6 Months Certification Program in

Data Science & Machine Learning

Contact us now

What We Offer In Data Science & Machine Learning

  • Domain Overview
  • Mathematical Data Science
  • DataBase Management Systems
  • Enterprise Resource Planning Concept
  • Data Engineering
  • Statistical Data Science
  • Data Science overview and life cycle
  • Data Distillation and Feature Engineering
  • Machine Learning Model 1
  • Commencement of Capstone
  • Machine Learning Model 2
  • Deep Learning
  • Text Analytics
  • Computer Vision and Image Processing
  • Data Visualization Techniques
  • Cloud Storage and Computing
  • IOT , IIOT & Industry 4.0
  • RPA as Process Automation Tool
  • Capstone Project
  • r
  • python
  • power bi
  • tableau
  • h20ai
  • anaconda


Dr. Ashutosh Khanna

Faculty & Head – Strategy and General Management, International Management Institute (IMI), Chairman - Centre for Disruptive Innovation & Enterprise(CDIE), International Management Institute, New Delhi

Mr. Vijaya Kumar Pisupati.

CEO, Digidexxt Information Systems Private Limited

Dr. Deepu Philip

Professor – Industrial Management Engineering (IME)
Faculty - Design Program
Head - Smart Systems and Operations Laboratory (SSOL) of IIT Kanpur

Dr. Kishore Rathi

Faculty – XIMR, SP Jain, JBIMS
Member of SEBI led Corporate Bond Market

Who can attend

* Fresh B.Tech / MBA with minimum 1 year of domain / IT experience, Domain Specialist.

* Fresh graduates looking for enhancing their career in Data Science.

* Aspirants wishing to rejoin the workforce after a career break.

* Professionals working in domains including Manufacturing, Supply Chain, Finance (Banking and Insurance), Retail, Logistics, Oil and Gas, Health and Pharma

Course Details

A firm’s Business Chain
Business Processes ( MTO , MTS ,SCM , CRM )
Business Processes Modelling

Set Theory
Vector algebra
Linear Algebra
Linear Programming
Derivatives and Applications of Derivatives ---Applicability of these into Business
Integration and Applications of Integration
Partial Differentiation

Basic File Systems and Introduction to Databases
Database terminologies, Characteristics, and Architecture
Data Models (Physical, Logical, and Conceptual)
ER diagrams.
The 12 Codd’s Rules
Basic Design of a Database
Introduction to Relational Databases and NoSQL
Relational Calculus (Tuple and Domain)
Relational Algebra
Introduction to Keys: Primary Key, Candidate Key, Super Key, and Foreign Key
Integrity Constraints (Domain, Entity, Key, and Referral Constraints)
Data Definition Language, Data Query Language , Data Manipulation Language and Data Control Language
SQL Commands
Query Processing
Dependencies: Functional (Trivial and Non-Trivial), Multi-Valued and Join
What is Normalization?
Why Normalization is required and why it is essential.
Normalization (up to Boyce Codd normal form)
Database Security- Classification of Data, Risks, and Threats
Database Access Control and Privilages
Some Key Differences between MySQL, MongoDB and Oracle Databases

What is ERP
Prominent ERP Systems
Features of ERP systems
ERP as a Core system to Business Processes

Intro to Data Engineering
Types of Data and Data Sources
Big Data - Four V's of Data
Lambda architecture - Batch and Streaming Analytics
Data Ingestion - Queries , Scripts and Rest API's
Work Flow orchestration- Building Data Pipelines and Connectors
Data Storage - Types of Data Stores
Introduction to Python for Data Science
Data Processing - Multi Threading and Parallel Processing
Code Management and Version control using git

Measures of Central Tendencies
Probability Distribution functions
Estimation & Testing
Stochastic Process
Multi Variate Analysis
Design of Experiments
Anova, PCA, FA

Origin, Introduction and evolution of data as a key asset
Identification of business problem to ------- outcome interpretation to outcome automation

Data Wrangling
Feature Engineering
Feature Interactions

Exploratory Data Analysis - Understanding the Data - Descriptive Analysis and Story telling with insights
Gradient Descent
Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Variable interactions and their effects on models
Linear models with Regularization - Lasso and Ridge Regressions
Mixed Linear Models
Support Vector Machines
Naïve Bayes
K Nearest Neighbours

Decision Trees
Homogeneous Ensemble models - Random forest and Custom model Building
Heterogeneous Ensemble models - Custom Model Building
Hierarchical Clustering
K means Clustering
Density-Based clustering
Hyperparameter tuning
Recommendation Engines
Introduction to Timeseries and different components
Basic and Traditional Forecasting Methods
Autoregressive Models and their variants
Holt-Winters Forecasting models
Advanced Forecasting Models
What is Optimisation?
Converting Business Problem into Mathematical Objective Function for Optimization
Optimization using Linear Programming
Advanced Optimisation Techniques(Genetic Algorithm, Ant Colony Optimization, Simulated Annealing)

Introduction to Deep Learning
Neural Networks Basics
Multilayer Perceptron
Convolutional Neural Networks
Recrurrent Neural Networks
Advanced Neural Networks
Interpretation of Neural Networks
Reinforcement learning

Basics of text analytics
Text Data Distillation - Word and Sentence tokenization,
Frequency Distribution, Dealing with stop words,
Lexicon Normalisation, Stemming, Lemmatization etc.,
Extracting Features out of text
POS Tagging
Text Summarization
Fuzzy matching
Named Entity Recognition
Advanced Sentiment Analysis
Text Classification
Semantic Analysis and Engine

Understanding images
Pixels and colors in an image
Cropping, Rotations, Scaling and Resizing
Image Sharpening
Blurring and Masking
Grey Scaling
Erosion, Dilation, Opening and Closing
Drawing Images
Edge Detection
Detecting shapes
Object detection
Mini Project

Objectives of visualization
Principles of Visualization -- Gestalt law
Exploratory Analysis ( Univariate, Bivariate -- Exploratory and trend analysis)
Important Distributions
Visualization hierarchy, Color hierarchy
Patterns and Anti Patterns
Case Study

What is Cloud Computing
Cloud Computing Platforms
Cloud Deployement Models
Key Cloud Concepts
Cloud Service Models
Cloud use Cases
Data Centre Architecture in Cloud
Virtual Networks
Virtual Machines in Cloud
Storage in Cloud
Container on Cloud
Application on Cloud
Development, Deployement, Monitoring
AWS Solution Architect
AWS SysOps associate
Microsoft Azure Fundamentals

Introduction to industry 4.0
over view of ISA 95 model
Technology Pillars
Introduction to Internet of Things
IOT Architecture and Protocols
Internet of Things sensing and actuator devices
Smart Sensors and Sensor Networking
Product Development for IOT
Edge Processing
Digital Twins
Dynamics between AI and IOT

What is Robotic Process Automation
Natural language processing and RPA
How Robotic Process Automation works
Why to Automate Repetitive Tasks/Process
RPA Solution Architecture Patterns – Key Considerations
Input Data Handling Solution Pattern
Exception Handling
Transaction Logging
Credential Management
Secure Execution
Monitoring and Reporting
List of Robotic Process Automation Tools
Robotic Process Automation Tool selection Checklist

Submission , Presentation , Discussion & Evaluation


Key Learning Outcomes

  • Strengthening conceptual understanding
  • Impress on the business understanding of core subjects
  • Case based teaching methods
  • Ability to identify business value enhancement areas
  • Elevating business chain into value chain
  • Injecting Optimization, Prediction, Predictive modelling as appropriate into business chain
  • KPIs’- from computation to prediction
  • Strong interface with industry
  • Leverage Data Science to hone decision-making skills
  • Create a data-driven framework for organization, Develop Hypothesis and Insights
  • Understand the importance of Data Science and how it relates to business decisions and organizational success
  • Data driven strategies
  • Build an AI centric team and AI driven culture in organization.
  • Identify ways in which organization can choose data driven decisions.
  • Approach to digital transformation


  • Case study-based teaching
  • Involvement in research and innovation
  • Industrial interactions and real-time projects
  • Industry-Institute Interface
  • Core understanding of programming language
  • In-depth knowledge in Hardware, system setup, OS concepts etc
  • Business models used in Data Science
  • Domain- BPM, SCM, CRM, KPAs’, KPIs’
  • Thorough understanding of IT landscape



  • Teach statistical thinking
  • Focus on conceptual understanding
  • Integrate real data with context and purpose
  • Foster active learning
  • Use technology to explore concepts and analyze data
  • Use assessment to improve and evaluate student learning
  • Suggest the student about the progress and area of improvements
  • Motivate the students in research and paper publications
  • Encourage the student for participating in group discussion and mock interviews