English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 10 Hours | 4.70 GB

Learn the statistics & probability for data science and business analysis Are you aiming for a career in Data Science or Data Analytics? Good news, you don’t need a Maths degree – this course is equipping you with the practical knowledge needed to master the necessary statistics. It is very important if you want to become a Data Scientist or a Data Analyst to have a good knowledge in statistics & probability theory. Sure, there is more to Data Science than only statistics. But still it plays an essential role to know these fundamentals ins statistics. I know it is very hard to gain a strong foothold in these concepts just by yourself. Therefore I have created this course. Why should you take this course? This course is the one course you take in statistic that is equipping you with the actual knowledge you need in statistics if you work with data This course is taught by an actual mathematician that is in the same time also working as a data scientist. This course is balancing both: theory & practical real-life example. After completing this course you ll have everything you need to master the fundamentals in statistics & probability need in data science or data analysis. What is in this course? This course is giving you the chance to systematically master the core concepts in statistics & probability, descriptive statistics, hypothesis testing, regression analysis, analysis of variance and some advance regression / machine learning methods such as logistics regressions, polynomial regressions , decision trees and more. In real-life examples you will learn the stats knowledge needed in a data scientist’s or data analyst’s career very quickly. What you’ll learn- Master the fundamentals of statistics for data science & data analytics
- Master descriptive statistics & probability theory
- Machine learning methods like Decision Trees and Decision Forests
- Probability distributions such as Normal distribution, Poisson Distribution and more
- Hypothesis testing, p-value, type I & type II error
- Logistic Regressions, Multiple Linear Regression, Regression Trees
- Correlation, R-Square, RMSE, MAE, coefficient of determination and more

**+ Table of Contents**

**Let’s get started**

1 Welcome!

2 What will you learn in this course

3 How can you get the most out of it

4 Download Formula cheat sheet

**Descriptive statistics**

5 Intro

6 Mean

7 Median

8 Mode

9 Skewness

10 Range & IQR

11 Sample vs. Population

12 Variance & Standard deviation

13 Impact of Scaling & Shifting

14 Statistical moments

**Distributions**

15 What is a distribution

16 Normal distribution

17 Z-Scores

18 More distributions

**Probability theory**

19 Intro

20 Probability Basics

21 Calculating simple Probabilities

22 Rule of addition

23 Rule of multiplication

24 Bayes Theorem

25 Bayes Theorem – Practical example

26 Expected value

27 Law of Large Numbers

28 Central Limit Theorem – Theory

29 Central Limit Theorem – Intuition

30 Central Limit Theorem – Challenge

31 Central Limit Theorem – Exercise

32 Central Limit Theorem – Solution

33 Binomial distribution

34 Poisson distribtuion

35 Real life problems

**Hypothesis testing**

36 Intro

37 What is an hypothesis

38 Significance level and p-value

39 Type I and Type II errors

40 Confidence intervals and margin of error

41 Excursion Calculating sample size & power

42 Performing the hypothesis test

43 t-test and t-distribution

44 Proportion testing

45 Important p-z pairs

**Regressions**

46 Intro

47 Linear Regression

48 Correlation coefficient

49 Residual, MSE & MAE

50 Coefficient of determination

51 Root Mean Square Error

**Advanced regeression & machine learning alogorithms**

52 Multiple Linear Regression

53 Overfitting

54 Polynomial Regression

55 Logistic Regression

56 Decision Trees

57 Regression Trees

58 Random Forests

59 Dealing with missing data

**ANOVA (Analysis of Variance)**

60 ANOVA Basics & Assumptions

61 One-way ANOVA

62 F-Distribution

63 Two-way ANOVA – Sum of Squares

64 Two-way ANOVA – F-ratio & conclusions

**Wrap up**

65 Wrap up

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