Data Science Resources

These resources were recommended by our community. While we haven't personally vetted all of them,  we hope you find them a helpful guide for additional help on your Dataquest learning journey.

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Know a great resource that should be included? Let us know by sending an email to [email protected] with the subject line: Resource Suggestion

Data Science Blogs

  • Towards Data Science — Towards Data Science is a Medium content aggregator of written content related to data science and machine learning, including tutorials, news, and career tips.
  • Dataquest Blog — Our data science blog has helpful tips, tutorials, other resources on the fields of data science, data analytics, and data engineering.
  • blog — a great source for user-supplied data sets, but the site also has a useful blog with interviews with industry figures, tips, and other great content.
  • FiveThirtyEight — FiveThirtyEight uses statistical analysis — hard numbers — to tell compelling stories about elections, politics, sports, science, economics, and lifestyle. If you want to see what really compelling data storytelling looks like, this is a great source.
  • Priceonomics — Priceonomics uses business data to tell stories, which is a skill any professional data scientist or data analyst needs. This blog provides a great source of inspiration.
  • Information is Beautiful — Information is Beautiful is a blog dedicated to posting incredibly well-designed data visualizations in the form of charts and infographics. If you aspire to improve your data visualization skills, their work is absolutely worth studying.

Free Courses

Paid Courses

Career Resources

  • Data Science Career Guide — An exhaustive seven-part guide to navigating the data science job hunt, from how to start your search all the way through how to negotiate a great salary.
  • The Muse — The Must publishes career advice articles on topics from designing your resume and cover letter to finding the best positions for your skill set.
  • Glassdoor — Glassdoor allows you to look up how past and current employees view a company, look up salary data for a company, and see potential interview questions.
  • Indeed — Indeed is a giant job board, but they also have searchable data resources on career-related topics like salaries. It's a great place to look up, for example, what the average data scientist makes in your city.



  • PyCon — PyCon hosts several Python conferences each year in multiple countries.
  • PyData — PyData is an educational program of NumFOCUS, a nonprofit charity promoting the use of accessible and reproducible computing in science and technology.


  • Belgrave Valley (London, UK) — Belgrave Valley is a 2-month data analysis bootcamp designed to give you the skills and experience needed to get a job in data analysis.


  • Best Free Books for Learning Data Science — A blog post written by us that aggregates the best free books to learn data science.
  • Deep Learning — This textbook is designed to help machine learning practitioners get familiar with deep learning.
  • Natural Language Processing in Python — If you have an interest in NLP, this book is highly recommended for you to check out. This book is written to give you an overview of the NLTK library.
  • Intro to Statistics — This book is designed to give you a traditional introduction to statistics at the college level.
  • Hands On Machine Learning — Interested in Machine Learning and with a more “hands-on” approach? This book is a great introduction if you are new to machine learning or just want a refresher.
  • Think Stats — A book that will walk you through how to think about statistics as you program in Python or R.


  • The 7 Best Data Science Newsletters — Our recommendations for the seven newsletters you'll definitely want to subscribe to if you're interested in data science.
  • Data Digest — Every Friday, you’ll get a thought-provoking newsletter containing the top finds, data goodies, and whatever else the team can find.
  • FiveThirtyEight — Every week, you’ll get FiveThirthyEight’s top stories for the week. It's a great source of data storytelling inspiration.


  • Anaconda — Anaconda is the most popular data science platform and the foundation for modern machine learning.
  • RStudio — RStudio is an IDE for the R programming language. It’s an open-source application where users can create R Notebooks to share visualizations, stories, ideas, and code.
  • Jupyter Notebook/Lab — Jupyter Notebook/Lab is an open-source web application that allows users to share visualizations, stories, ideas, and code.
  • VSCode — VSCode is a customizable and versatile IDE that works with any language you can think of.
  • Windows Subsystem for Linux — Windows Subsystem for Linux is designed to run on Windows so you can run Linux distributions and have access to a UNIX terminal.
  • Git Bash — Git Bash is designed to run on Windows so you can use Git commands while on a windows PC.

Helpful Libraries/Documentation

  • Dev Docs — DevDocs combines multiple API documentation in a fast, organized and searchable interface. 
  • TensorFlow Documentation — Documentation for the TensorFlow library, one of the many open-source deep learning libraries available.
  • Keras Documentation — Documentation for the Keras library, one of the many open-source deep learning libraries available.
  • 5 Genius Python Deep Learning Libraries — Blog post by Elite Data Science outlining the top five deep learning libraries. If you’re interested in Deep Learning, we highly suggest you check this out.
  • NLTK — Documentation for the NLTK library, one of Python's open-source natural language processing (NLP) libraries.
  • spaCy — Documentation for the spaCy library, another open-source NLP library for Python.
  • Scikit Learn — Documentation for the Scikit Learn library, one of Python's open-source machine learning libraries.
  • Numpy — Documentation for the NumPy library, one of Python's many open-source data analysis libraries.
  • Pandas — Documentation for the Pandas library, another of Python's open-source data analysis libraries.
  • SciPy — Documentation for the SciPy library, another popular Python data analysis library.
  • SymPy — Documentation for the SymPy library, one of Python's numerical computation libraries.
  • Bokeh — Documentation for the Bokeh library, one of Python's many open-source data visualization libraries.
  • Matplotlib — Documentation for the Matplotlib library, another open-source data visualization library for Python.
  • Plotly — Documentation for the Plotly library, one more Python open-source data visualization library.
  • dplyr — A fast, consistent tool for working with data frame like objects,both in memory and out of memory. 
  • purrr — A complete and consitent functional toolkit for R.
  • readr —  Provides a fast and friendly way to read rectangular data.
  • ggplot2 — Provides a way to create graphs in R.
  • DBI — Database interface for communication between R and a relational database management system.
  • RSQLite — Embeds the SQLite database engine in R.

Support Resources

  • StackOverflow — The Google for programming issues. A question and answer site for professional and enthusiast programmers. It covers a wide range of topics in computer programming.
  • Dataquest Discourse Community — Whether you’re new to the field or looking to take a step up in your career, we can help you connect with other passionate learners around the world.
  • Data Science Slack — 'Data Science Community' is the largest slack community for data practitioners. This is a free resource you can use to chat with data scientists around the world.
  • Quora — Quora is a question-and-answer website where questions are asked, answered, and edited by Internet users in the form of opinions.

Practice & Competitions

  • Hacker Rank — Practice your coding skills to prepare for technical interviews.
  • Hacker Earth — Participate in programming challenges, and improve your programming skills.
  • Kaggle — Participate in data science challenges to hone your data science skills and constantly improve them.