Python Statistics Essential Training
2h 39mAdvanced2023-08-17
Authors

Matt Harrison
Python and Data Science Corporate Trainer, Author, Speaker, Consultant
Course details
The field of statistics has become increasingly dependent on data analysis and interpretation using Python. With the rise of big data and data science, the demand for professionals who can effectively analyze and interpret data using Python has skyrocketed. In this course, Matt Harrison teaches you how to collect, clean, analyze, and visualize data using the powerful tools of the Python programming language. Join Matt as he gives into the various techniques that form the backbone of statistics and helps you understand the data with summary statistics and visualizations. He explains how to create predictive models using both linear regression andXGBoost, and wraps up the course with a look at hypothesis testing. If you’re interested in exploring statistics using a code-first approach, join Matt in this course as he shows you how to use Python to unlock the power of data.
Skills covered
StatisticsPythonEssential TrainingProgramming LanguagesData ScienceOpen SourceSoftware Development
Concepts
0. Introduction
- 01 - Being a Python statistics MVP
- 02 - What you should know
- 03 - Using GitHub Codespaces with this course
1. Loading and Cleaning Data
- 04 - Loading data
- 05 - Strings and categories
- 06 - Cleaning numbers
- 07 - Shrinking numbers
- 08 - Challenge - Clean Ames
- 09 - Solution - Clean Ames
2. Exploring and Visualizing
- 10 - Categorical exploration
- 11 - Histograms and distributions
- 12 - Outliers and Z-scores
- 13 - Correlations
- 14 - Scatter plots
- 15 - Visualizing categorical and numerical values
- 16 - Comparing two categoricals
- 17 - Challenge - Explore Ames
- 18 - Solution - Explore Ames
3. Linear Regressions
- 19 - Linear regression
- 20 - Interpreting linear regression models
- 21 - Standardizing values
- 22 - Regression with XGBoost
- 23 - Challenge - Predict Ames
- 24 - Solution - Predict Ames
4. Hypothesis Tests
- 25 - Exploring data
- 26 - Visualizing distributions
- 27 - Running statistical tests
- 28 - Testing for normality
- 29 - Challenge - Checking square footage distributions
- 30 - Solution - Checking square footage distributions
Conclusion
- 31 - Next steps