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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