Data Science Resources

These resources are recommended by the Data Science community. While we haven’t personally vetted all of them,  we hope you find them a helpful guide for additional help on your Data Science journey.  This resource page is heavily inspired by Dataquest.io, they are our favorite Data Science learning platform.

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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 — A data science blog has helpful tips, tutorials, other resources on the fields of data science, data analytics, and data engineering.
  • Data.world blog — Data.world 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.
  • R Bloggers — An aggregator/syndicator of great R content from all over the web.
  • R Weekly — A weekly curated list of great R-related content

Practice & Competitions

  • Hacker Rank — Practice your coding skills to prepare for technical interviews.
  • HackerEarth — Participate in programming challenges, and improve your programming skills. We’re currently running a HackerEarth challenge you can sign up for right here.
  • Kaggle — Participate in data science challenges to hone your data science skills and constantly improve them.

Data Science 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 find the best positions for your skillset.
  • Datacareers.io —Shameless plug!  We are the only platform dedicated to Data Science careers.
  • G2Crowd— Crowd reviews of many data science learning platforms as well as data science software solutions. They also have a learning hub that contains some good career advice.

Data Science Meetups

Data Science Events

  • 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.

Data Science Bootcamps

  • Brainstation.io — BrainStation’s Online Data Science Bootcamp is a full-time immersive, project-based, online learning experience, designed to transform your skill set and get a job as a Data Scientist.

Books to Learn Data Science

  • Best Free Books for Learning Data Science — A blog post written by us that aggregates the best free books to learn data science.
  • The Book of RThe Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis.
  • Deep Learning — This textbook is designed to help machine learning practitioners get familiar with deep learning.
  • Python Crash Course, 2nd Edition — Second edition of the best-selling Python book in the world. A fast-paced, no-nonsense guide to programming in Python.
  • Natural Language Processing in Python — This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.
  • An Introduction to Statistical Learning: with Applications in R — An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.
  • 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.
  • Learning Python — Get a comprehensive, in-depth introduction to the core Python language with this hands-on book.

Data Newsletters

  • 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 data.world 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.

Tools for Data Analysis

  • 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 documentations 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 consistent 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.
  • Datacareers Forum — A growing community of individuals interested in data science careers.
  • Quora — Quora is a question-and-answer website where questions are asked, answered, and edited by Internet users in the form of opinions.
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