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.

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.
• 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.

Introductory Data Science Courses

• Python for Data Science: Fundamentals — Learn about the fundamentals of Python programming and data science.
• Python for Data Science: Intermediate — Dig deeper into Python programming and improve your data skills.
• Introduction to Programming In R — Learn the basics of R, a popular programming language for data analysis and data science.
• Intermediate R Programming — Enhance your data analysis skills in R.
• Andrew Ng’s Machine Learning — Learn the fundamentals of machine learning from Stanford Professor Ng. This is a great introductory course for machine learning.
• FreeCodeCamp’s Data Visualization Course — FreeCodeCamp is a non-profit learning site that teaches web development skills, but they also offer a great course for data professionals: this class on data visualization using the JavaScript library D3.

Paid Courses

• SQL for Data Analysis and Data Scientists — This guided course will take absolute beginners through the basics of SQL to an ability to write queries with confidence
• SQL Fundamentals for Data Science — Learn the basics of working with SQL databases for Data Science.
• SQL Intermediate for Data Science — Learn to work with multi-table databases and answer business questions using SQL.
• Deep Learning Fundamentals — Learn the basics of deep neural networks. Includes graph representation, activation functions, multiple hidden layers, and image classification.
• Data Science Courses — Browse the rest of our interactive data science course offerings.

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.
• 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 Meetups — Find data science meetups near you and start networking.
• Python-Focused Meetups — Connect with fellow Python developers near you and exchange ideas.

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

• 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.

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.
• 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.

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 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.
  
• Quora — Quora is a question-and-answer website where questions are asked, answered, and edited by Internet users in the form of opinions.