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Data Science Resources

Explore how you can take your interest in data science further by using these data science resources.
© Coventry University. CC BY-NC 4.0

There is a wealth of reliable information on the topic of data science.

Although we do not necessarily endorse the resources listed below, we have listed some of our favourites including:

Towards data science

This is a great resource for discovering what other data scientists are working on and discussing. The site covers topics including data science, machine learning, programming, and artificial intelligence. You will often find daily articles on data science approaches complete with data and practical code demonstrations often in Jupyter Notebook form.

Kaggle

Kaggle is an online community of data scientists and programmers who publish datasets, Jupyter Notebooks, and competitions to solve challenges in data science. This is a great resource if you want to experiment with real-world data. Many datasets come with complete Jupyter Notebooks that you can run and experiment with.

DataWorld Digest

DataWorld is a platform where people share data and work together to solve problems. When you sign up to the mailing list you will receive an interesting or thought-provoking article and accompanying dataset every Friday, just in time for a weekend of the data science.

Hacker Earth

If you are feeling confident about your newly acquired skills, you might consider entering a coding competition. This site lists all current competitions taking place along with a brief of the challenge. If you do not feel quite ready for this, you will find a practice area where you can revise your skills and also take a mock coding test.

Information is Beautiful

This is a good source if you are looking for inspiration for visualising your next dataset in an aesthetically pleasing way.

Seth Stephens-Davidowitz. (2018). Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are. William Morrow & Co.

This book does what Freakonomics did for economics, but is tailored towards data science. The book provides high-level information about how to approach answering different kinds of questions using data, for instance, whether violent entertainment increases the rate of violent crime, or whether we can game the stock market.

© Coventry University. CC BY-NC 4.0
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