Introduction to Inferential Statistics

Learn real-world inferential statistics with Python including confidence intervals, hypothesis testing, and hands-on projects to build your data skills from the ground up.

What you’ll learn

[]How to use Python for real-world statistical analysis
[]Understand and apply confidence intervals
[]Master hypothesis testing techniques step-by-step
[]Use statistical thinking to make data-driven decisions
[]Explore t-tests, z-scores, ANOVA, and more
[]Spot and avoid common statistical errors
[]Build reusable Python functions for statistical tests
[]Work through hands-on projects using real datasets

The title may say "introduction" in it, but this course goes beyond the basics to help you truly understand inferential statistics. You’ll explore everything from descriptive foundations to hypothesis testing, confidence intervals, and advanced statistical methods like ANOVA and non-parametric tests.

Along the way, you’ll use Python to code your way through each concept – learning how to apply statistics to messy, real-world data. From estimating ratings to running t-tests, you’ll build confidence not just in your coding, but in your ability to draw meaningful conclusions from data.

It all wraps up with hands-on capstone projects that give you the space to apply everything you’ve learned in realistic scenarios. Whether you’re prepping for your first data job or trying to level up, this course gives you the stats skills hiring teams are looking for.

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