Apply Before 31st July Review to Qualify for a ₹73,750 Scholarship!

PG Level Advanced Certification Programme in Applied Data Science and Machine Learning

IITM Pravartak’s Data Science and Machine Learning Course

is a comprehensive program designed to equip mid-to-senior
professionals with in-demand expertise in the field.

Talentsprint

Apply Now

Coding Skill
is mandatory

  • 100% Live Online Classes
    Taught by 12 IITM Faculty
  • 12 Months 250+ hours of
    in-depth learning
  • 2 daysof Campus Visit
    at IIT Madras
  • 30+ ToolsHands on learning
    and Capstone projects

3 Reasons Why This Programme is Unique

1 The IITM Pravartak Advantage

  • Designed by WSAI at IIT Madras, India’s #1 ranked institution by NIRF in Engineering
  • Taught by 12 IIT Madras faculties via 100% Live Interactive Classes
  • Certification from IITM Pravartak
    and WSAI at IIT Madras

2Cutting-edge Applied Learning

  • Hands-on Learning with 30 mini projects, 30+ tools assignments and a capstone project
  • Visit IITM Campus and practise at WSAI, India's top Applied Research Lab
  • Industry interaction with
    experienced professionals

3 The TalentSprint Advantage

  • Learn on TalentSprint’s patent-pending Digital Platform
  • 250k+ professionals
    empowered till date
  • Get Dedicated support for
    enhanced career outcomes

Limited Scholarship Seats

Next Selection Review : 31st July

+91-8977111590 / +91-8341144105

Data Science and Machine Learning
Course Curriculum

Linear equations and solutions Matrices and their Properties; Eigenvalues and eigenvectors; Matrix Factorizations; Inner products; Distance measures; Projections; Notion of hyperplanes; halfplanes.

Probability theory and axioms; Random variables; Probability distributions and density functions ;Expectations and moments; Covariance and correlation; Statistics and sampling distributions; Hypothesis testing of means, proportions, variances and correlations; Confidence intervals; Correlation functions; Parameter estimation – MLE and Bayesian methods

Unconstrained optimization; Necessary and Sufficiency conditions for optima; Gradient descent methods; constrained optimization, KKTConditions; Introduction to least squares optimization;

Learning Outcomes: By completing this module, you will gain a solid foundation in Python programming and key concepts in probability and statistics for data-driven decisions. You'll also learn basic calculus to understand change and trends, along with optimization techniques to solve real-world problems and enhance performance.

  • Bayesian decision theory, K-Nearest Neighbors
  • Linear Regression, Ridge, LASSO
  • Linear Classification (Logistic Regression, Linear Discriminant Analysis)
  • Recap K-NN; Bias Variance tradeoff, cross-validation/ model selection
  • Evaluation methods (ROC, AUC, F-measure, etc.)
  • Naive Bayes, Decision tree
  • Ensemble Methods: Bagging, Random Forest, Gradient
  • Perceptron, Intro to Support Vector Machines
  • Clustering motivation, K-means/Hierarchical, GMM
  • Dimensionality reduction, Association Rule mining

Learning Outcomes: In this module, you’ll explore core machine learning algorithms like linear regression, decision trees, and k-nearest neighbors. You’ll learn to train, evaluate, and apply these models effectively, and develop the skill to choose the right algorithm based on the problem and data.

1. Use cases from the healthcare domain where NLP is applied

  • a. Automatic case-correction of all-caps or all-small text from EMRs.
  • b. Automatic token splitting of conjoined words and sentences.
  • c. NER on EHRs
  • d. Table detection and extraction of EOBs and EHRs.
  • e. Computer-assisted medical coding of EHRs.

2. Models such as Bi-LSTM-CRF, CAML, HAN, ResNexT.

3. Public domain datasets - MIMIC-III.

  • Introduction to big data in biology
  • Levels of omics data, basic information flow in biology
  • Importance of Networks in Biology: Overview
  • Introduction to Network Science
  • Learning from Network structure: Predicting essential genes
  • Learning on Networks: Community detection to identify disease genes - Learning using Networks: Graph mining for predicting biosynthesis routes - Omic data analysis: Predicting mutations and genes that drive cancer

  • Problem Statement: Four case studies will be demonstrated.
    1. CS1: Choice of mode
    2. CS2: Travel time estimation
    3. CS3: Accident hot spot analysis
    4. CS4: Accident severity modelling
  • Model(s) intended to demonstrate : Logistics regression, Support vector regression, k-means clustering and random forest
  • Dataset to be used during the demo
  • Dataset for the mini project

1. Levels of omics data, basic information flow in biology

2. Genomics, Transcriptomics, Epigenomics, Proteomics and Multi omics - Identification human disease genes using genomics

3. Application of transcriptomics for identifying disease mechanisms

4. Clinical data - kinds of clinical data Garbhini dataset - a clinical data case study

Learning Outcomes: In this module, you'll explore how machine learning drives innovation in industries like healthcare, finance, and marketing. You'll learn to analyze real-world use cases, understand the ML workflow, and identify problems where ML can create practical impact.

a. Artificial Neuron

b. Multilayer Perceptron

c. Universal Approximation Theorem

d. Backpropagation in MLPs

e. Backprop on general graphs

a. Gradient Descent and its variants

b. Momentum, Adam, etc.

c. Batch Normalization

a. Introduction

b. CNN Operations

c. CNN Training

d. Illustrative Example (“Hello World”) - MNIST digit classification e. Image Recognition-SoTA model(s)

f. Object detection/localization - SoTA model(s)

g. Semantic segmentation -SoTA model(s)

Learning Outcomes: At the end of this module you will gain knowledge about foundation in deep learning, from understanding neural networks to building and training models using TensorFlow or PyTorch. Learn to apply these techniques to real-world tasks like image classification, NLP, and explore their role in powering Generative AI.

a. Smart Cities

b. Industry Use case 1

c. Climate Science

d. Manufacturing

e. Bio-informatics

Industry Use case 2

Learning Outcomes: At the end of this module, you'll understand how deep learning powers real-world applications like image recognition, speech processing, and recommendation systems. You'll also learn to select the right architectures for different problems and tackle key deployment challenges.

  • Recommendation Systems
  • Object Recognition System
  • Digit Recognition System
  • Financial Fraud Detection System
  • Anomaly Detection in Manufacturing Systems
  • Urban Infrastructure Analytics
  • Healthcare Analytics
  • And more

Tools covered

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Faculty

Dr. Balaji
Srinivasan



Ph.D, Stanford University, USA

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Research Expertise : Fluid Dynamics, Turbulence in compressible and hypersonic flows, Computation of rarefied flows, Numerical Analysis and High-Performance Computing.

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Dr. Balaraman
Ravindran



Ph.D., Computer Science, University of Massachusetts, Amherst, USA

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Research Expertise : Reinforcement Learning, Geometric Deep Learning, Data Mining

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Dr. Nandan
Sudarsanam



Ph.D., Engineering Systems, MIT, Cambridge, MA, USA

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Research Expertise : Experimentation, Applied Statistics and Machine Learning

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Dr. Nirav P.
Bhatt



Ph.D., Computer, Communication and Information Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland

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Research Expertise : Machine Learning for Biological and Engineering Networks, Safe Reinforcement Learning

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Dr. Karthik
Raman



Ph. D., Systems Biology, IISc

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Research Expertise : Biological Networks, Computational Systems Biology, Genomics and Computational Biology

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Dr. Gitakrishnan
Ramadurai



Ph.D. ,Transportation Engineering, Rensselaer Polytechnic Institute, Troy, NY

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Research Expertise : Network Modeling, Dynamic Traffic Simulation and Assignment...

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Prof. Himanshu
Sinha



Ph.D., Cambridge University, UK

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Research Expertise : System Genetics, Multiomic Analysis of Genotype & Phenotype Relationship, Healthcare Data Science & AI

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Dr. Arun
Rajkumar



Ph.D., IISc Bangalore

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Research Expertise : Machine Learning, Rank Aggregation, Statistical Learning

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Dr. Chandrashekar
Lakshminarayanan



Ph.d., IISc Bangalore

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Research Expertise : Deep Learning, Reinforcement Learning, Stochastic Approximation and Large Scale Markov Decision Processes

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Dr. Harish G.
Ramaswamy



Ph.D, IISc Bangalore

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Research Expertise : Machine Learning, Learning Theory and Optimisation

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Prof. Ganapathy
Krishnamurthi



Ph.D., Purdue University, USA

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Research Expertise : Multi-Modal Pre-Clinical Imaging System, Medical Image Processing

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What Sets This Programme Apart

Features ADSML Programme by IITM Pravartak Other Data Science Courses
Duration 12 Months of executive friendly programme Short term or self paced format
Curriculum Practical curriculum with strong industry exposure designed by IIT Madras Faculties Lacks Industry alignment with no scope for practical exposure
Faculty Taught by 12+ distinguished faculty having 300+ years of collective experience Only one faculty throughout that too for few masterclasses
Tools Exposure Get access to 30+ most in demand tools and libraries like Phyton, Tensorflow, Fast API, Microsoft Azure and many more Limited tools exposure
Project Based Learning 30 Mini Projects, assignment
& 1 Capstone Project
No real world project exposure
Format 100% Live Interactive Sessions
100% Live engagement with faculty
Mostly pre-recorded videos with few live classes
Minimal engagement from faculty
Campus Visit 2 days of campus visit at IIT Madras Campus visits are optional and limited to a single day
Certification Certification from IITM Pravartak and WSAI at IIT Madras Certification from institutions with limited recognition
Alumni Network Become part of growing ADSML alumni network & 250K TalentSprint network No Alumni or community support
Fee Priced for 12 months of learning, offering much greater value Shorter courses often come at a higher cost

High Impact Learning Format

  • 12 Months Executive Friendly Programme
  • 250+ Hours of immersive learning
  • 100% live interactive classes
  • 12 IIT Madras faculties teaching the programme
  • 2 days of campus visit at IIT Madras
  • 30 Mini projects, assignments and a capstone project
  • Industry interactions and mentor supports

**Dates will be decided keeping the safety of participants in mind. Fees will be based on actuals.


Eligibility

  • Education: B.E./M.E./B.Tech/M.Tech/B.Sc./M.Sc or an equivalent degree
  • Work Experience: Minimum 1 Year
  • Coding Experience: Basic Programming Knowledge Required

Find Out Why Professionals Want To Join The Programme

Note: These are edited versions based on the details submitted by various programme applicants.

Industries Adopting
Data Science at Scale

  • Healthcare
  • Banking and Finance
  • Retail & E-commerce
  • Manufacturing
  • Telecom & Media
  • Energy & Utilities
  • Public Sector
  • Data Science will create 11.5 Million
    job openings by 2026.

Participant Profile

Experience

Top Organizations

All logos belongs to respective companies

Enrolment Process

  • Apply for the Programme
  • Wait for
    Selection
  • Block Your
    Seat
  • Enroll for the Programme
  • Start Building Expertise
  • Get Certified by IITM Pravartak

The selection for the programme will be done by IITM Pravartak and WSAI at IIT Madras and is strictly based on the eligibility criteria
and the motivation of applicants as expressed in their statement of purpose.

Program Fee

Application Fee*
₹2,000

Program Fee* ₹2,50,000

Programme Fee with Scholarship* ₹1,87,500

(*18% GST as applicable)

Apply Now

*Fees paid is non-refundable and non-transferable.

Campus visit fee to be borne by participants. Will be based on actuals.

Special Program Fee for Corporate Nominations**

**Applicable only for enterprises nominating their employees as a group

Modes of payment available

  • Internet Banking
  • Credit/Debit Card
  • UPI Payments

Easy Financing Options

EMI as low as ₹7,943/Month

EMI Options


Talk to Us

Glimpses from IIT Madras Campus

About IIT Madras

Indian Institute of Technology Madras (IIT Madras) is globally recognized for excellence in technical education, basic and applied research, innovation, entrepreneurship and industrial consultancy. Founded in 1959 with technical and financial assistance from the former government of West Germany, IIT Madras has been the top-ranked engineering institute in India for four consecutive years as well as the ‘Best Educational Institution’ in Overall Category in the NIRF Rankings by the Ministry of Human Resource Development. For more information visit www.iitm.ac.in

About IITM Pravartak

IITM Pravartak Technologies Foundation, a Section 8 company hosted by IIT Madras and funded by the Department of Science and Technology, Government of India, drives innovation through the Technology Innovation Hub on Sensors, Networking, Actuators, and Control Systems (SNACS) under the National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS). Focused on advancing Cyber-Physical Systems (CPS), which integrate digital and physical elements for data-driven decision-making, IITM Pravartak fosters cutting-edge research, technology development, entrepreneurship, and human resource development while collaborating with industry, academia, government, and international organizations to translate research into impactful products.

  • #5 World University Ranking (India)
  • #1 in Engineering in India, NIRF Rankings 2024
  • #1 Atal Rankings of Institutions on Innovation Achievements, GoI

About TalentSprint

Data Science and Machine Learning
Course Overview

The 12-month online PG Level Advanced Certification Programme in Applied Data Science and Machine Learning, offered by IITM Pravartak and Wadhwani School of Data Science & AI (WSAI) at IIT Madras in collaboration with TalentSprint, is a cutting-edge course designed to enable learners to build deep tech capabilities and make data-driven business decisions. With the massive amount of data generated daily from millions of devices, Applied Data Science has become a crucial field in today's world. The program offers a unique learning experience that combines masterclass lectures, hands-on labs, hackathons, workshops, industry interactions, and a campus visit to fast-track learning.

The Data Science course covers various topics such as data preprocessing, machine learning algorithms, deep learning models, and natural language processing, among others, to prepare learners to handle complex data sets and provide data-driven solutions. The curriculum is designed to meet industry standards and includes real-world case studies, ensuring learners have hands-on experience working with industry-relevant tools and technologies. The course also enables learners to acquire soft skills such as communication, teamwork, and leadership, which are essential for success in the workplace.

Participants of the course benefit from the expertise of leading faculty members and industry experts, who provide personalized guidance and mentorship throughout the course. Upon completion of the course, learners receive a PG Level Advanced Certification in Applied Data Science and Machine Learning from IITM Pravartak and WSAI (IIT Madras), which enhances their career prospects and enables them to become sought-after professionals in the data science and machine learning field.

Frequently Asked Questions

It is well known that Data Science helps businesses extract actionable insights from massive sets of data. Data Science and the technologies empowering it, like AI and Machine Learning, have become central to every business strategy today.

However, Applied Data Science takes the game many notches higher. It broadens the scope of data science to include

  • Finding new applications where data science can be applied and
  • Create predictions that are more accurate in their trends and seasonality

Applied Data Science becomes important in the backdrop of the fact that the global data created per day is likely to reach 394 zettabytes per day by 2028. Such massive data cannot be made sense of by traditional algorithms.

This is where Machine Intelligence (MI) can add immense value to business in conjunction with Applied Data Science.

Machine Intelligence as a higher evolution of machine learning - a stepping stone to true AI.

According to LinkedIn, Applied Data Science and Machine Learning offers exciting job opportunities for professionals with expertise in these fields.

  • Data Scientist (7000+ openings)
  • Data Science Manager (2000+ openings)
  • Machine Learning Engineer (24,000+ openings)
  • Data Engineer (29,000+ openings)
  • AI/ML Developer (4000+ openings)
  • AI Programmer (8000+ openings)
  • Junior Data Scientist (1000+ openings)
  • Junior Data Science Engineer (1000+ openings)
  • Data Analyst (10,000+ openings)
  • Applied AI/ML Analyst (600+ openings)
  • Backend AI Engineer (980+ openings)

The IITM Pravartak Advantage

  • Designed by Wadhwani School of Data Science and AI (WSAI) at IIT Madras
  • Taught by top researchers in Applied Data Science and Machine Learning
  • Taught by top researchers and faculty members of IIT Madras

Cutting-edge Applied Learning

  • Hands-on curriculum with use cases, capstones for effective learning
  • Visit the IIT Madras campus* and practise at WSAI, India’s top Applied Research Lab

The TalentSprint Advantage

  • Learn on TalentSprint’s patent-pending Digital Platform
  • Network with 10000+ TalentSprint Deep Tech Alumni
  • Get Dedicated Support for Enhanced Career Outcomes

The programme will be delivered in an interactive online format, retaining effectiveness while maintaining safety. The format uniquely combines the benefits of an in-class programme with the flexibility and safety of online learning.

  • Synchronous programme delivery by expert faculty
  • Interact and get your doubts answered by faculty
  • Peer learning through working in groups with other participants
  • Get support from mentors for labs and experiments
  • Re-attend classes through recorded archives
  • Access videos easily with searchable and indexed video archives
  • Learn from the comfort and safety of your home
  • Office hours with one-on-one mentor support

The programme will be delivered on TalentSprint's iPearl.ai, a leading digital learning platform of choice used for programs delivered by the likes of Google, IIM Calcutta, IIT Hyderabad, IISc, and IIT Kanpur, to name a few.