4.1 Formulation of Hypothesis

4.2 Selection of Statistical Test

4.3 Level of Significance and Degree of Freedom

4.4 Computing the Calculated Values

4.5 Computing the Table Values

4.6 Comparing Calculated and Table Values

4.7 Hypothesis Conclusion

4.8 Learn to perform T-test to measure the variance between the means of two samples or population

4.9 Learn to perform Z-test to measure the variance between the means of two samples or population

4.10 Learn to perform Chi Square-test to measure the variance between the means of two samples or population

4.11 Wilcoxson Sign Test and Friedman Test

4.12 MannWhitney Test and Krushkal Wallis Test

4.13 One Sample T-Test

4.14 2-Sample Paired T test

4.15 2-Sample Independent T test

4.16 Introduction to ANOVA

4.17 What is One Way ANOVA?

4.18 What is Two way ANOVA?

4.19 What is Multi way ANOVA?

4.20 What is ANCOVA?

4.21 Difference between ANOVA and ANCOVA?

4.22 Introduction to probability

4.23 Types of events

4.24 Marginal Probability

4.25 Baye’s Theorem

4.26 Introduction to Probability Distribution

4.27 Binomial Probability

4.28 Possion Probability Distribution

4.29 Hypergeometric Probability Distribution

4.30 Uniform Probability Distribution

4.31 Normal Probability Distribution

4.32 Exponential Probability Distribution

5.1 Linear Predictive Analysis

5.2 Implemention of Predictive Analysis Using Python

5.3 What is Multiple Predictive Model?

5.4 Building the Multiple Predictive Model using Python

5.5 Assumption of Multiple Predictive Model

5.6 AutoCorrelation,MultiColliniearity and Hetrosadacity

5.7 How Simple Predictive Model used in real time application

5.8 How Multiple Predictive Model used in Real Time Application

5.9 Simple Predictive Model used in Real Time Industry

5.10 Multiple Predictive Model used in Real Time Industry

5.11 Comparing Simple and Multiple Predictive Model using Python

5.12 What is Correlation Analysis?

5.13 Correlation Coefficient and Hypothesis Testing

5.14 Product Movement Correlation, Partial Correlation and Non Metric Correllation

5.15 Introduction to Classification Model

5.16 Framming Single and Multiple Predictor Model

5.17 Application of Classification Model

5.18 Introduction to Discriminant Analysis

5.19 Two Group Discriminant Analysis

5.20 Three Group Discriminant Analysis

5.21 Multiple Group Discriminant Analysis

5.22 Application of Discriminant Analysis

5.23 Introduction to Association Rule

5.24 What is Apriori Algorithm?

5.25 How Apriori Algorithm used to Build to Recommendation System?

5.26 What is MBA(Market Basket Analysis)?

5.27 Application of MBA in Retail and Telecom Sectors

5.28 How MBA helps to Build the Recommendation System?

6.1 Image Processing and Image Extraction

6.2 Image-Histogram and Contrast Measures

6.3 Image Processing and Object Regognition

6.4 Viola Jones Algorithm – Face Regognition

6.5 Introduction to Time Series Analysis

6.6 Trend Line Analysis, Pattern Identification

6.7 Time Series Smothening Methods

6.8 Time Series Prediction Analysis

6.9 Differnece between AI and Machine Learning

6.10 Differnece between Machine Learning and Deep Learning

6.11 Differnece between Machine Learning and Data Science

6.12 Differnece between Machine Learning and Deep Learning

6.13 Data Architecture Design and Data Warehousing

6.14 Schema Design – Star Schema, Snow Flake Schema and Fact Concelltation

6.15 Master Data Management(MDM)