Executive Certification in Advanced Data Science & Applications

IITM Pravartak
Technology innovation hub of IIT Madras

Course Duration
10 Months
Application Closure Date
March 31, 2023
Programme End Date
December 2023
Hybrid (Online)

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 data analytics, machine learning & artificial intelligence and its mathematical foundations
  • Provide contextual understanding using case studies from various business verticals
  • Self-study by applications of the various techniques to real-life data from multiple 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


3 days of Campus immersion at the end of the programme

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.

Syllabus Breakdown

  • Overview of Course
  • Introduction to Data Science
      • Definition
      • Application areas
      •  Past, Present and Future
  • The role of computation in Data science
  • The role of mathematics in Data science
  • Python
      • Install and run Python, use IDE
      • Basic control structures
      • Importing and Manipulating data, tools for plotting
  • R
      • Getting started with R
      • Basics, control structures
      • Functions
      • Importing and manipulating data
  • SQL
      • Getting started with MySQL
      • Creating, inserting, retrieving records
      • Searching
      • Interfacing with Python and R
This module covers the fundamental mathematical and statistical tools required for data science. All of the techniques will be discussed in the context of data science with relevant examples.
  • Probability and Statistics
      • Probability and counting, Bayes Theorem
      • Independence, Conditional Probability, Marginal Probability
      • Random Variables, Probability Distributions
      • Expectation, Variance, Covariance
      • Descriptive Statistics
      • Statistical Estimation
      • Hypothesis Testing
      • Predictive and Prescriptive Analytics
      • Practical Considerations in Data Science
  • Linear Algebra
      • Vectors and their operations
      • Matrices and their operations
      • Inner Products and norms
      • Matrix Decomposition – Eigenvalues, SVD
      • Applications
  • Calculus and Optimisation
    • Partial Derivatives, Multivariable Calculus
    • Gradient, Jacobian
    • Automatic Differentiation
    • Constrained and Unconstrained Optimization
    • Gradient Descent and its variants
  • Introduction to Machine Learning (3 hours) - From Data Science to Machine Learning, Learning paradigm, Components of a machine learning algorithm, Bias-Variance tradeoff, Model selection, Hyperparameters, Regularisation
  • Supervised Learning (15 Hours)- Definition of supervised learning, data requirements, types of supervised learning problems, algorithms for regression and classification using supervised learning
      • Linear Regression
      • Logistic Regression
      • Multiclass Classification
      • KNN
      • Decision tree
      • Random Forest
      • Support Vector Machines
      • Naïve Bayes
  • Unsupervised Learning (7 Hours)- Definition of unsupervised learning, scenarios for unsupervised learning, types of data, examples, algorithms for unsupervised learning
      • Agglomerative clustering
      • K-Means
      • Gaussian Mixture Models
      • Introduction to Generative Models
  • Deep Learning (35 Hours)- Artificial Neural networks and its evolution, Backpropagation algorithm, modern applications including text & speech analysis, computer vision and natural language processing using deep learning. State of the art Deep Learning techniques in –
      • Deep Neural Networks (DNN)
      • Convolutional Neural Networks (CNN)
      • Recurrent Neural Networks (RNN)
      • Transformers
      • Generative Adversarial Networks (GANs)

The techniques discussed in the previous 4 modules will be applied to problems in various domains here. The students can visit the campus to learn in person from domain experts. 
Practical applications will be selected from:

  • Business Intelligence
  • Business Analytics
  • Sectorial Analytics (Marketing/ Finance/ Operations/ Supply Chain/ HRM)
  • Computer Vision
  • Language Modelling
  • Applications in Engineering
  • Healthcare
  • Decision Making
  • Project (In candidate's own time - 30 hours)
Online Live Sessions
120 hrs of interactive learning
Real life Case Studies
madras logo

About IITM Pravartak

IITM Pravartak Technologies Foundation is a section 08 Company housing the Technology Innovation Hub on Sensors, Networking, Actuators, and Control Systems (SNACS). IITM Pravartak is funded by the Department of Science and Technology, Government of India, under its National Mission on Interdisciplinary Cyber-Physical Systems and hosted as a Technology Innovation Hub (TIH) by IIT Madras. The IITM Pravartak Technology Innovation Hub aims to focus on new knowledge in the SNACS area through extensive and application-oriented research. IITM-PTF gladly takes the role of preparing young India for the next generation of world-class technologies. The NM-ICPS is a comprehensive Mission aimed at complete convergence with all stakeholders by establishing strong linkages between academia, industry, Government, and International Organisations.

Know the Facilitators

Programme Certification

Participants who successfully meet the evaluation criteria and satisfy the requisite attendance criteria will be awarded a ‘Certification of Completion’ – Executive Certification in Advanced Data Science & Applications”.

Note: The sample certificate is indicative. The Institute reserves the right to revise it.

IIT Pravartak_Jaro_Certificate_new

Fee Structure

Programme fee
Fee Structure
Application Fee
INR 1,500/- + GST
Programme Fee
INR 2,00,000/- + GST
Easy EMI options available
Installment Pattern
Installment Pattern
1st Instalment

7 days from the date of offer release (latest by 5th April 2023)

INR 80,000/-

2nd Installment

15th May 2023

INR 60,000/-

3rd Instalment

15th August 2023

INR 60,000/-

  • Note: *All the amounts mentioned above are exclusive of GST.

Admission Process

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The Jaro Advantage

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Profile Building

Rigorously building the candidate’s profiles and resume scrutinizing their LinkedIn profiles. Jaro Education enables personalised feedback to boost overall virtual presence.

Resume Review

Moving forward with carefully curated resume reviews that ensures you are interview-ready for the workplace of tomorrow.

Placement Assistance

Get career assistance as per the profile and preferences. On average, get 5-6 job recommendations to enhance quality employment opportunities.

Career Enhancement Sessions

Bridging connectivity to link the best talent with organizations through eminent sessions from top-class industry speakers.


Note: IITM Pravartak or Jaro Education do not guarantee or promise you a job or advancement in your existing position. Career Services is simply provided as a service to help you manage your career in a proactive manner. Jaro Education provides the career services described here. IITM Pravartak is not involved in any way with the career services described above and offer no commitments.

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Frequently Asked Questions
What’s the total fee for Executive Certification in Advanced Data Science & Applications by IIMT Pravartak?

 The total programme fee is INR  2,00,000/- + GST.

Describe the projected career growth after this programme.
  • The applicant will be able to use the methods taught in this case to interface with other teams and incorporate them into his or her business case. Additionally, it will assist candidates in transitioning into careers in machine learning and data analytics. 
  • Middle-level executives can benefit by understanding the strength and limitations of these methods in their business case.
What are the objectives of IIMT Pravartak Executive Certification in Advanced Data Science & Applications?
  • The core objectives of this interdisciplinary programme is to:
    • Give a thorough introduction to the myriad of methods in data analytics, machine learning, and artificial intelligence, as well as their mathematical foundations.
    • Case studies from various business verticals are used to provide contextual understanding.
    • Self-study through the application of various techniques to real-world data from various business verticals.
What kind of tools are taught in this programme?
  • Acquire exposure to new age tools and techniques such as:
    • AL
    • ML
    • Python
    • R
    • Matlab
    • SQL
    • Pytorch
    • TensorFlow
Specify the assessment methodology for the programme?
  • The assessment is divided with Homework, Final exam, project wherein:
    • Final Certifying examination with grades ranging from A to F, with A as highest grade F indicating Failure.
    • 50% weightage to homework/case studies and 50% to the final exam.
    • Attendance: 70% attendance is mandatory.

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