Home|

Category: Dataquest Updates

Learn to Optimize Algorithms in Our New Algorithm Complexity Course

Being a data engineer means helping others work with data efficiently, and that means being able to assess and implement algorithms specific to your use case.

Read More

New Statistics Course: Conditional Probability in R

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!

Read More

Dataquest’s Active Curriculum: How We Teach Data Science

Learn more about how Dataquest teaches data science, and why this approach is optimal for most learners.

Read More

New Course: Learn Linear Modeling in R

Learn the fundamentals of modeling with R and build a foundation for machine learning as you work through building, fitting, and assessing linear models.

Read More

New Course: Statistics Intermediate in R: Averages and Variability

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.

Read More

New Statistics Course: Hypothesis Testing in R

In this interactive course, learn hypothesis testing in R and build the statistics skills you’ll need to test the statistical significance of your analysis.

Read More

New Python Statistics Course: Conditional Probability

Learn about conditional probability for data science, including Bayes’ Theorem and Naive Bayes algorithms, in this new interactive online course.

Read More

A Year Learning Data Science at Dataquest

What can you learn in a year of studying data science at Dataquest? Python, SQL, statistics, and an awful lot more.

Read More

Go From Total Beginner to Data Engineer with Our New Path

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!

Read More

Write Better Code With Our New Advanced Functions Python Course

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.

Read More