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
The PG Level Advanced Certification Programme in Applied Data Science and Machine Learning course will be delivered by the Robert Bosch Centre for Data Science and AI (RBCDSAI), one of India's pre-eminent interdisciplinary research centres for Data Science and AI with the largest network analytics, deep reinforcement learning, and the most active natural language processing and deep learning groups. The Centre was started to expand AI adoption in engineering applications and leverage the available expertise on network systems modelling across various Institute departments. The programme Certification will be awarded by the Centre for Continuing Education (CCE), IIT Madras that is committed to helping build national capabilities in science, technology, humanities, management, education and research.
The 12-month online PG Level Advanced Certification Programme in Applied Data Science and Machine Learning, offered in partnership with the Robert Bosch Centre for Data Science and AI (RBCDSAI) at IIT Madras, 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 IIT Madras, which enhances their career prospects and enables them to become sought-after professionals in the data science and machine learning field.
The programme is designed by a distinguished faculty group bearing academic accreditation from premier institutions around the world.
Ph.D., Computer Science and Automation, IISc
Machine Learning, Rank Aggregation, Statistical Learning, Sequential Decision Making
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Ph.D., Computer Science, University of Massachusetts, Amherst, USA
Reinforcement Learning, Geometric Deep Learning, Data Mining
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Ph.D., Engineering Systems, MIT, Cambridge, MA, USA
Experimentation, Applied Statistics and Machine Learning
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Ph.D., Computer, Communication and Information Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
Machine Learning for Biological and Engineering Networks, Safe Reinforcement Learning
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Ph. D., Systems Biology, IISc
Biological Networks, Computational Systems Biology, Genomics and Computational Biology
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Ph.D., Transportation Engineering, Rensselaer Polytechnic Institute, NY, USA
Dynamic Traffic Assignment, Transportation Network Modelling, Intelligent Transportation Systems, Pedestrian Safety
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One time application fee is ₹2,000
| Details | Amount |
|---|---|
| Program Fee* | ₹2,50,000/- |
EMI Facility Available 
Special Pricing for Corporates *GST as applicable
Campus visit fee to be borne by participants. Will be based on actuals.
Fee is non-refundable and non-transferable.
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
Applied Data Science becomes important in the backdrop of the fact that the global data created per day is likely to reach 463 Billion GB/Per Day by 2025 from 44 Billion GB/Per Day in 2016 according to IDC. 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.
The IIT Madras Advantage
Cutting-edge Applied Learning
The TalentSprint Advantage
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.
The programme will be delivered on TalentSprint's patent-pending 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.
Established in 2010, TalentSprint is a part of NSE group and a global edtech company that brings transformational high-end and deep-tech learning programs to young and experienced professionals. The company’s digital learning platform ipearl.ai offers a hybrid onsite/online experience to seekers of deep technology expertise. TalentSprint partners with top academic institutions and global corporations to create and deliver world class programs, certifications, and outcomes.Its programs have consistently seen a high engagement rate and customer delight. It is a leading Innovation Partner for the National Skill Development Corporation, an arm of the Ministry of Skill Development and Entrepreneurship, Government of India. A recipient of various prestigious accolades, TalentSprint was recently honored with the Indian Achievers Award 2022, for its excellence in building deeptech talent in India. For more information about TalentSprint, visit TalentSprint website
Machine Learning, Rank Aggregation, Statistical Learning, Sequential Decision Making
Assistant Professor at the Dept. of Computer Science & Engineering and core member of the Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras. Before joining IIT Madras, he worked as a research scientist at the Xerox Research Center (now Conduent Labs), Bangalore. His research interests include Machine Learning, Rank Aggregation, Statistical Learning and Sequential Decision Making.
Experimentation, Applied Statistics and Machine Learning
Faculty member in the Department of Management Studies, core member of the Robert Bosch Center for Data Science and Artificial Intelligence (RBCDSAI), and Co-Head of the American Express Lab for Data Analytics, Risk and Technology (DART) at IIT Madras. Before joining IIT Madras, Nandan worked as a quantitative researcher for five years at a high-frequency algorithmic trading firm in New York. His research and work experience focuses on applications of experimentation, machine learning and the abstraction of data to models and algorithms. This spans data and problems across different domains, including but not limited to finance, urban mobility, digital platforms, civic services, and criminology.
Machine Learning for Biological and Engineering Networks, Safe Reinforcement Learning
Assistant Professor at the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, coordinator of Network Systems Learning, Control and Evolution Group, core faculty member at Robert Bosch Centre for Data Science and Artificial Intelligence and Initiative for Biological Systems Engineering, IIT Madras. Nirav’s research interests include first-principle and machine learning approaches for modeling and control of biological networks from multi-sensor and multi-scale data, safe learning and control of man-made networks, and bioprocess data analysis.
Reinforcement Learning, Geometric Deep Learning, Data Mining
Head of the Robert Bosch Centre for Data Science & Artificial Intelligence (RBCDSAI) and Mindtree Faculty Fellow and Professor in the Department of Computer Science and Engineering at IIT Madras. He has published nearly 100 papers in premier journals and conferences such as ICML, AAAI, IJCAI, ICDM, ICLR, NeurIPS, UAI, ISMB, and AAMAS. He has also co-authored the chapter on Reinforcement Learning in the Handbook of Neural Computation published by Oxford University Press. His work with students has won multiple best paper awards, the most recent being a best-paper runner-up at AAMAS 2020. His current research interests are centred around geometric deep learning and reinforcement learning.
Biological Networks, Computational Systems Biology, Genomics and Computational Biology
Associate Professor at the Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras, coordinator for the Initiative for Biological Systems Engineering (IBSE) and core faculty member at RBCDSAI, IIT Madras. Karthik is also the co-founder of qBiome Research Private Limited, an IIT Madras incubated startup offering customised cellular, metabolic and process engineering solutions for the biotech, pharma and chemical industry. His group works on scalable algorithms to study biological networks, and machine learning using high-dimensional multi-omic data, with applications in metabolic engineering and disease biology.
Dynamic Traffic Assignment, Transportation Network Modelling, Intelligent Transportation Systems, Pedestrian Safety
Associate Professor in the Transportation Engineering Division, Department of Civil Engineering and core faculty member in Robert Bosch Center for Data Science and AI, IIT Madras. His overarching goal is to develop technological and management solutions for a safe and sustainable transportation system. His primary research interests are in network modeling, dynamic traffic simulation and assignment, Intelligent Transport Systems (ITS), vehicular emissions, urban freight, public transit systems, and road safety.
