uses data science to ensure customer satisfaction by tailoring product choices. Consequently, the generated data analyses customers’ needs and helps the brand to tailor them accordingly. Amazon utilises its data to serve users with recommendations on offered services and products. As a result, Amazon can persuade its consumers to purchase and make more sales.

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5) Loan Eligibility prediction using Machine Learning: Finance and Banking industry

Data science proves quite beneficial in the finance and banking industry. The corresponding data analyst case study helps identify this industry’s many crucial facets. This Python case study uses Python to predict whether or not a loan must be provided to an applicant. It predicts using a parameter like a credit score. 

It also uses a machine learning algorithm to detect customer anomalies or malicious banking behaviour. When it comes to customer segmentation, data science uses customers’ behaviour to offer tailored services and products. This case study can suggest ways to boost financial performance depending on customers’ transactions and behaviours. 

6) Machine learning models identify, automate and optimise the manufacturing process: Supply Chain Management

Machine learning models can determine efficient supply systems after automating and optimising the manufacturing procedure. It facilitates the customisation of supply drugs to several patients.

The factors like big data and predictive analytics ensure innovation in this industry. This case study analyses the company operations, customers’ demands, products’ costs, reduces supply chain anomalies, and more.   

Another decent example of the use of this data science case study is the package delivery business in supply chain management. Timely and safe package delivery is inevitable for this company’s success. This company can develop advanced navigation tools using cutting-edge big data or Hadoop algorithms. This tool helps the company’s driver to determine the optimum route based on time, distance, and other aspects. Hence, the customers are assured of a flawless shipping experience.

7) Netflix uses over 1300+ recommendation clusters to offer a personalised experience: Entertainment Industry

Netflix uses more than 1300 recommendation clusters to provide a customised experience. These clusters are dependent on consumers’ viewing priorities. Netflix collects users’ data like platform research for keywords optimisation, content pause/rewind time, user viewing duration, etc. This data predicts the viewers’ viewing preference and offers a customised recommendation of shows and series.

The demand for OTT media platforms has significantly increased in the last few years.  Nowadays, people prefer watching web series and movies or enjoying music in their comfort. The widespread adoption of these platforms has changed the face of the entertainment industry. So, many media platforms now use data analytics to ensure user satisfaction and provide necessary recommendations to subscribers.

This data analyst case study is used in renowned media platforms like Netflix and Spotify. Spotify includes a database of a myriad of songs. It uses big data to support online music streaming with a satisfying user experience and create tailored experiences for every user. It uses various algorithms and big data to train machine learning models for offering personalised content.

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8) The use of data analytics to create an interactive game environment: Gaming

There are excellent job opportunities for data scientists willing to embark on their careers in the gaming field. This field uses data science to develop innovative gaming technologies. 

Data inferred from game analytics is employed to obtain detailed information about players’ expectations, forecasting game issues, etc.

The data science case study plays a vital role in the game development path. It assists in obtaining insights from the data to develop games that keep its players engrossed in the play. Another usefulness of this case study is the monetisation of games. It leads to the rapid development of games at a cost-effective price.

Graphics and visual interfaces play key roles in gaming. This case study is used to improve the games’ visual interface. It facilitates attractive graphics in the game to give the users a satisfying game-playing experience.

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These data science case studies are run on some of the most prominent industry names, reflecting the significance of data science in today’s evolving tech world. Data science and its prominence is bound to grow even further in the coming days, and every field is susceptible to its influence. The best you can do is start preparing yourself for the big change, which could be made possible by inheriting in-demand data science skills and experience. 


Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Frequently Asked Questions (FAQs)

1What is the first step while working on any data science case study?

The first step to follow when working on a data science case study is clarifying. It is used to collect more relevant information. Generally, these case studies are designed to be confusing and indefinite. The unorganised data will be intentionally complemented with unnecessary or lost information. So, it is vital to dive deeper, filter out bad info, and fill up gaps.

2What is a hotel recommendation system data science case study?

Usually, a hotel recommendation system works on collaborative filtering. It makes recommendations according to the ratings provided by other customers in the category in which the user searches for a product. This case study predicts the hotel a user is most likely to select from the list of available hotels.

3How is data science beneficial in Pharmaceutical industries?

Two aspects of data science make it easier for the pharmaceutical industry to gain a competitive edge in the market. These aspects are the parallel pipelined statistical models’ processing and the advancements in analytics. The different statistical models, including Markov Chains, facilitate predicting the doctors’ likelihood of prescribing medicines depending on their interaction with the brand.

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