One of the best ways to improve your Data Science skills is to work on real-world problems. Working on real-world problems helps you understand how to solve a Data Science problem from a particular domain or a particular type of data. So, if you are looking for some challenges to improve your Data Science skills, this article is for you. In this article, I’ll take you through 5 challenging Data Science case studies based on real-time business problems you should try.
5 Challenging Data Science Case Studies You Should Try
Below are 5 challenging Data Science case studies you should try to improve your skills in working with data.
Optimizing Cost and Profitability
This Data Science Case Study is based on a food delivery service’s operations, focusing on understanding its cost structure and profitability through a dataset of 1,000 food orders. It challenges you to dissect major cost components, evaluate individual and overall profitability, and propose strategic recommendations for cost reduction, pricing adjustments, and optimization of commission fees and discount strategies.
By analyzing data points such as order values, delivery fees, discounts, and commission fees, you need to find a profitable balance for the service. It also includes the challenge of simulating the financial impact of your strategies to forecast potential improvements in profitability to provide actionable insights for the service to transform losses into profits.
You can find this case study and the dataset here.
Fashion Recommendations Using Image Features
This case study is based on developing a fashion recommendation system that leverages image feature extraction to analyze and recommend similar or complementary fashion items, such as clothing and accessories, to users. Using a dataset of women’s fashion item images, categorized by type, style, colour, and pattern, your challenge here will be to employ a pre-trained Convolutional Neural Network (CNN) model (e.g., VGG16, ResNet) to extract detailed features from each image.
Then, these features, capturing texture, colour, and shape, need to be compared using a similarity measure (e.g., cosine similarity) to find and recommend items from the dataset that visually resemble the user’s input item. The goal is to enhance the shopping experience by providing personalized fashion recommendations based on visual similarity.
You can find this case study and the dataset here.
Quantitative Analysis
This case study involves conducting a quantitative analysis using a dataset that contains detailed information on stock market transactions, including ticker symbols, trading dates, and price points (open, high, low, close, adjusted close) along with trading volume. The objective is to delve into stock market dynamics to enhance investment strategies by identifying long-term price trends, assessing stock volatility, exploring correlations among different stocks for diversification opportunities, and analyzing the risk-return trade-off for various stocks.
This comprehensive approach challenges you to provide a deeper understanding of market movements and inform more strategic investment decisions, focusing on optimizing portfolio management based on empirical data analysis.
You can find this case study and the dataset here.
Light Theme Vs Dark Theme
This case study revolves around an A/B testing experiment conducted by an online bookstore to optimize its website design by comparing two themes: “Light Theme” and “Dark Theme”, aiming to enhance user engagement and increase book purchases. The experiment involves analyzing a dataset containing user interactions and engagement metrics such as click-through rates, conversion rates, bounce rates, and scroll depth, alongside demographic information like age and location, session duration, book purchases, and cart additions.
The primary objective is to ascertain whether there is a statistically significant difference in these key metrics between the two themes to identify which theme promotes better user engagement, higher conversion rates, and increased purchases, thereby guiding the bookstore in making an informed decision on the optimal website theme for boosting performance.
You can find this case study and the dataset here.
B2B E-commerce Fraud
This case study outlines ABC Company’s initiative to verify the accuracy of courier fees charged for delivering orders from its e-commerce platform across India. ABC collaborates with several courier companies, which calculate fees based on product weight and the distance from the warehouse to the customer’s address. To ensure the correctness of these charges, ABC utilizes data from three internal reports (Website Order Report, Master SKU, and Warehouse PIN for all India Pincode mappings) and compares it against the invoices received from the courier companies.
The process involves matching the total order weight, calculated using the SKU master for product weights and rounded to the nearest 0.5 kg, against the shipment weight reported by the couriers. Additionally, ABC verifies the delivery area using the warehouse PIN to Pincode mappings and calculates the total courier charges based on a rate card that specifies fixed fees and additional charges for weight slabs and delivery areas. The goal is to reconcile the internally calculated charges with those billed by the courier companies, ensuring billing accuracy for each order shipment.
You can find this case study and the dataset here.
Summary
So below are 5 challenging Data Science case studies you should try to improve your skills in working with data:
- Optimizing Cost and Profitability
- Fashion Recommendations Using Image Features
- Quantitative Analysis
- Light Theme Vs Dark Theme
- B2B E-commerce Fraud
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