Full Stack Data Science Program

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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 R, Python, Tableau, Tensor Flow and Keras. It will enable you to master all three elements of Data Science - Statistics, Tools, and Business Knowledge. India's First Industry validated Data Science Course by NASSCOM.  Download Course Details
Learning Mode:  Online (Instructor-led)
Certification Certification:  Jigsaw and Nasscom
Course Duration:  6 months
Access Duration:  12 months

What you get

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

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

IBM: Data Journalism - First Steps, Skills and Tools



Case Study - Credit Card Spend Patterns

Case Study - Response Modeling Data

Using Statistics to Summarize Data Effectively

Building Descriptive Analytics Dashboards

Case Study - Direct Marketing

Exploratory Data Analysis

Cleaning Data - Missing Values, Outliers

Preparing Data for Modeling - Transformations, Derived Variables

Visualization Methods and Applications in Excel

Case Study - Campaign Response Data - Exploration and Preparation

Case Study - Second Hand Car Pricing

Case Study - Auto Insurance

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

Case Study - Customer Analytics

Understanding Tree Based Algorithms

Regression Trees

Classification Trees

Decision Trees

Random Forest and Ensemble Methods

Bagging and Boosting Algorithms

K-means Clustering

Case Study - Store Clustering

Case Study - Brand Perception for Snack manufacturer

Case Study - Text analytics with product reviews

Capstone Project - Building a churn scorecard for telecom

Understanding Visualization and Storytelling Principles

Information Hierarchy

The appropriate use of Color

Building interactive dashboards with Tableau

Creating an effective Story with Data

Visualisation with Tableau

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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 help you know which courses to take in what specific order, so that you can work toward a specific job role. Jigsaw Academy has crafted certain programs based on the skills that are in demand in the industry. You can maximize your chances of entering the field of analytics, Big Data, Machine Learning and Artificial Intelligence with these customized combinations of courses.

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.

SSC NASSCOM aims to scale quality capacity, with a larger talent pool for the industry and have greater alignment between industry occupational requirements and aptitude of the appearing candidates. To fulfill its key purpose, after detailed functional analysis, SSC NASSCOM has identified job roles in various service verticals, such as, IT Services, Engineering and R&D, Software Products and Business Process Management (BPM) etc.National Occupational Standards (NOS) or Occupational Standards (OS) defines one key function in a job role. NOS specify the standard of performance an individual must achieve when carrying out a function in the workplace. These Occupational Standards are combined to a set, which is called Qualification Pack (QP).

They are as follows:

  1. N8101 - Import Data
  2. N8102 - Pre Process Data
  3. N8103 - Perform exploratory data analysis
  4. N8104 -Perform research and design of algorithm networks
  5. N8105 - Applied pre-defined algorithmic model
  6. N8106 - Evaluate the risk of deploying algorithmic models
  7. N8107 - Evaluate the business performance of algorithmic models
  8. N8108 - Define business outcomes and create visualizations
  9. N8127 - Collect and define business requirements

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