Statistical knowledge is key to evaluating, interpreting, and reporting findings from your data. In this skill track, you'll learn the four fundamentals of statistics using Python, including:
✓ Summary statistics and probability
✓ Statistical models such as linear and logistic regression
✓ Techniques for sampling
✓ How to perform hypothesis tests and draw conclusions from a wide variety of data sets
By the end of this track, you'll be ready to apply your statistical skills in Python to analyze data, implement and evaluate statistical models, and draw conclusions from hypothesis test results!
Overview
Syllabus
- Introduction to Statistics in Python
- Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
- Introduction to Regression with statsmodels in Python
- Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
- Intermediate Regression with statsmodels in Python
- Learn to perform linear and logistic regression with multiple explanatory variables.
- Sampling 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.
- Hypothesis Testing in Python
- Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
- Hypothesis Testing with Men's and Women's Soccer Matches
Taught by
Maggie Matsui, Maarten Van den Broeck, and James Chapman
