English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 56 lectures (8h 38m) | 1.84 GB

Master applied Statistics with Python by solving real-world problems with state-of-the-art software and libraries

Welcome to Python for Statistical Analysis!

This course is designed to position you for success by diving into the real-world of statistics and data science.

Learn through real-world examples: Instead of sitting through hours of theoretical content and struggling to connect it to real-world problems, we’ll focus entirely upon applied statistics. Taking theory and immediately applying it through Python onto common problems to give you the knowledge and skills you need to excel.

Presentation-focused outcomes: Crunching the numbers is easy, and quickly becoming the domain of computers and not people. The skills people have are interpreting and visualising outcomes and so we focus heavily on this, integrating visual output and graphical exploration in our workflows. Plus, extra bonus content on great ways to spice up visuals for reports, articles and presentations, so that you can stand out from the crowd.

Modern tools and workflows: This isn’t school, where we want to spend hours grinding through problems by hand for reinforcement learning. No, we’ll solve our problems using state-of-the-art techniques and code libraries, utilising features from the very latest software releases to make us as productive and efficient as possible. Don’t reinvent the wheel when the industry has moved to rockets.

What you’ll learn

- Gain deeper insights into data
- Use Python to solve common and complex statistical and Machine Learning-related projects
- How to interpret and visualize outcomes, integrating visual output and graphical exploration
- Learn hypothesis testing and how to efficiently implement tests in Python

## Table of Contents

**Introduction**

1 Introduction

2 Setup

3 BONUS Learning Path

4 Live Install and Verification

5 Coding Editors

6 Live Coding Editor Comparison

7 File Management

**Exploring Data Analysis**

8 Loading Data

9 Loading Data – Practical Example

10 Dataset Preparation – Practical Example

11 Dealing with Outliers – Practical Example

12 D Distribution Overview

13 D Histograms – Practical Example

14 D Bee Swarm – Practical Example

15 D Box and Violin – Practical Example

16 D Empirical CDF and Pandas Describe – Practical Example

17 Higher Dimensional Distributions Overview

18 ND Scatter Matrix – Practical Example

19 ND Correlation – Practical Example

20 D Histograms, Contours and KDE – Practical Example

21 ND Scatter Probability – Practical Example

22 Exploratory Data Analysis Summary

**Characterising**

23 Introduction – Why bother characterising

24 Mean Median Mode – Practical Example

25 Widths – Practical Example

26 Skewness and Kurtosis – Practical Example

27 Percentiles – Practical Example

28 Multivariate Distributions – Practical Example

29 Summary

**Probability**

30 Probability Refresher

31 Introduction to Probability Distributions

32 Probability Distributions – Practical Example

33 Probability Functions and Empirical Distributions

34 Empirical Distributions – Practical Example

35 Introduction to Sampling and the Central Limit Theorem

36 Sampling Distributions – Practical Example

37 Extra Writeup More resources on sampling distributions

38 Central Limit Theorem – Practical Example

39 Summary

**Hypothesis Testing**

40 Introduction to Hypothesis Testing

41 Motivation Loaded Die – Practical Example

42 Basic Tests

43 Basic Tests Example – Asteroid Impacts

44 Introduction to Proportion Testing

45 Proportion Testing Example – Election Rigging

46 Pearsons Chi2 Test – Practical Example

47 Comparing Distributions – Kolmogorow-Smirnow and Anderson-Darling Tests

48 Extra Writeup All the ways to do A B testing

49 Summary

**Conclusion**

50 Conclusion

51 Extra Significance Hunting – What not to do

52 Extra Introduction to Gaussian Proccesses

53 Extra Prac – Cosmic Impact

54 Extra Prac Car Emission Standards

55 Extra Prac Diagnosing Diabetes

56 Extra Prac Numerical Uncertainty on Sales

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