/statistic-essential-for-data-science

Advanced Statistics

• Lesson 01: Course Introduction

  07:05Preview

  • 1.01 Course Introduction

    05:19

  • 1.02 What Will You Learn

    01:46

• Lesson 02: Introduction to Statistics

  25:49Preview

  • 2.01 Learning Objectives

    01:16

  • 2.02 What Is Statistics

    01:50

  • 2.03 Why Statistics

    02:06

  • 2.04 Difference between Population and Sample

    01:20

  • 2.05 Different Types of Statistics

    02:42

  • 2.06 Importance of Statistical Concepts in Data Science

    03:20

  • 2.07 Application of Statistical Concepts in Business

    02:11

  • 2.08 Case Studies of Statistics Usage in Business

    03:09

  • 2.09 Applications of Statistics in Business: Time Series Forecasting

    03:50

  • 2.10 Applications of Statistics in Business Sales Forecasting

    03:19

  • 2.11 Recap

    00:46

• Lesson 03: Understanding the Data

  17:29Preview

  • 3.01 Learning Objectives

    01:12

  • 3.02 Types of Data in Business Contexts

    02:11

  • 3.03 Data Categorization and Types of Data

    03:13

  • 3.03 Types of Data Collection

    02:14

  • 3.04 Types of Data

    02:01

  • 3.05 Structured vs. Unstructured Data

    01:46

  • 3.06 Sources of Data

    02:17

  • 3.07 Data Quality Issues

    01:38

  • 3.08 Recap

    00:57

• Lesson 04: Descriptive Statistics

  34:51Preview

  • 4.01 Learning Objectives

    01:26

  • 4.02 Descriptive Statistics

    02:03

  • 4.03 Mathematical and Positional Averages

    03:15

  • 4.04 Measures of Central Tendancy: Part A

    02:17

  • 4.05 Measures of Central Tendancy: Part B

    02:41

  • 4.06 Measures of Dispersion

    01:15

  • 4.07 Range Outliers Quartiles Deviation

    02:30

  • 4.08 Mean Absolute Deviation (MAD) Standard Deviation Variance

    03:37

  • 4.09 Z Score and Empirical Rule

    02:14

  • 4.10 Coefficient of Variation and Its Application

    02:06

  • 4.11 Measures of Shape

    02:39

  • 4.12 Summarizing Data

    02:03

  • 4.13 Recap

    00:54

  • 4.14 Case Study One: Descriptive Statistics

    05:51

• Lesson 05: Data Visualization

  23:36Preview

  • 5.01 Learning Objectives

    00:57

  • 5.02 Data Visualization

    02:15

  • 5.03 Basic Charts

    01:52

  • 5.04 Advanced Charts

    02:19

  • 5.05 Interpretation of the Charts

    02:57

  • 5.06 Selecting the Appropriate Chart

    02:25

  • 5.07 Charts Do's and Dont's

    02:47

  • 5.08 Story Telling With Charts

    01:29

  • 5.09 Data Visualization: Example

    02:41

  • 5.10 Recap

    00:50

  • 5.11 Case Study Two: Data Visualization

    03:04

• Lesson 06: Probability

  21:51Preview

  • 6.01 Learning Objectives

    00:55

  • 6.02 Introduction to Probability

    03:10

  • 6.03 Probability Example

    02:02

  • 6.04 Key Terms in Probability

    02:25

  • 6.05 Conditional Probability

    02:11

  • 6.06 Types of Events: Independent and Dependent

    02:59

  • 6.07 Addition Theorem of Probability

    01:58

  • 6.08 Multiplication Theorem of Probability

    02:08

  • 6.09 Bayes Theorem

    03:10

  • 6.10 Recap

    00:53

• Lesson 07: Probability Distributions

  24:45Preview

  • 7.01 Learning Objectives

    00:52

  • 7.02 Probability Distribution

    01:25

  • 7.03 Random Variable

    02:21

  • 7.04 Probability Distributions Discrete vs.Continuous: Part A

    01:44

  • 7.05 Probability Distributions Discrete vs.Continuous: Part B

    01:45

  • 7.06 Commonly Used Discrete Probability Distributions: Part A

    03:18

  • 7.07 Discrete Probability Distributions: Poisson

    03:16

  • 7.08 Binomial by Poisson Theorem

    02:28

  • 7.09 Commonly Used Continuous Probability Distribution

    03:22

  • 7.10 Application of Normal Distribution

    02:49

  • 7.11 Recap

    01:25

• Lesson 08: Sampling and Sampling Techniques

  36:45Preview

  • 8.01 Learnning Objectives

    00:51

  • 8.02 Introduction to Sampling and Sampling Errors

    03:05

  • 8.03 Advantages and Disadvantages of Sampling

    01:31

  • 8.04 Probability Sampling Methods: Part A

    02:32

  • 8.05 Probability Sampling Methods: Part B

    02:27

  • 8.06 Non-Probability Sampling Methods: Part A

    01:42

  • 8.07 Non-Probability Sampling Methods: Part B

    01:25

  • 8.08 Uses of Probability Sampling and Non-Probability Sampling

    02:08

  • 8.09 Sampling

    01:08

  • 8.10 Probability Distribution

    02:53

  • 8.11 Theorem Five Point One

    00:52

  • 8.12 Center Limit Theorem

    02:14

  • 8.13 Sampling Stratified: Sampling Example

    04:35

  • 8.14 Probability Sampling: Example

    01:17

  • 8.15 Recap

    01:07

  • 8.16 Case Study Three: Sample and Sampling Techniques

    05:16

  • 8.17 Spotlight

    01:42

• Lesson 09: Inferential Statistics

  37:08Preview

  • 9.01 Learning Objectives

    01:04

  • 9.02 Inferential Statistics

    03:09

  • 9.03 Hypothesis and Hypothesis Testing in Businesses

    03:24

  • 9.04 Null and Alternate Hypothesis

    01:44

  • 9.05 P Value

    03:22

  • 9.06 Levels of Significance

    01:16

  • 9.07 Type One and Two Errors

    01:37

  • 9.08 Z Test

    02:24

  • 9.09 Confidence Intervals and Percentage Significance Level: Part A

    02:52

  • 9.10 Confidence Intervals: Part B

    01:20

  • 9.11 One Tail and Two Tail Tests

    04:43

  • 9.12 Notes to Remember for Null Hypothesis

    01:02

  • 9.13 Alternate Hypothesis

    01:51

  • 9.14 Recap

    00:56

  • 9.15 Case Study 4: Inferential Statistics

    06:24

  • Hypothesis Testing

• Lesson 10: Application of Inferential Statistics

  27:20Preview

  • 10.01 Learning Objectives

    00:50

  • 10.02 Bivariate Analysis

    02:01

  • 10.03 Selecting the Appropriate Test for EDA

    02:29

  • 10.04 Parametric vs. Non-Parametric Tests

    01:54

  • 10.05 Test of Significance

    01:38

  • 10.06 Z Test

    04:27

  • 10.07 T Test

    00:54

  • 10.08 Parametric Tests ANOVA

    03:26

  • 10.09 Chi-Square Test

    02:31

  • 10.10 Sign Test

    01:58

  • 10.11 Kruskal Wallis Test

    01:04

  • 10.12 Mann Whitney Wilcoxon Test

    01:18

  • 10.13 Run Test for Randomness

    01:53

  • 10.14 Recap

    00:57

• Lesson 11: Relation between Variables

  20:07Preview

  • 11.01 Learning Objectives

    01:06

  • 11.02 Correlation

    01:54

  • 11.03 Karl Pearson's Coefficient of Correlation

    02:36

  • 11.04 Karl Pearsons: Use Cases

    01:30

  • 11.05 Correlation Example

    01:59

  • 11.06 Spearmans Rank Correlation Coefficient

    02:14

  • 11.07 Causation

    01:47

  • 11.08 Example of Regression

    02:28

  • 11.09 Coefficient of Determination

    01:12

  • 11.10 Quantifying Quality

    02:29

  • 11.11 Recap

    00:52

• Lesson 12: Application of Statistics in Business

  17:25Preview

  • 12.01 Learning Objectives

    00:53

  • 12.02 How to Use Statistics In Day to Day Business

    03:29

  • 12.03 Example: How to Not Lie With Statistics

    02:34

  • 12.04 How to Not Lie With Statistics

    01:49

  • 12.05 Lying Through Visualizations

    02:15

  • 12.06 Lying About Relationships

    03:31

  • 12.07 Recap

    01:06

  • 12.08 Spotlight

    01:48

• Lesson 13: Assisted Practice

  11:47

  • Assisted Practice: Problem Statement

    02:10

  • Assisted Practice: Solution

    09:37