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As data analysis is usually complex (and often even repetitive), it can still be enjoyable. It’s an excellent concept to experiment with already-existing internet datasets, and there are many different data-driven initiatives created by enthusiasts and experts.
In this blog, we’ll go over some of the fun datasets you can use to sharpen your talents, which are all free and publicly available, and range from entertainment to animals to sports. For a learning path that is more specifically suited to you.
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What Is a Fun Data Set?
“Fun” datasets cover subjects that the user finds interesting and can be used to investigate connections and find answers to queries that don’t seem obvious at first. Maybe you begin with a query or a theory, and then you look for a dataset to support (or refute) your theory. In addition, you may create your own dataset by utilizing an open API or web scraping methods. You can gather, annotate, and prepare a clean dataset by making your own.
Working with interesting datasets will make your data science portfolio more appealing to employers, who have probably seen their fair share of Netflix-inspired recommendation engines and Twitter sentiment analysis projects. The dataset ought to have enough depth for you to explore and identify trends. Stated otherwise, the minimum required number of rows and columns would be several thousand, together with a suitable proportion of both continuous and categorical values. These datasets may be the ideal resource for discovering fresh ideas in the field of data science. It’s critical to maintain your edge in an industry this dynamic. One guaranteed method to improve your talents on your own is to practice without any pressure.
Fun Data Sets for Analyze
1: Which of the following algorithms is most suitable for classification tasks?
Data Cleaning
Funny Data
The first multimodal humor detection dataset was produced by the Language Technologies Institute and the Human-Computer Interaction lab at the University of Rochester. This fun data set is a fantastic starting point for data cleansing because it makes use of linguistic, visual, and auditory aspects. Cleaning the original version and comparing your effort to the changes that are accessible would be a terrific exercise, as an upgraded version removes noisy data instances.
Video Game Culture Wars
Utilizing an existing dataset and setting your limitations, practice data cleansing. This spreadsheet was created by compiling tweets over a 72-hour period following the Gamergate issue a few years ago. When sorting through the data, decide on a course of action and begin training your system to recognize irrelevant data on its own.
Clever Weather Patterns
The largest nation in South America, Brazil experiences warm weather and copious amounts of precipitation. Develop your data cleansing skills by going through this sizable dataset of hourly weather data from more than 100 stations and determining what should be kept and removed.
Trending Shows on Streaming Platforms
Watchers have a wide range of options because there are so many streaming platforms available. The most streamed episodes, from recent releases to classic favorites, comprise an ever-evolving dataset that frequently reflects the cultural zeitgeist of the day (recall how Tiger King sparked the plethora of memes relating to the pandemic?). By utilizing this dataset, which covers Netflix’s top 10 series between March 2020 and March 2023, you may examine what content viewers binge-watched during the COVID-19 outbreak.
Data Visualization
LEGO Bricks Data
The original objective of compiling this dataset was to assist individuals in finding new uses for LEGO sets they already owned. The information includes all of the official LEGO sets’ parts, sets, colours, and inventories from the Rebrickable database. Although the information is up to date as of July 2017, you can access more recent data by using the Rebrickable API. With the use of this dataset, you may investigate queries like: Which sets contain the most often used pieces? Which LEGO components are the rarest? In what ways have LEGO sets’ sizes evolved throughout time?
Global Warming Trends
The data science NGO Berkeley Earth provides information on regional variations in land and ocean temperatures in this dataset. This data is a wonderful place to start for data analysis because it has already been cleansed and packaged. You can go here for information that delves deeper into anomalies in the global surface temperature. To visualize data and display temperature variations over time, consider making a line graph.
A Smarter Way to Play Fantasy Football
While you watch your favorite fantasy football team, practice data visualization. The Football Database contains patterns that you can use to choose your starting lineup. To improve everyone’s experience, plot pertinent data points in graphs and present them to the other members of your league. To raise your game each season, consult the graphical representations you made.
The Nutritional Value of Starbucks Drinks
Have you ever wondered how much fat and sugar are added to your favorite coffee beverages? It’s simple to assume that food from Starbucks is healthier than that from McDonald’s due to branding, but you can’t be certain of that without looking into the statistics. The nutrition information for menu items from McDonald’s and Starbucks is included in this Kaggle dataset. You can compare the nutritional values of comparable food and drink items using one or both sets of data, and then visualize your results.
Machine Learning
Jeopardy! Questions
This Kaggle dataset, created by data scientist Bojan Tunguz, has over 200,000 questions from the hit game show Jeopardy! and can be utilized for a variety of applications if you’re ready to take on a challenging machine learning project. For instance, you can estimate the question’s type or monetary amount by using classification algorithms. Alternatively, you can train a BERT model, which is a language model for natural language processing (NLP), to take things to the next level.
Fake Job Posts
Scammers steal people’s identities by creating exceptionally appealing job descriptions and then demanding applicants to supply their Social Security numbers and other information up front, allegedly so they can be considered for an interview. Of the 18,000 job descriptions in this Kaggle dataset that data scientist Shivam Bansal assembled, roughly 800 are fraudulent. The data includes meta-data about the job postings in addition to textual data. Utilizing the data, classification models can be built to identify genuine versus fake job postings.
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Data Analysis
Pokémon
Data from all seven generations of Pokémon has been scraped here, including base stats, height, weight, skills, and more. The dataset can distinguish between legendary Pokémon and the strongest and weakest Pokémon types. You can quickly practice your analytics skills by formulating a few questions that can be answered using the information provided.
Harry Potter
Have you ever wondered which house you would belong to at Hogwarts? Have trouble choosing a favorite character? Utilize these Harry Potter datasets to get a conclusive response. These are our top picks:
- A comprehensive list of every character in every film, together with their demographic data, is provided by this dataset.
- This dataset delves deeply into sentiment analysis and language processing in the films.
- Explore beyond the novels with this data collection, which contains 111,963 fanfiction titles, authors, and summaries about Potter.
Related Articles
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Start Learning Now with EMI OptionsFrequently Asked Questions
How can I come up with original data science project ideas that would differentiate my portfolio?
Although data science activities in the real world include several stages, your portfolio projects don’t have to cover them all. Rather, every job ought to address particular deficiencies and enhance the remainder of your portfolio and resume.
What makes a strong portfolio in data science?
All of your finest work and projects should be displayed in your portfolio in an understandable, user-friendly layout that is easy on the eyes.
How can I make data science fun?
The most enjoyable aspect of data science is data modeling. It lets you quickly compute and forecast using the information you already know about the data. As a component of machine learning, modeling entails determining algorithms based on given data.
Is data science too difficult?
Yes, it might be challenging to get into a data science degree because it requires a strong foundation in math, statistics, and computer programming.
