Full Stack Data Science Program

₹48,400 + taxes

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₹48,400 + taxes

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This is the most comprehensive Data Science course available, covering all steps of the Data Science process from Data Integration, Data Manipulation, Descriptive Analytics and Visualization to Statistical Analysis, Predictive Analytics and Machine Learning models, using the most in-demand tools like R, Python, SAS, and Tableau. It will enable you to master all three elements of Data Science - Statistics, Tools, and Business Knowledge.
Learning Mode:  Online (Self-paced)
Certification Certification:  Jigsaw and IBM
Course Duration:  6 Months
Access Duration:  12 Months

What you get

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
Guaranteed Internships
In-Person Faculty Support
IOT Hardware Kit
IOT Hardware Kit
IconNot Applicable
* T&C apply

Tools & Curriculum Covered

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

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

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

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

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.

Full Stack Programs are designed to give you the combination of skills that’s most in-demand in the industry. For example, the Full Stack Data Science will equip you with the knowledge of SAS, R, Python, Excel, VBA and Advanced SAS. This combination will give you a better chance of entering the field of analytics than learning a single course.You can speak to a Jigsaw Academy counselor if you still have questions.

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

"Kanav, an IT professional, switched to analytics, and gained a HUGE salary hike in six months  After working for over two years as a data test engineer with a leading software MNC, Kanav decided ..."

Kanav Nayyar

Founder, Data Analyst, Leading Analytics Company