Free Mathematics and Statistics Course

Mathematics and Statistics for Data Science

• Lesson 01: Course Introduction

  06:48

  • 1.01 Course Introduction

    06:48

• Lesson 02: Introduction to Scalars, Matrices and Tensors

  18:45

  • 2.01 Introduction to Linear Algebra and Scalars

    01:16

  • 2.02 Vectors

    05:31

  • 2.03 Matrix

    07:06

  • 2.04 Inverse of a matrix

    02:29

  • 2.05 Tensors

    02:23

  • 2.06 Introduction to Scalars, Matrices and Tensors I

  • 2.07 Introduction to Scalars, Matrices and Tensors II

• Lesson 03: Introduction to Statistics

  44:18

  • 3.01 Introduction to Statistics

    04:40

  • 3.02 Why Statistics

    04:38

  • 3.03 Types of Statistics Descriptive Statistics

    02:51

  • 3.04 Inferential Statistics

    04:43

  • 3.05 Predictive Statistics

    02:42

  • 3.06 Importance of Statistical Concepts in Data Science Part 1

    03:07

  • 3.07 Importance of Statistical Concepts in Data Science Part 2

    07:19

  • 3.08 Importance of Statistical Techniques

    03:14

  • 3.09 Why Statistics Is Vital for Business Success​

    05:28

  • 3.10 Case Studies of Statistics Use in Business

    05:36

  • 3.11 Introduction to Statistics I

  • 3.12 Introduction to Statistics II

• Lesson 04: Understanding the Data

  38:32

  • 4.01 Types of Data in Business Contexts

    04:52

  • 4.02 Data Classification

    02:23

  • 4.03 Data Collection and Its States

    04:02

  • 4.04 Types of Data

    04:51

  • 4.05 Structured vs Unstructured Data

    02:27

  • 4.06 Key Sources of Data

    07:07

  • 4.07 Data Quality Issues

    06:10

  • 4.08 Demo Understanding Data Part 1

    03:26

  • 4.09 Demo Understanding Data Part 2

    03:14

  • 4.10 Understanding the Data I

  • 4.11 Understanding the Data II

• Lesson 05: Descriptive Statistics

  50:18

  • 5.01 Descriptive Statistics Data for Business

    01:15

  • 5.02 Central Tendency

    01:32

  • 5.03 Significance of Central Tendency

    02:47

  • 5.04 Mean

    01:46

  • 5.05 Median

    05:04

  • 5.06 Mode

    00:26

  • 5.07 Measures of Dispersion

    01:02

  • 5.08 Range

    03:46

  • 5.09 Outliers and Distortion of Mean

    03:29

  • 5.10 Mean Absolute Deviation Standard Deviation and Variance

    05:25

  • 5.11 Z Score or Standard Score and Empirical Rule

    03:45

  • 5.12 Coefficient of Variation and Its Application

    03:46

  • 5.13 Measures of Shape

    05:14

  • 5.14 Case Study Descriptive Statistics

    05:05

  • 5.15 Demo Descriptive Statistics

    05:56

  • 5.16 Descriptive Statistics Branching Scenario Central Tendency

  • 5.17 Descriptive Statistics Branching Scenario Dispersion

• Lesson 06: Data Visualization

  39:25

  • 6.01 Introduction to Data Visualization

    02:02

  • 6.02 Basic Charts Bar Graph Pie Charts Histogram Box Plot

    05:32

  • 6.03 Advanced Charts Scatter Plot and Bubble Plot

    03:05

  • 6.04 Interpretation of the Charts

    03:00

  • 6.05 Selecting the Appropriate Chart

    02:53

  • 6.06 Data Preprocessing Dos and Donts

    06:13

  • 6.07 Case Study Deciding the Variables

    05:07

  • 6.08 Case Study Data Visualization

    04:37

  • 6.09 Demo Data Visualization

    06:56

  • 6.10 Histogram

  • 6.11 Scatter Diagram

• Lesson 07: Probability

  42:52

  • 7.01 Probability

    07:32

  • 7.02 Key Terms of Probability

    04:42

  • 7.03 Conditional Probability

    06:41

  • 7.04 Independent and Dependent Events

    01:56

  • 7.05 Addition Theorem of Probability

    05:36

  • 7.06 Multiplication Theorem of Probability

    03:03

  • 7.07 Bayes Theorem

    06:54

  • 7.08 Demo Probabibilty

    06:28

  • 7.09 Probability I

  • 7.10 Probability II

• Lesson 08: Probability Distributions

  37:24

  • 8.01 Random Variable

    04:01

  • 8.02 Probability Distribution

    07:26

  • 8.03 Discrete Probability Distributions

    03:04

  • 8.04 Binomial Distribution

    05:09

  • 8.05 Poisson Distribution and Continuous Probability Distribution

    06:05

  • 8.06 Normal Probability Distribution

    05:37

  • 8.07 Uniform Probability Distribution

    06:02

  • 8.08 Probability Distributions

  • 8.09 Binomial Exponential and Uniform Distributions

• Lesson 09: Sampling and Sampling Techniques

  17:04

  • 9.01 Introduction to Sampling and Sampling Errors

    04:12

  • 9.02 Advantages and Disadvantages of Sampling

    01:27

  • 9.03 Probability Sampling Methods

    01:48

  • 9.04 Non Probability Sampling Methods

    02:07

  • 9.05 Sampling

    01:25

  • 9.06 Sampling Distribution

    01:21

  • 9.07 Central Limit Theorem

    00:55

  • 9.08 Case Study Sample and Sampling Techniques

    01:23

  • 9.09 Demo Sampling and Sampling Techniques

    02:26

  • 9.10 Sampling Bias Random Selection Proportional Representation

  • 9.11 Simple Random Sampling Stratified Sampling and Systematic Sampling

• Lesson 10: Inferential Statistics and its Application

  01:50:59

  • 10.01 What Is Inferential Statistics

    08:16

  • 10.02 Hypothesis

    03:35

  • 10.03 Types of Hypothesis

    04:33

  • 10.04 Components of Hypothesis

    03:59

  • 10.05 Hypothesis Validation

    02:34

  • 10.06 Hypothesis Testing

    04:42

  • 10.07 Statistical Methods of Hypothesis Testing

    01:54

  • 10.08 T Test Critical Value

    03:05

  • 10.09 Two Tailed Test

    03:00

  • 10.10 Knowledge Check Match The Graph

  • 10.11 One Sample T Test

    03:48

  • 10.12 Two Sample Test Independent Samples

    05:51

  • 10.13 Knoweldge Check Formula

  • 10.14 P Value

    03:27

  • 10.15 Z Test

    09:31

  • 10.16 Analysis of Variance ANOVA

    03:27

  • 10.17 One Way ANOVA

    02:35

  • 10.18 Two Way ANOVA

    02:05

  • 10.19 Type I and Type II Errors

    02:08

  • 10.20 Decision and Outcomes of Hypothesis Testing

    01:49

  • 10.21 Probability of Error

    02:48

  • 10.22 Chi Squared Test

    03:40

  • 10.23 Fishers Exact Test

    00:54

  • 10.24 Sign Test

    04:39

  • 10.25 Kruskal Wallis Test

    05:16

  • 10.26 Mann Whitney Wilcoxon Test

    02:33

  • 10.27 Run Test

    06:40

  • 10.28 Regression Analysis

    04:56

  • 10.29 Drag and Drop Group

  • 10.30 Demo Inferential Statistics

    09:14

  • 10.31 Inferential Test

  • 10.32 Type of Hypothesis

• Lesson 11: Relation Between Variables

  18:46

  • 11.01 Correlation

    04:22

  • 11.02 Types of Correlation Coefficients

    01:36

  • 11.03 Karl Pearsons Correlation Coefficient Use Cases

    01:14

  • 11.04 Spearmans Rank Correlation Coefficient

    02:14

  • 11.05 Causation

    02:37

  • 11.06 Regression

    02:04

  • 11.07 Coefficient of Determination

    02:13

  • 11.08 Demo Relation Between Variables

    02:26

  • 11.09 Relation Between Variables I

  • 11.10 Relation Between Variables II

• Lesson 12: Applications of Statistics in Business

  14:31

  • 12.01 How to Use Statistics in Day to Day Business

    11:19

  • 12.02 How Not to Lie with Statistics​

    03:12