Data science courses

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

Learn to perform linear and logistic regression with multiple explanatory variables.

Master sampling to get more accurate statistics with less data.

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

Build the foundation you need to think statistically and to speak the language of your data.

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Learn the core techniques necessary to extract meaningful insights from time series data.

Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.

In this course you will learn to fit hierarchical models with random effects.

Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

Learn survey design using common design structures followed by visualizing and analyzing survey results.

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

Learn to perform linear and logistic regression with multiple explanatory variables.