Applied Statistics for Data Science: A Hands-On Approach!

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Timo Kerremans

10:08:03

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1 - Course Introduction

1 - All-Jupyter-Notebooks.zip

1 - Course Introduction.mp4

05:23

2 - Chapter 1 Why Data science

2 - All-Jupyter-Notebooks.zip

2 - Introduction.mp4

11:05

2 - Statistics-for-Data-science-Course-Notes-PDF.pdf

2 - Statistics-for-Data-science-Slides-PDF.pdf

3 - Chapter 2 The Histogram

3 - HistogramBuilding.mp4

01:03

3 - How to build a histogram.mp4

08:08

4 - AdjustSigma.mp4

00:07

4 - ChangeAverage.mp4

00:10

4 - HistogramBins.mp4

00:20

4 - Introducing the Probability Density Function.mp4

08:41

4 - Chapter 3 Generating Artificial Data

5 - Different types of Data.mp4

09:49

5 - stats-for-datascience-chapter-3.zip

6 - How to Generate Artificial Data.mp4

12:28

7 - Sample Versus Population.mp4

17:00

8 - Lets Compute some Basic Statistics.mp4

13:57

9 - Visualisation of Sample Statistics.mp4

12:11

10 - Simulate Sample Statistics Fluctuations.mp4

08:11

5 - Chapter 4 The Central Limit Theorem

11 - Simulating the Central Limit Theorem.mp4

21:33

11 - stats-for-datascience-chapter-4.zip

12 - The Strength and Weakness of the Central Limit Theorem.mp4

15:35

6 - Chapter 5 Distribution Functions

13 - Data Distributions Introduction.mp4

14:12

13 - stats-for-datascience-chapter-5.zip

14 - Percentiles and Data Intervals.mp4

22:44

15 - What is the Standard Deviation really.mp4

17:37

16 - The Cumulative Distribution Function.mp4

16:45

17 - Distribution Zoo 1 Normal Distribution.mp4

05:55

18 - Distribution Zoo 2 Uniform Distribution.mp4

04:54

19 - Distribution Zoo 3 Exponential Distribution.mp4

03:04

20 - Distribution Function 4 Poisson Distribution.mp4

03:03

21 - Distribution Zoo 5 Binomial Distribution.mp4

04:10

22 - Distribution Zoo 6 Rayleigh Distribution.mp4

03:03

7 - Intermediate Break

23 - Youre doing great.mp4

00:22

8 - Chapter 6 Statistical Testing

24 - Introduction to Statistical Testing.mp4

15:47

24 - stats-for-datascience-chapter-6.zip

25 - The Pvalue and Statistical Significance.mp4

19:31

26 - Implementing the Pvalue in Python.mp4

21:40

27 - Testing the Pvalue through simulation.mp4

20:19

28 - Statistical Test 1 Normalcy.mp4

18:06

29 - Statistical Test 2 Equal Variances.mp4

15:25

30 - Statistical Test 3 Equal Means.mp4

19:26

31 - Statistical Test 4 ANOVA Test.mp4

16:27

32 - Statistical Test 5 Testing Equal Distributions.mp4

17:15

33 - Nonparametric Statistical Tests.mp4

12:30

9 - Chapter 7 Two Concrete RealLife Examples

34 - Example 1 Detecting A Biased Coin.mp4

24:40

34 - stats-for-datascience-chapter-7.zip

35 - Implementing Coin Flipping in Python.mp4

20:29

36 - Playing Around with the Simulation.mp4

08:02

37 - Example 2 AB testing.mp4

17:28

10 - Chapter 8 Correlation between Variables

38 - Introduction to Correlation.mp4

07:32

38 - stats-for-datascience-chapter-8.zip

39 - Linear Correlation.mp4

12:58

40 - Linear Correlation in Python.mp4

07:18

41 - Pearson Correlation Coefficient.mp4

18:05

42 - Correlation between Categorical Variables.mp4

17:38

43 - Categorical Correlation ChiSquared test.mp4

12:26

11 - EXTRA Regression and Machine Learning Introduction

44 - Linear Regression.mp4

20:11

44 - stats-for-datascience-chapter-9.zip

45 - Logistic Regression MLpipeline.mp4

23:20

Description

Build An Intuitive Understanding Using Python code: Histograms, CLT, Testing, Distributions, Correlation and much more!

What You'll Learn?

Perform elaborate and involved Data Analysis on any dataset.
Build an intuitive understanding of concept in Statistics: Sample, Population, Correlation, P-value, Significance, and others.
Be able to write Python code that generates elaborate and beautiful Visuals.
Make Simulations using Python code that showcase various Statistical Concepts.
Be able to perform various Statistical Tests using Python (Student T-test, Welsh's Test, Levene's Test, Shapiro-Wilk test, ...)
Be able to build a Machine Learning model to predict outcomes based on linear and logistic regression.

Who is this for?

Students on a Data Science track or other technical field.

Professionals that want to pivot towards a data science career.

Active Data Scientists that want to add statistical knowledge and intuition to their tool belt.

Managerial Roles in technical fields that want to up their skill to make better decisions about data.

What You Need to Know?

Statistics Prerequisite: virtually none, you will learn everything you need to know.

Coding Prerequisites: the very basics of Python code, all code will be explained.

More details

Description
Welcome to the course on Statistics For Data Scientists!

Learn about the key concepts in statistics, and how to apply them to your data analysis.
A highly practical and hands-on approach.
A focus on building an intuitive understanding of each topic.
Learn to use Python code to simulate various scenarios in a plug-and-play manner.

What is included in the course:
Detailed Course Notes (100 page textbook with 50+ illustrative figures)
Deck of 360 slides
Lectures with 10h+ content spread over 40+ videos
All of the code in Jupyter Notebooks (7 notebooks, 2000+ lines of code)
Bonus Chapter:Â Introduction to Machine Learning

Topics that the course covers:
The Histogram
Generating artificial Data sets
The central tenet of Statistics
The Central Limit Theorem
Distribution functions
Percentiles
Data Ranges
Cumulative Distribution Function
Different Distribution types:
Normal Distribution
Uniform Distribution
Exponential Distribution
Poisson Distribution
Bernoulli Distribution
Rayleigh Distribution
Statistical Testing
Reasoning behind statistical testing
P-value
Statistical Significance
Different Statistical Tests:
Shapiro-Wilk test
Levene's test
Student T-test/ Welsh T-test
ANOVAÂ test
Kolmogorov Smirnov test
Non-parametric tests
Two real-life examples
Detect a biased coin with 95% certainty
Real-life A/B testing
Correlation
Linear correlation - Pearson correlation coefficient +Â alternatives
Categorical correlation - Chi-Squared test +Â contingency tables
EXTRA:Â Regression and intro to Machine Learning
Linear Regression
Logistic Regression +Â ML pipeline

Who is this course for:
Students on a data science track, or any other technical field.
Professionals that want to pivot into a data science career.
Managers that want to be able to make data driven decisions.
Practicing Data Scientists that want to add this value skill to their tool belt.
Who this course is for:
Students on a Data Science track or other technical field.
Professionals that want to pivot towards a data science career.
Active Data Scientists that want to add statistical knowledge and intuition to their tool belt.
Managerial Roles in technical fields that want to up their skill to make better decisions about data.

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My name is Timo. I have a background in theoretical physics (Master's degree and PhD studies), but I currently work as a senior Data Scientist at a multi-billion dollar company. I have a passion for education, I regard it as one of the pillars of society. Over the years I have built several education projects: I run two YouTube channels; one about physics and mathematics exercises, and one about understanding complex ideas about the universe in understandable terms.I am the founder and maintainer of the "ai-tutor", a web application where I connect a fine-tuned Large Language Model with students that require help with their science and mathematics studies.The latest endeavour is producing the course on "Statistics for Data Science", where I teach key concepts in statistics with a focus on intuition and hands-on code.Thanks for considering my courses,Happy learning,Timo

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