You don’t need to learn everything in data science to get a job! Finding your passion and narrowing your target role can make learning more approachable.
There are quite a few great, free, open-source Python libraries for data science. In this post, we’ll cover 15 of the most popular and what they can do.
When you’re just getting started, looking at Python can be intimidating. Why the different colors? What are those line breaks for? Here are the answers you seek.
Learn the fundamentals of conditional probability in R with this interactive statistics course. Master Naive Bayes and learn to build a spam filter with R!
Learn more about how Dataquest teaches data science, and why this approach is optimal for most learners.
Stacey had tried learning data science on other platforms, but it didn’t stick — she wasn’t making progress. Here’s how Dataquest made the difference.
Dataquest is launching a data science scholarship for anyone who’s working on, or plans to work on, a data project for social good!
Dataquest is launching another data science scholarship for women and anyone who identifies as an underrepresented gender in data science!
Learn how to use average and variability measures like mean, median, mode, range, standard deviation, z-scores, and more in this hands-on R statistics course.
Supercharge your study of data science (or anything else you’re trying to learn) with one of the best study habits of all, according to science: teaching.
Should you enroll in an online data science bootcamp or try one of the expensive offline schools? Here’s how your options stack up when it comes to learning data science.
Learn about conditional probability for data science, including Bayes’ Theorem and Naive Bayes algorithms, in this new interactive online course.
Learn the skills you need to become a data engineer with our new interactive data engineering course path, which covers Python, SQL, Postgres, and more!
Learn to analyze and filter survey data, including multi-answer multiple choice questions, using Python in this beginner tutorial for non-coders!
Learning to write better code — code that’s readable, maintainable, and debuggable — is a crucial skill for being an effective part of a data science team.