Introduction to Statistics in Python
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Follow short videos led by expert instructors and then practice what you’ve learned with interactive exercises in your browser.
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.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels 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.
Learn to perform linear and logistic regression with multiple explanatory variables.
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
Master sampling to get more accurate statistics with less data.
Build the foundation you need to think statistically and to speak the language of your data.
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
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.
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
In this course you will learn to fit hierarchical models with random effects.
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
Learn the core techniques necessary to extract meaningful insights from time series data.
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Learn to design and run your own Monte Carlo simulations using Python!
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Learn to perform linear and logistic regression with multiple explanatory variables.
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
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.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.