Datasets to Improve Data Science Skills

If you are learning Data Science, it’s important to work on datasets where you are challenged to dig deeper into the data and explore the domain knowledge behind the problem you are solving. So, if you are looking for such datasets, this article is for you. In this article, I’ll take you through 5 datasets that will help you improve your Data Science skills.

5 Datasets to Improve Data Science Skills

Here are 5 datasets which will challenge you with various problems to improve your Data Science skills.

IPL 2024 Match Data

This dataset includes quantitative and qualitative data, such as runs scored, players involved, and specific match events (e.g., wickets). The complexity arises from handling incomplete data (e.g., missing player and wicket kind information), understanding domain-specific terms and rules, and deriving meaningful insights from granular events.

Exploring this data requires blending statistical analysis, data cleaning, and domain knowledge to extract patterns, trends, and actionable insights relevant to cricket, which can deepen one’s expertise in data science through real-world problem-solving. You can find this dataset here.

Delhi Metro Operations

Analyzing the Delhi Metro dataset, which includes multiple files like calendar data, routes data, geographical coordinates, stopping time data, stops data, and trips data can be highly challenging due to its complex, interconnected nature. Each file holds distinct yet interrelated information about transit schedules, routes, stops, and trips, necessitating comprehensive data integration and normalization.

The challenges in merging these datasets, addressing missing values, and ensuring consistency demand advanced data manipulation skills. Furthermore, understanding the contextual significance of the data, such as the scheduling patterns and geographic routing details, requires deep domain knowledge of urban transit systems. You can find this dataset here.

Cost and Profitability of Food Delivery Service Data

Analyzing the dataset for the cost and profitability of a food delivery service, which includes detailed information on orders, delivery fees, payment methods, discounts, commissions, and processing fees, presents several challenges. The complexity arises from integrating and normalizing diverse data types, such as timestamps, numerical values, and categorical variables.

Understanding the interplay between factors like how discounts impact profitability or how various payment methods affect processing fees requires deep domain knowledge of the food delivery industry. This analysis demands robust data cleaning, handling missing or inconsistent values, and deriving meaningful insights to optimize costs and profitability. You can download the dataset from here.

Light Theme and Dark Theme Data

Analyzing the AB testing dataset for website themes, which includes metrics such as click-through rate, conversion rate, bounce rate, scroll depth, session duration, and user demographics, presents several challenges. The dataset’s complexity lies in integrating and normalizing diverse data types—numerical, categorical, and boolean variables.

Understanding the relationships between these variables and how different themes impact user behaviour requires domain knowledge in web analytics and user experience design. This analysis demands robust data cleaning, handling missing or inconsistent values, and applying statistical methods to test hypotheses and draw meaningful conclusions. You can download the dataset here.

Dynamic Pricing

Analyzing the dynamic pricing dataset, which includes variables such as the number of riders and drivers, location category, customer loyalty status, number of past rides, average ratings, time of booking, vehicle type, expected ride duration, and historical cost of rides, presents several challenges. The complexity lies in integrating diverse data types and understanding how these variables interact to influence pricing.

Handling this dataset requires addressing potential issues such as missing values, multicollinearity, and outliers. Additionally, understanding the contextual significance of each variable, such as the impact of customer loyalty or time of booking on pricing, demands deep domain knowledge in transportation and pricing strategies. You can download the dataset here.

Summary

So, here are 5 challenging datasets to improve your Data Science skills:

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