Python for Statistical Analysis

Python for Statistical Analysis

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

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

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

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

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