Introduction of Statistics
Types of Statistics
Types of variables
Levels of Measurements
Construction of frequency polygon
Constructing frequency table
Plotting histogram
Concept of population and sample
Measures of central tendency
Measures of dispersion
Measures of position
Probability concepts
Discrete probability distribution
Continuous probability distribution
Central limit theorem
Sampling methods
Estimation and confidence interval
p-values
Hypothesis testing
Types of Error
Analysis of Variance

Introduction to Python Installation Python Anaconda Basic Python Variables Variables Assignment Strings List & List as Stack Dictionary Tuples Sets & Frozen Sets Operators Conditional Statements Loops Functions and Methods Error Handling
Libraries in Python – Pandas Scikit Learn Numpy MatplotLib
Reading and Writing files

Regression
Classification techniques
Time Series Forecasting
Clustering

Terminologies – Records, Fields, Tables, Introduction to database
Concept of ER Modelling
Relational Algebra: The fundamental operations of relational algebra are as follows −
Select
Project
Union
Set different
Cartesian product
Rename
Introduction to SQL, SQL Syntax, SQL data Types, SQL Operators, Table creation in SQL- Create, Insert, Drop, Delete, and Update, Table access & Manipulation, Select with Where Clause (In between, logical, operators, wild cards, order, group by), Concepts of Join – Inner, Outer

SVM
KNN
Adaboost
Ridge and Lasso regression
Principle Component Analysis (PCA)
Gradient Boosting

Introduction, Natural language processing, Natural language processing, Text clustering, Topic modelling, Document summarization,  Sentiment analysis, Text visualization

Introduction to deep learning, Neural Networks Basics, Shallow neural networks, Deep Neural Networks

1.Understanding the project topic

2.Research about the project

3.Constructing the project using R programming

4.Step by step building of project

5.Mentoring sessions for the project

6.Final product