Python for Statistical Analysis

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Setup

Lecture 3: Learning Paths

Lecture 4: Live Install and Verification

Lecture 5: Coding Editors

Lecture 6: Live Coding Editor Comparison

Lecture 7: File Management

Chapter 2: Exploring Data Analysis

Lecture 1: Loading Data

Lecture 2: Loading Data – Practical Example

Lecture 3: Dataset Preparation – Practical Example

Lecture 4: Dealing with Outliers – Practical Example

Lecture 5: 1D Distribution Overview

Lecture 6: 1D Histograms – Practical Example

Lecture 7: 1D Bee Swarm – Practical Example

Lecture 8: 1D Box and Violin – Practical Example

Lecture 9: 1D Empirical CDF and Pandas Describe – Practical Example

Lecture 10: Higher Dimensional Distributions Overview

Lecture 11: ND Scatter Matrix – Practical Example

Lecture 12: ND Correlation – Practical Example

Lecture 13: 2D Histograms, Contours and KDE – Practical Example

Lecture 14: ND Scatter Probability – Practical Example

Lecture 15: Exploratory Data Analysis Summary

Chapter 3: Characterising

Lecture 1: Introduction – Why bother characterising?

Lecture 2: Mean Median Mode – Practical Example

Lecture 3: Widths – Practical Example

Lecture 4: Skewness and Kurtosis – Practical Example

Lecture 5: Percentiles – Practical Example

Lecture 6: Multivariate Distributions – Practical Example

Lecture 7: Summary

Chapter 4: Probability

Lecture 1: Probability Refresher

Lecture 2: Introduction to Probability Distributions

Lecture 3: Probability Distributions – Practical Example

Lecture 4: Probability Functions and Empirical Distributions

Lecture 5: Empirical Distributions – Practical Example

Lecture 6: Introduction to Sampling and the Central Limit Theorem

Lecture 7: Sampling Distributions – Practical Example

Lecture 8: Extra Writeup: More resources on sampling distributions

Lecture 9: Central Limit Theorem – Practical Example

Lecture 10: Summary

Chapter 5: Hypothesis Testing

Lecture 1: Introduction to Hypothesis Testing

Lecture 2: Motivation Loaded Die – Practical Example

Lecture 3: Basic Tests

Lecture 4: Basic Tests Example – Asteroid Impacts

Lecture 5: Introduction to Proportion Testing

Lecture 6: Proportion Testing Example – Election Rigging

Lecture 7: Pearsons Chi2 Test – Practical Example

Lecture 8: Comparing Distributions – Kolmogorow-Smirnow and Anderson-Darling Tests

Lecture 9: Extra Writeup: All the ways to do A/B testing!

Lecture 10: Summary

Chapter 6: Conclusion

Lecture 1: Conclusion

Lecture 2: Extra: Significance Hunting – What not to do!

Lecture 3: Extra: Introduction to Gaussian Processes

Lecture 4: Extra Prac – Cosmic Impact

Lecture 5: Extra Prac: Car Emission Standards

Lecture 6: Extra Prac: Diagnosing Diabetes

Lecture 7: Extra Prac: Numerical Uncertainty on Sales

Chapter 7: Congratulations!! Don't forget your Prize 🙂

Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)