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.

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

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

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

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

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

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

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

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

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

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

Elevate your data storytelling skills and discover how to tell great stories that drive change with your audience.

Enhance your reports with Power BI's Exploratory Data Analysis (EDA). Learn what EDA is for Power BI and how it can help you extract insights from your data.

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

Learn how to explore, visualize, and extract insights from data.

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

Master sampling to get more accurate statistics with less data.

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

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

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

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

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

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

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

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

Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.

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.