12. Module Summary

Self Paced Videos

Module 1 - Introduction to Data Science
Module 2 - Introduction to Python
Module 3 - Pandas
Module 4 - Merging Data Frames with Pandas

Module 5 - Introduction to Data Visualisation Using Matplotlib
https://t.me/c/1454674396/8
Part- 1,2,3,4

Module 6 - Data Visualization with Seaborn
https://t.me/c/1454674396/61
Part - 1,2

Module 7 - Descriptive Statistics with Python
https://t.me/c/1454674396/106

Module 8 - Inferential Statistics with Python
https://t.me/c/1454674396/129

Module 9 - Feature Engineering
https://t.me/c/1454674396/155
Part- 1,2,3,4

Module 9.5 - Exploratory Data Analysis
Module 10 - Machine Learning for Everyone
Module 11 - Supervised Learning with scikit-learn
Module 12 - Machine Learning with Tree-Based Models in Python
Module 12.5 - Extreme Gradient Boosting with XGBoost
Module 13 - Dimensionality Reduction and Unsupervised Learning
Module 14 - Machine Learning for Time Series Data

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👇Inferential Statistics with Python👇

1. Module Overview

2.Prerequisites and Course Outline

3. Descriptive Statistics to Summarize Data

4.Hypothesis Testing

5. Hypothesis Testing, Lady tasting tea

6.Hypothesis Testing, The Power, Alpha and p-value of a Statistical Test

7. Hypothesis Testing, t-test and z-test

8. Hypothesis Testing, One Sample Location t-test

9. Hypothesis Testing, z-test

10. Hypothesis Testing, Other types of t-test

11. Hypothesis Testing, Demo Preparing Data for Hypothesis Testing

12. Hypothesis Testing, Demo Performing the t-test

13. Hypothesis Testing, Demo Performing the Paired Difference t-test