PG Program in Data Science
& Engineering

Learn from IIBM Institute of Data Science that makes you job-ready and build a rewarding career

India’s top ranked program  | 9 Months | Online Mentorship

Fees: INR 80,000 + GST

Batch Start: 15th August

Enquire: +91-9808811409 


Why we’re ranked #1


  • Designed for working professionals
  • Modelling, Forecasting, Data Science, Analytics, Data visualisation and more


  • Designed for working professionals
  • Modelling, Forecasting, Data Science, Analytics, Data visualisation and more

Dedicated Career

  • Job opportunities shared by 350+ companies
  • 7000+ Alumni transitions with 48% avg salary hike

Join India's #1 Data Science & Engineering Program

Online Mentorship | 09 Months

Module 1: Fundamentals of Data Analytics

2 Hours

 Insurance, Automobile, Retail , Banking, Retial, Marketing, Aviation, Defence, Social Services, Computer Vision

Insights, Reports, Historical Performance, Trend, Visualization

Descriptive, Diagnostic, Predictive, Prescriptive, Exploratory 

Difference between AI, Machine Learning, Deep Learning and Data Science

Module 2: Basics concepts in Statistics for Data Analytics

4 Hours

Undertand difference between Population vs Sample, importance of statistical concepts in data science and ML models

Know the foundation principal in statistics – Central Limit Theorem

Understand the importance of Mean, Medium, Mode of a variable

Understand the importance of Variance, Standard Deviation of a variable

Different scales of measuring data – Nominal, Ordinal, Interval, Ratio

Application of central tendencies for data analysis

Usage of correlation, regression concepts for data analysis

Module 3: Advanced concepts in Statistics for Data Analytics

6 Hours

Understand different types of data distribuitions – Uniform, Binomial, Poisson, Normal, Logarithmic, ExponentialHypoth

Learn to Perform Null Hypothesis and p-value to find the significant variables

Learn to perform t-test, z-test to measure the variance between the means of two samples or population

Learn techniques like ANOVA (1-way, 2-way, w/o replication), ANCOVA, f-test to compare the variance betweeen variables

Perform Chi-square test to evaluate distribution of sample same as expected population under study

Introduction to probability

Types of events

Marginal Probability

Baye’s Theorem

Module 4: Python essential for Data Science

15 Hours

What is Python?

Why is Python essential for Data Science?

Versions of Python

How to install Python

Anaconda Distribution

How to use Jupyter Notebooks

Command line basics

GitHub overview

How to execute Python scripts from command line

Python Data Types

Programming Concepts

Python, Operators

Conditional Statement, Loops

Lists, Tuples, Dictionaries, Sets

Methods and Functions

Errors and Exception Handling

Object Oriented Programming in Python

Modules and Packages

NumPy overview

Arrays & Matrices

NumPy basic operations, functions

NumPy for Data Analysis

Importing Pandas

Pandas overview

Pandas Series and Data Frames

Dealing with missing data

GroupBy, Merging, Concatenating and Joining

Data Input & Output

Why visualize data?

Importing MatplotLib

Chart: Line Chart, Bar Charts and Pie Charts

Plotting from Pandas object

Object Oriented Plotting: Setting axes limits and ticks

Multiple Plots

Plot Formatting: Custom Lines, Markers, Labels, Annotations, Colors

Importing Seaborn

Seaborn overview

Distribution and Categorical Plotting

Matrix plots & Grids

Regression Plots

Style & Color

Review Session

Module 5: Data Science with Python

11 Hours

Key Terms in Data Science

Introduction to Supervised Learning,Unsupervised Learning

What is Reinforcement Learning?



Data Science Life Cycle

Data Science in cloud







Missing Values

Outliers treatment

imbalance Data Handeling

Standardization / Normalization 


Data Science Library
Scikit learn

Module 6: Supervised Learning-Regression and Classification Algorithms:

20 Hours

Understanding Regression
Introduction to Linear Regression
Linear Regression with Multiple Variables
Disadvantage of Linear Models
Interpretation of Model Outputs
Assumption of Linear Regression
Project 2
Understanding classification
Introduction to Logistic Regression
Odds Ratio
Logit Function/ Sigmoid Function
Cost function for logistic regression
Application of logistic regression to multi-class classification
Assumption in Logistics Regression
Evaluation Matrix : Confusion Matrix, Odd’s Ratio And ROC Curve
Advantages And Disadvantages of Logistic Regression.
Project 3
Understanding Decision Tree
Building Decision Tree
Using ID3 / Entropy
CART model – Gini index
Stopping Criteria And Pruning
Hyperparameter Tunning for Decision Tree
Overfitting Problem
Tradeoff between bias and variance
Ensamble methods
Random Forest
Grid Serach CV
Hyperparameter Tunning for Random forest
Feature inmportance
Project 4
Conditional Probability
Bayes Theorem
Building model using Naive Bayes
Naive Bayes Assumption
Laplace Correction
NLP with Naive Bayes
Project 5
Basics of SVM
Margin Maximization
Kernel Trick
RBF / Poly / Linear
Project 6
Distance as Calssifier
Euclidean Distance
Manhattan Distance
KNN Basics
KNN for Regression & Classification
Project 7

Module 7: Unsupervised Learning

9 Hours

Clustering Methods
Agglomerative Clustering
Divisive Clustering
Project 8
Basics of KMeans
Finding value of optimal K
Elbow Method
Silhouette Method
Project 9
Eigenvalues and Eigenvectors
Orthogonal Transformation
Using PCA
Project 10

Module 8: Tableau

10 Hours

creating views, visual analysis, mapping , generate trends

Creating waterfall, bump, funnel, box, pareto, histogram

Introduction to RNN
Sequence prediction of RNN

Creating Interactive Dashboards, Adding views and objects to dashboards

Project 13:

Module 9: Big Data Introduction

2 Hours

Module 10: Hadoop

40 Hours

Introduction to Data and System
Types of Data
Traditional way of dealing large data and its problems
Types of Systems & Scaling
What is Big Data
Challenges in Big Data
Challenges in Traditional Application
New Requirements
What is Hadoop? Why Hadoop?
Brief history of Hadoop
Features of Hadoop
Hadoop and RDBMS
Hadoop Ecosystem’s overview
Installation in detail
Creating Ubuntu image in VMware
Downloading Hadoop
Installing SSH
Configuring Hadoop, HDFS & MapReduce
Download, Installation & Configuration Hive
Download, Installation & Configurations Pig
Download, Installations & Configurations SQL
Download, Installations & Configurations Hive
Configuring Hadoop in Different Modes
File System – Concepts
Replicating Factor
Version File
Namespace IDs
Purpose of Name Node
Purpose of Data Node
Purpose of Secondary Name Node
Purpose of Job Tracker
Purpose of Task Tracker
HDFS Shell Commands – copy, delete, create directories etc.
Reading and Writing in HDFS
Difference of Unix Commands and HDFS commands
Hadoop Admin Commands
Hands and exercise with Unix and HDFS commands
Read / Write in HDFS – Internal Process between Client, Name Node & Data Nodes
Various Ways of Accessing HDFS
Understanding HDFS Java classes and methods
Commissioning / Decommissioning Data Node
Replication Policy
Network Distance / topology Script
Abut MapReduce
Understanding block and input splits
MapReduce Data types
Understanding Writable
Data Flow in MapReduce Application
Understanding MapReduce problem n datasets
MapReduce and Functional Programming
Writing MapReduce Application
Understanding Mapper function
Understanding Reducer Function
Understanding Driver
Usage of Combiner
Usage of Distributed Cache
Passing the parameters t mapper and reducer
Analyzing the Results
Log files
Input Format’s and output Formats
Counters, Skipping Bad and unwanted Records’
Writing Joins in MapReduce with 2 Input files. Join Types
Execute MapReduce Job – Insights
Exercise’s n MapReduce
Hive concepts
Hive architecture
Install and configure hive and cluster
Different type of tables in hive
Hive library functions
Joins in hive
Inner joins
Outer Joins
Hive UDF
Hive Query Language
Pig basics
Install and configure of PIG
PIG Library functions
Pig Vs Hive
Write sample Pig Latin scripts
Modes of running PIG
Running in Grunt shell
Running as Java program
Install and configure Sql
Connecting t RDBMS
Installing MySQL
Import data from MySQL to hive
Export data t MySQL
Internal mechanism of import/export
HBase concept’s
HBase architecture
Regin server architecture
File storage architecture
HBase basics
Column access
HBase use cases
Install and configure HBase and a multi node cluster
Create database, Develop and run sample applications
Access data stored in HBase using Java API
Map Reduce client to access the HBase data
Resurce Manager (RM)
Node Manager (NM)
Application Master (AM)

Module 11: Capstone Project

10 Hours

Languages and Tools covered

and more…

Capstone Projects​

Data sets from the industry

100 +
Project Completed
22 +
100 +
Papers published at IIMB


Learn from leading academicians in the field of data science and business analytics and several experienced industry practitioners from top organizations.

22 +
22 +
Industry Mentors
100 +
Award winning faculties

Program Fees

PG Program in Data Science and Engineering

Rs. 80,000 + GST

Post Graduate Certificate from
  • Personalised weekly online mentorship sessions
  • 9-month Program
  • 129+ hours of online learning content
  • Dedicated career support through interview workshops and 1:1 mentorship
  • Access to  IIBM Institute – curated jobs portal and exclusive career fairs
  • Individual doubt-solving with expert mentors
  • 13 real-world projects guided by industry experts
  • IIBM Institute- Industry and Peer Networking Events


Candidates can pay the course fee through Netbanking, Credit/Debit cards, Cheque or DD. Also, with our corporate financial partnerships avail education loans at 0% interest rate*.

Application Process

Step 1 : Fill the application form


Apply by filling a simple online application form

Step 2 : Interview Process


Go through a screening call with the Admission Director’s office.


Step 3 : Join Program 


 An offer letter will be rolled out to the select few candidates. Secure your seat by paying the admission fee.

Upcoming Application Deadline

Our admissions close once the requisite number of participants enroll for the upcoming batch . Apply early to secure your seats.

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