Solving problem statements and case studies is one of the best ways to improve your practical skills in working with data. So, if you are looking for some challenging problem statements to improve your data analysis skills, this article is for you. In this article, I’ll take you through 5 challenging data analysis problem statements you should solve to improve your data analysis skills.
Challenging Data Analysis Problem Statements
Below are some challenging Data Analysis problem statements you should solve to improve your skills in working with data.
Market Size of EVs
This problem statement revolves around performing a Market Size Analysis for electric vehicles (EVs) in the United States, focusing primarily on assessing and predicting the growth of the EV market. The analysis utilizes a dataset containing detailed information about EV registrations, including vehicle identification, location, model specifics, and other attributes.
The main objectives are to evaluate the historical growth trends of EV registrations, forecast future trends, analyze the distribution across various factors, estimate the market size and growth potential, and provide actionable insights to aid stakeholders in decision-making related to production, infrastructure, and policy-making.
You can find this problem statement with the dataset and references to solve it from here.
Recession Trends
This problem statement involves analyzing a dataset containing the monthly GDP growth rates of the United Kingdom from 2020 to 2022 to identify periods of recession. Recessions are defined as significant economic downturns characterized by a noticeable decline in GDP, leading to increased unemployment, income loss, and reduced business activity.
The challenge here is to discern which specific months within the provided timeframe experienced a recession, noting that not every decrease in GDP growth signifies a recession. The analysis will focus on pinpointing those months where the economic contraction meets the criteria of a recession.
You can find this problem statement with the dataset and references to solve it from here.
Cohort Analysis
This problem statement involves conducting a Cohort Analysis using a dataset of user interaction data to understand behaviour and engagement trends over time. The dataset tracks new and returning users and their engagement durations on the first and seventh days of interaction, structured to enable time-series analysis. The analysis aims to identify trends in new user acquisition and returning user retention every week and to analyze how user engagement changes from Day 1 to Day 7.
It also involves detecting weekly patterns or anomalies in user behaviour and investigating their causes. Additionally, the task seeks to explore the relationship between user retention and engagement to assess the impact of engagement strategies. The ultimate goal is to provide insights that could inform marketing strategies, content development, and user experience enhancements.
You can find this problem statement with the dataset and references to solve it from here.
RFM Analysis
This problem statement involves conducting RFM (Recency, Frequency, Monetary value) analysis using a dataset from an e-commerce platform to understand customer behaviour and enhance engagement strategies. The dataset includes details like customer ID, purchase dates, transaction amounts, product information, and location. RFM analysis will segment customers based on their purchase recency, how often they buy, and the monetary value of their purchases.
The goal is to identify various customer segments, such as high-value customers who make frequent and recent purchases, at-risk customers who have not purchased recently, and others who may present new opportunities for targeted marketing campaigns. This segmentation aims to offer insights into customer behaviours, enabling the development of personalized marketing strategies to optimize customer engagement and value.
You can find this problem statement with the dataset and references to solve it from here.
B2B E-commerce Fraud
This problem statement concerns ABC Company, an e-commerce operator in India, verifying the accuracy of shipping charges levied by its courier partners. ABC needs to cross-check the weight and distance-based fees against internal records of order weights (derived from product data and warehouse locations) and delivery distances.
The process involves reconciling data from the company’s order reports, SKU weights, and warehouse-to-customer pincode mappings with the couriers’ invoiced weights, delivery zones, and charged fees. The goal is to produce a detailed comparison and summary analysis identifying any discrepancies in billing, to ensure ABC is neither overcharged nor undercharged for courier services.
You can find this problem statement with the dataset and references to solve it from here.
Summary
So, below are some challenging Data Analysis problem statements you should solve to improve your skills in working with data:
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