PG Program in Data Science
& Machine Learning

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 60,000 + 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 & Machine Learning

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 (1 Hour)
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 (1 Hour)
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
eature inmportance
Project 4 (1 Hour)
Understanding Decision Tree
Building Decision Tree
Using ID3 / Entropy
CART model – Gini index
Stopping Criteria And Pruning
Hyperparameter Tunning for Decision Tree
Project 4 (1 Hour)
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 (1 Hour)

Module 7: Unsupervised Learning

9 Hours

Clustering Methods
Agglomerative Clustering
Divisive Clustering
Project 8 (1 Hour)
Basics of KMeans
Finding value of optimal K
Elbow Method
Silhouette Method
Project 9 (1 Hour)
Eigenvalues and Eigenvectors
Orthogonal Transformation
Using PCA
Project 10 (1 Hour)

Module 8: Machine Learning with Python

25 Hours

Data Preparation
Stationary Data
Trends /Seasonility
SARIMA & Other Models
Project 11 (1 Hour)
Deep Learning and Neural Networks (2 Hours)
Deep Learning Fundamentals
Working of Neural Networks
Gradient Descent and Back Propagation
Activation Function
TensorFlow Introduction (1 Hours)

Building Artificial Neural Networks (ANN) with Python

Project 5

Introduction to OpenCV
Learning Basic Image manipulations Introduction to CNN
CNN Institution
Convolution Operation Pooling, Flattening Building a CNN using Python
Project 6
Introduction to RNN
Sequence prediction using RNN
Introduction to LSTM
Sequence prediction using LSTM
Applications in text analytics , stock prediction , time series data
Project 7
Deploying an Scikit-learn model [train, deploy]
Deploying a Keras model [train, deploy]
Autoencoders and Generative Adversarial Networks

Module 9: Tableau

10 Hours

creating views, visual analysis, mapping , generate trends

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

Craeating Interactive Dashboards, Adding views and objects to dashboards

Project 13

Module 10: Introduction to Database

6 Hours

What is Database
Introduction to MySQL and NoSQL
DDL v/s DML v/s DCL v/s TCL
Datatypes in SQL
Basic SQL statements (SELECT, DELETE and UPDATE)
How to convert data into tables
COMMIT and ROLLBACK statements

Filter Data using the WHERE and ORDER BY Clause

Using of Filtering Operators – IN, NOT IN, IS NULL, BETWEEN

Introduction to Joins and its types

Introduction to Grouping
Single-Row, Multiple-Row Subqueries
Subqueries with ANY and ALL Operators
Conditional Expressions using CASE Clause

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 & Machine Learning

Rs. 60,000 + GST

Post Graduate Certificate from
  • Personalised weekly online mentorship sessions
  • 9-Month Program
  • 118+ hours of online learning content
  • Dedicated career support through interview workshops and 1:1 mentorship
  • Access to  IIBM eInstitute – curated jobs portal and exclusive career fairs
  • Individual doubt-solving with expert mentors
  • 16 real-world projects guided by industry experts
  • Access to 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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Contact Us

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hours. You can also reach out to us at or +91-9808811409