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
Regression
Linear Regression
Multiple Linear Regression
SLR
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
K-means
Nearest Neighbouring
Random forest classifiers
Deep Learning fundamental
Artificial Intelligence
5+ Case Studies and Projects