Data Visualization is one of the most valuable skills for any Data Science role. It helps in understanding and presenting data. If you are trying to improve your data visualization skills, work on datasets that can help you understand and present the story behind the data. So, if you are looking for such datasets, this article is for you. In this article, I’ll take you through 3 datasets to practice data visualization.
Datasets to Practice Data Visualization
Below are three datasets you can use to practice data visualization.
IPL Match Dataset
IPL match dataset contains granular information on each ball delivered during an inning, including details such as the batting team, over number, batter, bowler, non-striker, runs scored, extras, total runs, player out, type of wicket, and fielders involved.
This dataset presents several challenges for deeper analysis and visualization due to its high granularity and multiple dimensions. Analyzing this data involves handling categorical variables (teams, players), numerical variables (runs, overs), and complex relationships (partnerships, bowler-batter matchups, dismissal types). Visualizing such datasets requires creating detailed and interactive plots to capture the sequential nature of the game, player performance trends, and comparative analysis between different entities (teams, players).
You can find this dataset here.
YouTube Trending Videos Dataset
The dataset on trending YouTube videos includes numerous attributes such as video ID, title, description, publication date, channel information, category, tags, duration, definition, caption availability, and engagement metrics (views, likes, dislikes, favourites, and comments).
Analyzing this data poses challenges due to its complex and heterogeneous nature. The variety of categorical (e.g., category, tags, channel) and numerical (e.g., view count, like count) variables requires extensive preprocessing to handle missing values, normalize data, and encode categorical variables appropriately. The temporal aspect (publication date) adds another layer of complexity, which necessitates time-series analysis techniques. Additionally, the high dimensionality, with numerous unique tags and categories, complicates the visualization process.
You can find this dataset here.
Delhi Metro Network Dataset
The dataset on the Delhi Metro Network includes attributes such as station ID, station name, distance from the start, metro line, opening date, station layout, latitude, and longitude.
Analyzing this dataset presents several challenges due to its spatial, temporal, and categorical dimensions. The spatial aspect involves geospatial data (latitude and longitude), which requires geographic information system (GIS) techniques to visualize station locations and network connectivity. The temporal component (opening dates) necessitates time-series analysis to understand the development and expansion of the metro network over time.
Additionally, the categorical variables (station layout, metro line) add complexity when analyzing different types of stations and lines. Visualizing this data requires integrating diverse visualization methods, such as interactive maps for spatial data, timelines for network growth, and network graphs to depict connectivity and accessibility. You can download this dataset here.
Summary
So, below are some datasets you can use to practice data visualization concepts:
I hope you liked this article on the datasets you can use to practice data visualization concepts. Feel free to ask valuable questions in the comments section below. You can follow me on Instagram for many more resources.
