Part - I Introduction to Data Science

What is Data Science

Evolution of Data Science

Various Data Science related profiles and applications

Machine Learning and AI in HFT

Part –II Machine Learning using R
Section - A
R - Programming Basics

Data Manipulation and Wrangling

Writing Functions

Graphics

Statistics using R

Descriptive Statistics

Inferential Statistics

Linear Regression

Statistical Plots

Section - B
Machine Learning
1. Supervised Learning - Key Methods and Techniques

Multiple Linear Regression & Polynomial Regression

Shrinkage Method for model improvements

Variable Reduction Techniques - PCA / PCR

R-Squared

Case Study - Using R

Logistics Regression

Shrinkage Method for model improvements

Variable Reduction Techniques - PCA / PCR

Model Accuracy - Precession vs Recall and Confusion Matrix

Case Study - Using R

Tree Based Methods

Pruning of Tree

Measuring r^2 in tree based methods

Gradient Boosting, Bagging and Ensemble Methods

Model Accuracy - Precession vs Recall and Confusion Matrix

Case Study - Classification - Using R

Case Study - Regression - Using R

Naïve Bias Algorithm

Case Study - Using R

Section - C
Time Series Modelling

Time Series Decomposition

Holt Winters smoothing

ARIMA Models

Case Study - using R

Part III Data Visualization
Data Visualization Using Tableau

Importing Data into Tableau

Creating Graphs

Creating Sets and Performing Custom Calculations in Tableau

Using R in Tableau

Creating Dashboard

Publish a Tableau Dashboard on web

Case Studies - 1

Case Studies - 2