Executive Certification in Advanced Data Science & Applications

IITM Pravartak
Technology innovation hub of IIT Madras

Course Duration
10 Months
Hybrid (Online)
Commencement Date
28th April 2024
Application Closure Date
Closing Soon
Session Timings
Sunday, 1:00 pm to 4:00 pm

Programme Overview

Data science techniques and associated methods in artificial intelligence and machine learning have now been at the forefront of the revolution in various traditional fields. Consequently, an increasing number of professionals in scientific computing, software engineering and development. Businesses are looking to increase their understanding of the fundamental techniques and ideas driving this field. The current programme aims to empower professionals to move to the forefront of this revolution with the objective to:
  • Provide a thorough introduction to the various methods in the field of Artificial Intelligence, Deep Learning, Data Analytics, and its mathematical foundations
  • Provide strong hands on experience in both the mathematical and computational aspects of Deep Learning
  • Case studies through applications of the techniques to realistic data from various business verticals
Amplify your skills using Data science, AI & ML fundamental techniques to navigate growth trajectories. Learn about the essential methods and concepts that drive ideas in growing fields like scientific computing, software engineering, development, etc. IITM Pravartak’s Executive Certification in Advanced Data Science & Applications aims to give you contextual know-how using case studies from various business verticals. This interdisciplinary programme has intensive self-study applications using varied techniques to real-life data from multiple business verticals. The curriculum strives to empower professionals with the fundamental methods and tools they need to move at the forefront of the AI revolution.

Programme Highlights


Highly recognised Certificate of Completion from IITM Pravartak


2 days of campus immersion

Industry specified case studies


Peer-to-peer learning and mentoring from industry experts


The live programme is entirely taught by IIT Madras faculty


Pedagogy filled with case studies, industry projects & practical application

Admission Criteria

Selections will be based on a detailed Profile of the Candidate in his own words elaborating his Academic record, Profile, Designation, Salary, Roles, Responsibilities, Job Description, and a write-up on ”Expectations from the Programme”.

Eligibility & Selection

  • Qualification: Graduate/4-year Engineering Degree/B.Sc+M.Sc from a recognised university (UGC/AICTE/DEC/AIU/State Government/recognised international universities).
  • Minimum Experience: 3 years preferably in software engineering and/or other disciplines involved in computational work.
  • Must be comfortable with basic mathematics.

Syllabus Breakdown

Overview of the Course

  • Overview of AI and ML
  • The Mathematics required for AI and ML

Linear Algebra for AI

  • Linear equations and solutions
  • Vectors, Matrices and their Properties
  • Inner Products and Norms
  • Projections
  • Eigenvalues and Eigenvectors
  • Singular Value Decomposition

Probability and Statistics for AI

  • Probability theory and axioms
  • Random variables
  • Probability distributions and density functions
  • Expectations and moments
  • Covariance and correlation
  • Hypothesis testing
  • MLE, MAP and Bayesian methods

Optimization for Data Science

  • Multivariable Calculus
  • Unconstrained optimization
  • Introduction to least squares optimization
  • Gradient based methods
Introduction to Python Programming
  • Overview Python
  • Setting up Python environment
Basics of Python
  • Variables, Data Types
  • Control flow (if-else statements, loops)
  • Functions and Modules
Data Structures in Python
  • List, Tuples
  • Dictionaries, Sets
  • Classes, Objects and Methods
Scientific computation with Python and its Libraries
  • NumPy
  • Pandas
  • Scikit-learn library for ML
  • Matplotlib for plotting and visualizations
Python for Deep Learning
  • PyTorch
  • TensorFlow and Keras
  • Foundations of Machine Learning – The Machine Learning Paradigm
  • Linear and Polynomial Regression
  • K-Nearest Neighbors
  • Linear Classification – Logistic Regression
  • Bias Variance tradeoff,Regularization
  • Evaluation methods
  • Recap of Linear and Logistic Regression
  • Multiclass Classification
  • Artificial Neural Networks
    • Artificial Neuron
    • Multilayer Perceptron
    • Universal Approximation Theorem
    • Backpropagation in MLPs
    • Backprop on general graphs
  • Optimization in Neural Networks
    • Gradient Descent and its Variants
    • Momentum, Adam, etc.
    • Batch Normalization and other techniques in modern Deep Learning
  • Basics of Hyper parameter optimization
  • Convolutional Neural Networks (CNN)
    • Introduction
    • CNN Operations
    • CNN Training
    • Image Recognition-SoTA model(s)
    • Object detection/localization - SoTA model(s)
    • Semantic segmentation - SoTA model(s)
  • Sequence Analysis Models
    • Recurrent Neural Networks (RNNs)
    • Long Short Term Memory Networks (LSTMs)
  • Introduction to Generative Models and their role in Modern AI
  • Generative Adversarial Networks (GANs)
  • Diffusion Models for image generation
  • Transformer Architectures
  • Large Language Models (such as ChatGPT)
    • Encoder only models
    • Decoder only models
  • Applications of existing Generative AI models
    • Leveraging tools such as ChatGPT in your context
  • Future Trends in Generative AI