Integrated Program in Analytics (IPA)

₹130,000 + taxes

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₹130,000 + taxes


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The IPA is an online-only, long-term program designed for working professionals who are interested in moving into a career in Data Science & Machine Learning. This program combines Data Science, Business Analytics, Machine Learning, Big Data, Data Visualization and Analytics Project Management education with the goal of creating industry-ready data professionals.
Learning Mode:  Online (Instructor-led)
Certification Certification:  Jigsaw and IBM
Course Duration:  10 months
Access Duration:  22 months

What you get

Guaranteed Internships
IBM Certification
IBM Certification
Placement Support
Capstone Project
Online Q&A Sessions
Live Online Classes
Faculty & Technical Support
Mobile App Access
Case Studies
UChicago Certification
In-Person Faculty Support
IOT Hardware Kit
IOT Hardware Kit
IconNot Applicable
* T&C apply

Tools & Curriculum Covered

Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools Tools

An Overview of Analytics and Data Science

Analytics Methodology and Problem Solving Frameworks

Models & Algorithms - How they work and Scope

Excel Basics & Excel Functions

Analytics Using Solver

Simulation and Clustering

Data Visualisation

Case Study - Budgeting with Excel

Case Study - Airline No Show Demand Simulation

Case Study - Table occupancy and Capacity Optimization

Case Study - Travel Inventory Optimization

Understanding Visualization and Storytelling Principles

Information Hierarchy

The Appropriate use of Color

Building Interactive Dashboards with Tableau

Building Interactive Dashboards with Power BI

Creating an Effective Story with Data

Visualization with Tableau

Case Study - Analysis of Credit Card Tracking

Using Statistics to Summarize Data Effectively

Visualization Methods and Applications in Excel

Building Descriptive Analytics Dashboards

Case Study - Auto Insurance

Case Study - Direct Marketing

R for Data Science - Setup and Introduction

Data Import from Multiple Sources - Flat Files, Databases, Web Sources etc.

Basic Data Manipulation with R - Summarizing, Aggregating, Functions

Advanced Data Manipulation with R including Custom Functions

Building Effective Visualizations with R

Case Study - Credit Card Spend Patterns

Case Study - Response Modeling Data

Understanding the Machine Learning Approach to Algorithms

Introduction to Python - Set Up, Libraries

Introduction to Pandas

Data Manipulation with Pandas

Visualization in Python: MatplotLib

Feature Engineering with Structured and Unstructured Data

Case Study - Customer Analytics

Exploratory Data Analysis

Cleaning Data - Missing Values, Outliers

Preparing Data for Modeling - Transformations, Derived Variables

Case Study - Campaign Response Data - Exploration and Preparation

Case Study - Second Hand Car Pricing

Introduction to Inferential Statistics

Understanding Probability and Distributions

Sampling Theory and How to Choose Representative Samples

Hypothesis Testing Concepts and Frameworks

Single Sample Hypothesis Tests - Z and T

Two Sample Tests - Independent and Paired

Multiple Samples Tests - ANOVA, Chi Square

Non-Parametric Tests

Case Study - HR Analytics

Case Study - Sales and Marketing Effectiveness

Linear Regression Models

OLS Algorithm and Implementation in R

Model Building and Iterations with Linear Models

Interpretation of Output and Evaluating Model Results

Generating Business Insights and Outcomes from Linear Models

Logistic Regression Models and the MLE Algorithm

Understanding the Odds Ratio

Building Logistic Models in R

Evaluating Logistic Regression Output - Probabilities, Confusion Matrix, Concordance, Lift

Generating Business Insights and Outcomes from Linear Models

Time Series Concepts

Simple Exponential Smoothing

Holt-Winter's Forecasting


Case Study - Market Mix Modeling to Calculate ROI on Marketing Activities

Case Study - Building a Default Risk Scorecard Model

Case Study - Predicting Debit Card Usage based on Historical Spends

Feature Engineering for Structured Data

Feature Engineering for Unstructured Data

Case Study - Churn Analysis

Tree Based Regression and Classifier

Random Forest and Gradient Boosted Machines

Case Study - Prices of AirBnB Properties in Amsterdam



Memory Based Engines

Model Based Engines: Singular-Value Decomposition (SVD) & Non-Negative Matrix Factorization (NMF)



K - Means

Agglomerative Clustering

Case Study - Image Classification (Retail Data)



Neural Networks

Convolutional Neural Network

Recurring Neural Networks (RNN) and Long Short-Term Memory (LSTM)

IBM Content - Tensor Flow

Case Study - OCR to Detect Regional Languages



Intuitive Examples of AI Applications

How is AI different from ML and DL?

Foundations and History of AI

Different AI categories

What is BlockChain?

Demystifying Cryptocurrency and Smart Contracts

Use Cases Driven by Block chain

Capstone Project - Building a Churn Scorecard for Telecom

Case Study - Predicting Proposal Success with ML

Case Study - Direct Marketing Business

Introduction to Big Data

Hadoop and HDFS


MapReduce Advanced



Programming in Hive

Case Study - Airlines Data Analysis

Case Study - Movielens Data Analysis

Data Extraction Tools

Cloudera Distributions , HUE & Impala

Case Study - Data Migration from RDBMS to HDFS

Introduction to NoSQL

HBASE and Oozie

Introduction to Cassandra

Architecture of Cassandra

Installing and Configuring Cassandra

Copy Bulk Data And Batch Statements

Interfaces to Apache Cassandra

Advanced Architecture

Indexing and Aggregation

Replication and Sharding

Cluster Administration

Introduction to Big Data and Real-time Big Data Processing

Introduction to Storm

Storm Installation and Configuration

Storm Advanced Concepts

Storm Interfaces

Storm Trident

Case Study: User Calls Analysis in MongoDB

Set-Up and Getting Started

Introduction to Spark

Internals of Spark

Spark Architecture

Spark Components

Spark R

Spark MLLib

Case Study - Recommendation Engine for eCommerce Data

Case Study - Streaming Data Analysis with KAFKA

Case Study - New York Stock Exchange Data Analysis

Case Study - Bank Transaction Data Analysis

Introduction to Spark

Resilient Distributed Dataset (RDD) and Operations (Non-Compulsory)

Spark SQL and Data Frames

Spark Streaming (Non-Compulsory)

SparkMLlib - Machine Learning Library

Capstone Project

Case Study: Email Marketing Campaign Data Analysis and Reporting

An Introduction to the SAS language

Data Import into SAS

Data Manipulation with SAS

Advanced Data Manipulation with SAS

Case Study - Credit Card Spend Analysis

Case Study - Sales Performance Review

Case Study - Creating an Analytics Sandbox for a Pizza Company


Our programs have been designed for all students regardless of any prior knowledge of analytics, statistics or coding. We have had and continue to have many successful students who are from non-IT or non-mathematics backgrounds. But the subject matter is quantitative, and hence a background in maths, statistics or coding is helpful. If you have specific questions regarding eligibility or prerequisites for any program, please contact us at +91 9019217000.

Long term programs are better suited to professionals looking to transition to an entirely new role in data science or machine learning, or those who are looking to start careers in these fields. They are more comprehensive and prepare you a variety of roles in these domains. With this knowledge, professionals will have the flexibility to choose the type of role they move into.

If you are enrolled for any of our in-person classes, you can ask your questions during the live sessions. For online learning, you can reach our faculty through email, call, chat (Google hangouts) or ask via the forum. Once the course is over, you can still get in touch with us via email.

We have a strong network in the field of analytics. We are constantly in touch with various companies for their hiring and training needs. We identify the right opportunities for our students and help them get in touch with the relevant HR teams.

Success Stories & Placements

Success Stories

"I come from a non-technical and non-programming background. I have excelled in the Data Science & Machine Learning specializations at Jigsaw due to which I've been a winner of two industry mentored awards."

Vicky Crasto

L&D Manager, eLearning Company