**Statistics Masterclass for Data Science and Data Analytics**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5 Hours | 5.47 GB

Build a Solid Foundation of Statistics for Data Science, Learn Probability, Distributions, Hypothesis Testing, and More!

Starting a career in Data Science or Business Analysis?

then this course will help you to Built a Strong Foundation of statistics for Data Science and Business Analytics

This course is Very Practical, Easy to Understand and Every Concept is Explained with an Example!

I have added real life examples to understand the applications of statistics in the field of Data Science…

We’ll cover everything that you need to know about statistics and probability for Data Science and Business Analytics!

Including:

1) Levels of Measurement

2) Measures of Central Tendency

3) Population and Sample

4) Population Standard Variance

5) Quartiles and IQR

6) Permutations,Combinations

7) Intersection, Union and Complement

8) Independent and Dependent Events

9) Conditional Probability

10) Bayes’ Theorem

11) Uniform Distribution, Binomial Distribution

12) Poisson Distribution, Normal Distribution, Skewness

13) Standardization and Z Score

14) Central Limit Theorem

15) Hypothesis Testing, Type I and Type II Error

16) Students T-Distribution

17) ANOVA – Analysis of Variance

18) F Distribution

19) Linear Regression and much more…

What you’ll learn

- Understand the Fundamentals of Statistics
- Understand the Probability for Data Analysis
- Learn how to work with Different Types of Data
- Different Types of Distributions
- Apply Statistical Methods and Hypothesis Testing to Business Problems
- Understand all the concepts of Statistics for Data Science and Analytics
- Working of Regression Analysis
- Implement one way and two way ANOVA
- Chi-Square Analysis
- Central Limit Theorem

**Table of Contents**

**Welcome to the Course !**

1 Introduction

2 Updates on Udemy Reviews

3 Course FAQs

**Statistics Basic**

4 Data

5 Levels of Measurement

6 Measures of Central Tendency

7 Population and Sample

8 Measures of Dispersion

9 Quartiles and IQR

**Probability**

10 Introduction to Probability

11 Permutations

12 Combinations

13 Intersection, Union and Complement

14 Independent and Dependent Events

15 Conditional Probability

16 Addition and Multiplication Rules

17 Bayes’ Theorem

**Distributions**

18 Introduction to Distribution

19 Uniform Distribution

20 Binomial Distribution

21 Poisson Distribution

22 Normal Distribution

23 Skewness

24 Standardization and Z Score

**Central Limit Theorem**

25 Central Limit Theorem

**Hypothesis Testing**

26 Hypothesis Testing and Hypothesis Formulation

27 Null and Alternative Hypothesis

28 Important Concepts in Hypothesis Testing

29 Exercise 1

30 Exercise 2

31 Type I and Type II Error

32 Students T-Distribution

33 Exercises on Students T-Distribution

**ANOVA – Analysis of Variance**

34 ANOVA – Analysis of Variance

35 F Distribution

36 One-Way ANOVA

37 Two-Way ANOVA

38 Two-Way ANOVA Exercise

39 Two-Way ANOVA with Replication

**Regression Analysis**

40 Linear Regression

41 Exercise on Linear Regression

42 Multiple Regression

**Chi-Square Analysis**

43 Chi-Square Test

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