Module: Data Analytics

Why Data Analytics?

Where & how to apply Data Analytics?

Discussion with R and Python. Why not others Programming ?

Data-Driven Decision Making

Types of Data Analytics

Data Categories

Data Cycle

Data Mining and Data Preparation

Data Cleaning

Approaches for Handling Missing Data

Forecasting and Predictive Analytics

Statistical Inference

Data Base Management Systems

BI and Tableau

Module: Maths, Programming and Beyond

Linear Algebra

Probability and Distribution Models

Exploratory Data Analysis

Data Structures and Algorithms

Python Programming

Advanced concepts like Comprehension, File handling, Regular Expressions, Object-oriented Programming, Pickling

Data handling in Python – Pandas & MongoDB

Probability and Statistics with NumPy

Probability theory, Bayes theorem, distributions

Module: Data-Driven Modelling

Prior Knowledge

Data Preparation

Data Modelling Process

Overfitting and Underfitting

Evaluation of Model

Data Validation

Model Analytics

Testing and Training Data

Validation and Cross-validation

Errors and Biases

Bias and Variance

Feature Selection

Data Visualisation in Python (Matplotlib, Seaborn)

Module: Machine Learning Concepts

Supervised Learning

Unsupervised Learning

Reinforcement Learning


Linear Regression

Multiple Linear Regression


Supervised Learning Techniques

Data Processing

Unsupervised Learning : Clustering

Classification using Decision Tree

Performance Classifiers

SVM Classifier

Naïve Bayes Classifier

Logistic Regression

Decision Tree Classifier

KNN Classifier


Nearest Neighbouring

Random forest classifiers

Deep Learning fundamental

Artificial Intelligence

5+ Case Studies and Projects