Data Science

Master’s Program in Data Science And Artificial Intelligence


Bachelor Graduate Program in Data Science (BGP-DS)

24+12 Months|Physical Interactive Learning | Live Projects & Case Studies | Career Assistance
A comprehensive program in Data Science teaches by most influential industry leaders and world-class faculty.


Module 1 (Basics Fundamentals)

  • Introduction of Microsoft Office
  • MS Word
  • MS Excel
  • MS Power Point

DSU (Digital Secure User)

  • What is Digital Security
  • Authentication and Authorization
  • Importance of Digital Security
  • How to achieve Digital Security
  • Potential Threats in Digital Security


Computer Assembling and Installation

  • Introduction of Computer Hardware
  • Assembling a Computer
  • Disassembling a Computer
  • Basic Peripheral Devices


Fundamentals of Operating System

  • What is Operating System
  • Windows
  • Linux
  • Operating System Shell vs Kernel
  • Operating System Services


Computer Networking Basics

  • Introduction of Computer Networks
  • How Internet Works
  • Difference Between Website and Web Application
  • IP Address
  • What is a Domain Name


Client Server Architecture

  • What is Client Server Architecture
  • Definition of Client, Server
  • Apache, nginx
  • Components of CS Architecture
  • What is a Protocol


Web Application Fundamentals

  • What is Web Applications
  • Components of a Web Application
  • Web Application Life Cycle
  • Application Routing
  • Request and Response
  • HTTP Status Codes


Basic Maths & Data Structure

  • Basics of Mathematics
  • Number Systems
  • Algorithm & Pseudocode
  • Graph
  • Tree
  • Sets
  • Functions
  • Linked Lists


Module 2 (Data Science using Python)

  • Introduction to Data Science
  • Data Collection and Cleaning
  • Python Fundamentals
  • Control Flow & Functions
  • Array Computations
  • Data Manipulation
  • Visualizing Data
  • Web Scraping


Module 3 (Statistical Foundations)

  • Introduction to Statistical Analysis
  • Exploratory Data Analysis
  • Introduction to Probability
  • Probability Distribution Functions
  • Random Processes
  • Inferential Statistics


Module 4 (R Programming for Data Science)


Module 4 (Machine Learning )

  • Introduction to Machine Learning
  • Supervised Learning - Regression
  • Mathematical and Bayesian Models
  • Natural Language Processing
  • Supervised Learning – Classification
  • Dimensionality Reduction
  • Unsupervised Learning Using Clustering
  • Association Rules Mining & Recommendation Engines
  • Time Series Analysis
  • Model Evaluation & Hyperparameter Tuning
  • Model Boosting & Optimization



Module 6 (AI and Deep Learning)

  • Neural Networks with Tensor Flow 2.x
  • Deep Learning for Images using CNN
  • Deep Learning for Sequences using RNN
  • Building Games using RL


Module 7 (Data Mining and Warehousing)

  • Data Warehousing
  • Data Mining
  • Data Integration and ETL
  • Mining Frequent Patterns


Module 8 (Big Data Storage and Spark Developer)

  • Introduction to Big Data and Big Data Mining
  • Big Data with Hadoop
  • Apache HBase and Hive
  • Data Ingestion
  • Apache Spark
  • Big Data Analytics
  • In-Class Project


Module 9 (Tableau- Data Visualization)

  • Introduction to Data Visualization
  • Working with Data & Visualizations in Tableau
  • Advanced Visualizations
  • Sharing your Insights


Module 10 (Data Science Capstone Project)



An industry-level project will be a part of your Post-Graduate Certification to consolidate your
Learning. This industrial project will ensure that you have accumulated the real-world
Experience to start your career as a globally recognized Data Scientist.


Main Tools & Languages You Will Learn




For whom

  • Working professionals in IT / Analytics / Statistics / Big Data / Machine Learning Fresh graduates from
    Engineering / Mathematics / IT backgrounds
  • Professionals looking to develop skills to do statistical analysis to support decision making
  • Final year students completing their graduation on or before December 2020



  • 10+2 (PCM) BE / B.Tech / BCA / MCA / B.Sc. (Maths) / M.Sc (Maths) with a minimum of 50%
    aggregate marks is compulsory.
  • Candidates with Mathematics, Statistics background will be given preference.
  • A minimum of two years of full-time work experience after graduation or post-graduation is required.



Top 10 Reason choose Data Science

  • High demands of experts
  • Open opportunity to work with top executives
  • Attractive salary and perks
  • Increase business knowledge
  • Open opportunity to work with big brands
  • Key Factor in Decision making
  • Data Analytics used everywhere
  • Various Job opportunities
  • Flexibility of learning
  • Diverse working exposure