PG Program in Data Science
& Artificial Intelligence

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 65,000 + GST

Batch start: 15th August

Enquire: +91-9808811409 

social-media-free-img

Why we’re ranked #1

Comprehensive
Curriculum

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

Personalised
Mentorship

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

Dedicated Career
Assistance

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

Join India's # 1Data Science & Artificial Intelligence 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?

Regression

Classification

Data Science Life Cycle

Data Science in cloud

Univariate

Bivariate

Multivariate

Missing Values

Outliers treatment

imbalance Data Handeling

Standardization / Normalization 

Project1

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
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
Bagging
Boosting
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
Dendogram
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: Artificial intelligence with Python

30 Hours

Basics of NLP
Removing Stop Words
Stemming & lemmatization
Parts of speech tagging
TFIDF vectorizer
Senmiment Analysis
Text Classification with Linear Models
Language Modelling with Probabilistic Graphical Models and Neural Networks
Word Embeddings and Topic Models
Machine Translation and Sequence-To-Sequence Models
Project: Building a Chatbot
Basics of Bayesian probability theory
Expectation-Maximization algorithm and Gaussian Mixture Models
Variational Inference and Latent Dirichlet Allocation
Markov Chain Monte Carlo
Bayesian Neural Networks and Variational Autoencoders
Gaussian Processes and Bayesian Optimization
Project: Forensics to generate images of suspects
Introduction to Reinforcement Learning
Model-Based Reinforcement Learning (Dynamic Programming)
Model-Free Reinforcement Learning (SARSA, Monte Carlo, Q-Learning)
Approximate and Deep Reinforcement Learning (Deep Q-Learning)
Policy Gradient Reinforcement Learning
Advanced Topics on Exploration and Planning
Project

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
Using GROUP BY & HAVING

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 +
Domains
100 +
Papers published at IIMB

Faculty

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

22 +
Professors
22 +
Industry Mentors
100 +
Award winning faculties

Program Fees

PG Program in Data Science & Artificial Intelligence

Rs. 65,000 + GST

Post Graduate Certificate from
  • Personalised weekly online mentorship sessions
  • 9-month Program
  • 123+ 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.

Still have queries?
Contact Us

Please fill in the form and an expert from the
admissions office will call you in the next 4 working
hours. You can also reach out to us at
 pgpds@iibmindia.in or +91-9808811409