Methodology

  • Lectures are released on a daily basis with relevant practise assignment and quiz

  • Lecture materials and Jupyter notebooks will be shared with lectures on same day

  • Post completion of the course, you will receive a certificate of completion

  • Complete two end-to-end Capstone Projects on real-world problems

Faculty

A team of IIT Kharagpur graduates & experienced data science professionals

Chief AI Faculty

Shivam Dutta

Helped set-up the data science division of Radware in India. Worked extensively on convolutional neural networks and reinforcement learning algorithms.

Chief Faculty of Data Science

Vikash Srivastava

Developed the entire FX options volatility prediction stack at HSBC using deep supervised LSTM networks. Possesses keen interest in Natural Language Processing.

Chief Data Scientist

Alok Anand

Worked extensively on hybrid fraud detection techniques at American Express. Built a state-of-the-art merchant recommender system being used globally.

Curriculum

  • 1

    Day 1 : Data Science in Industrial Setup - Why, What, How?

  • 2

    Day 2 : Python - Essential Toolkit for a Data Scientist

    • Today's Agenda

    • Exploring Data Types in Python

    • Strings

    • Boolean Variables

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 3

    Day 3 : Basic Units of the Python Universe

    • Today's Agenda

    • Introduction to Lists

    • List Indexing & Slicing

    • List Functions & Methods

    • Tuples

    • Sets

    • Dictionary Objects

    • Indentation in Python

    • If elif else statements

    • For Loop

    • While Loop

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 4

    Day 4 : Introduction to Methods & Functions

    • Today's Agenda

    • Methods

    • Functions

    • Scope

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 5

    Day 5 : Writing Production Grade Code

    • Today's Agenda

    • What is Production Environment?

    • Object Oriented Programming

    • Methods

    • Error & Exception Handling

    • Real World Example

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 6

    Day 6 : Facing off with Linear Algebra

    • Today's Agenda

    • Linear Algebra & Its Applications

    • Vectors

    • Matrices

    • Today's Summary

    • Reference files

    • Lecture Materials & Practise Assignment

    • Additional Reading Material

    • Practise Quiz
  • 7

    Day 7 : Calculus

    • Today's Agenda

    • Introduction to Calculus

    • Today's Summary

    • Reference files (PDF)

    • Additional Reading Material

    • Practise Quiz
  • 8

    Day 8 : Probability Theory

    • Today's Agenda

    • Concepts in Probability

    • Random Variables and its Types

    • Probability Mass Function

    • Probability Density Function

    • Today's Summary

    • Additional Reading Material

    • Practise Quiz
  • 9

    Day 9 : Statistics - Measures of Central Tendency & Spread

    • Today's Agenda

    • Measures of Central Tendency

    • Measures of Spread

    • Symmetry and Skewness

    • Reference files(ppt)

    • Mean, Median, Mode, Variance & IQR - Python Implementation

    • Today's Summary

    • Additional Reading Material

    • Practise Quiz
  • 10

    Day 10 : Statistics - Hanging in with Statistical Distributions

    • Today's Agenda

    • Discrete Statistical Distributions

    • Continuous Statistical Distributions

    • Today's Summary

    • Additional Reading Material

    • Practise Quiz
  • 11

    Day 11 : Statistics - Covariance, Correlation & Chi-Squared

    • Today's Agenda

    • Explanatory vs Response Variable

    • Covariance & Correlation

    • Chi - Squared

    • Today's Summary

    • Reference files (PPT & Chi - Squared table)

    • Practise Quiz
  • 12

    Day 12 : Numpy - Operations on Arrays

    • Today's Agenda

    • Why Numpy

    • List vs Array

    • Array Inspection

    • Placeholders

    • Array Indexing and Slicing

    • Array Manipulation

    • Basic Operations

    • Real World problem: Operations on Image

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 13

    Day 13 : Pandas - Chug Data, Spit Frames

    • Today's Agenda

    • Pandas package - Introduction

    • Introduction to Series and Data Frame

    • Load csv, xlsx and json format

    • Saving file to location

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 14

    Day 14 : Pandas - Operations on Dataframes

    • Today's Agenda

    • Data Frame Inspection

    • Indexing and Slicing

    • Manipulating Columns

    • Merging dataframes

    • Unique and missing values

    • Groupby

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 15

    Day 15 : Matplotlib - Data Visualization

    • Today's Agenda

    • Matplotlib & Seaborn : Worth 1000 words

    • Line Plot

    • Bar Plot

    • Box Plot

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 16

    Day 16 : Seaborn - Data Visualization

    • Today's Agenda

    • Histogram

    • Understanding Correlation

    • Correlation Heatmap

    • Two-way plots

    • Today's Summary

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 17

    Day 17 : Capstone Project - I

    • Defining the Problem Statement
  • 18

    Day 18 : Machine Learning Essentials

    • Machine Learning Overview

    • Supervised Learning

    • Unsupervised Learning

    • Reinforcement Learning

    • Steps for Supervised Machine Learning Modelling

    • Deep Dive in Supervised Machine Learning Modelling

    • Effective approach for training any Machine Learning algorirthm

    • Practise Quiz
  • 19

    Day 19 : Linear Regression

    • Implemention of Linear Regression in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 20

    Day 20 : Multiple Linear Regression

    • Definition of Multiple Linear Regression

    • Assumptions of Multivariate Linear Regression

    • Implementing Multiple Linear Regression in Python

    • Concept of Gradient Descent

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 21

    Day 21 : Logistic Regression

    • Classification with Logistic Regression

    • Implementing Logistic Regression in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 22

    Day 22 : Naive Bayes Classifier

    • Introduction to Naive Bayes Algorithm

    • Implementation of Naive Bayes classifier in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 23

    Day 23 : Model Evaluation Metrics

    • Metrics for evaluating classification models

    • Metrics for evaluating regression models

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 24

    Day 24 : Bias-Variance Trade-off

    • Training Machine Learning Models

    • Overview of Bias-Variance tradeoff

    • Error - Mathematical Representation

    • Bias - Variance tradeoff

    • Ways to balance the Bias-Variance tradeoff

    • Practise Quiz
  • 25

    Day 25 : Regularization Techniques

    • Introduction to Regularization Techniques

    • Predicting sales using Regularization Techniques

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 26

    Day 26 : Support Vector Machines

    • Support Vector Machines

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 27

    Day 27 : Kernel Tricks

    • Kernel Tricks

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 28

    Day 28 : Neural Network Architecture

    • What is a Neural Network?

    • Implementing Neural Networks in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 29

    Day 29 : Hyperparameter Tuning in Neural Networks

    • Hyperparameter Tuning in Neural Networks

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 30

    Day 30 : Introduction to Decision Trees

    • Introduction to Decision Trees

    • Predicting humidity using Decision Trees

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 31

    Day 31 : Ensembles of Decision Trees

    • Why ensemble of Decision Trees?

    • Different ensembling methods

    • Bagging vs Boosting

    • Advantages and Disadvantages of ensembling

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 32

    Day 32 : Random Forest

    • Introduction to Random Forest Algorithm

    • Random Forest Implementation in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 33

    Day 33 : Gradient Boosting Machine

    • Introduction to Gradient Boosted Machine

    • Implementing GBM in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 34

    Day 34 : Hyperparameter Tuning in Tree Algorithms

    • Hyperparameter Tuning in Tree Algorithms

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 35

    Day 35 : Principal Component Analysis

    • Introducing Principal Component Analysis

    • PCA as Dimensionality Reduction Technique

    • PCA for visualization

    • Choosing the number of components

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 36

    Day 36 : K-Means Algorithm

    • Introduction to K-Means algorithm

    • Identify similar handwritten digit using K-Means

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 37

    Day 37 : Hierarchical Clustering

    • Introduction to Hierarchical Clustering

    • Customer segmentation using Hierarchical Clustering

    • K-Means vs Hierarchical Clustering

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 38

    Day 38 : Anomaly Detection

    • Univariate Anomaly Detection

    • Multivariate Anomaly Detection

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 39

    Day 39 : Engineering relevant variables

    • Engineering relevant variables

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 40

    Day 40 : Recommender Systems - Collaborative Filtering

    • Collaborative Filtering

    • Implementation of Collaborative Filtering in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 41

    Day 41 : Recommender Systems - Content-based Filtering

    • Content-based Filtering

    • Implementing Content Based Filtering in Python

    • Lecture Materials & Practise Assignment

    • Practise Quiz
  • 42

    Day 42 : Introduction to Deep Learning

    • Why Deep Learning?

    • Practise Quiz
  • 43

    Day 43-48 : Capstone Project - II

    • Loan Default Prediction - Kaggle Benchmark Score Solution