For the past two decades, drastic global changes have been experienced in every society due to rapid and disruptive advancements in the field of IT and related areas. Growth of computer power, abundance of smart phones, cloud technology and artificial intelligence enabled successful digital transformation in all corners of business and technology. As a result, required skill bases for new normal activities have shifted dramatically, generating hunger for new skills in the business and industries.
Amid such skill crisis, JAGSoM came up with a Tomorrow’s technology driven business analytics practice oriented module in their PGDM program. Aspiring graduates will be provided with a ten-credit sandwiched course between business houses and academic campus. The course will primarily have objectives of keeping pace with technological and managerial advancements and learn the stuff at the industry bench.
The course includes all possible data oriented advanced skills on end-to-basis. A capstone project to solve challenging business problems, is also included, guided by industry mentors. Contextual data analyses including, pre-processing, visualization, machine learning models and deep learning models and optimization algorithms, are in the central focus.
Analytics Concepts with Python
Supervised Machine Learning using Python
Image Processing Fundamentals
Introduction to Deep Learning
Professor and Chairperson of Digital Business and Analytics area, JAGSoM
Dr.Supriyo Ghose has an academic and corporate experience of over 25 years. He has completed his Fellow Program in Management from IIM Calcutta. His corporate affiliations include brands like TCS, PwC, Mahindra Satyam, and Infosys. He started his teaching career at IIM Calcutta, and has also served at Amrita University, Alliance University, and IIM Ranchi (as Visiting Faculty).
Professor, Digital Business and Analytics, JAGSoM
Professor, researcher and consultant, Dr. Sengupta has a rich experience of academics and industry for more than three decades. He completed his graduation and post-graduation from the University of Warwick, UK, and his Ph.D. from the University of Calcutta. He was affiliated with Jadavpur University, IISWBM, ICFAI, IMT Ghaziabad, RCC, IBS, etc. He also taught at IIM- Ranchi, IIFT, IMI- Kolkata as a visiting faculty. He has a rich consulting experience across various clients in India.
Senior Professor, Digital Business and Analytics, JAGSoM Dr. Subramanyam was the chair Professor & officiating Director at FORE School of Management, New Delhi. He has done his B.Tech and M.Tech from IIT Kanpur, and PhD from University of Georgia, USA. He has a vast experience of more than 40 years in teaching and research. He worked as Professor and Area Chair of the Quantitative and Information Systems Group at IIM Lucknow, and also a visiting Professor at Manchester Business School. He has filed two patents in Financial domain.
Assistant Professor, Digital Business and Analytics, JAGSoM
Dr.Ellur Anand is an Assistant Professor in Digital Business and Analytics area. He is Green Belt Certified in Lean Six Sigma. He has completed his post-graduation from Amrita Institute of Management and Ph.D. from Pondicherry Central University. He served in many reputed institutions, including Alliance University and Kirloskar Institute of Advanced Management Studies (KIAMS).
CEO & Chief Data Scientist – Quadratyx and Principal Advisor & Mentor, INSOFE
Dr.Sreerama KV Murthy received a PhD degree in Data Mining and Machine Learning from Johns Hopkins University. In the past 25 years, Dr. Murthy worked for world-class research labs such as Siemens Corporate Research, IBM Research and CDAC, Mumbai. He has 8 US patents and international publications, all in Data Science.
Dr.SuryaprakashKompalli has an extensive research background in image processing, pattern recognition, and GPU computing. He did his Ph.D. and MS from University at Buffalo. He has multiple patents and publications, and has also taught at Carnegie Mellon University and worked at Microsoft and HP Labs.
Principal Data Scientist, INSOFE
Mr. Maheshkumar is Principal Data Scientist at INSOFE. He has more than 15 years of experience in the domain of statistical modeling and analysis. He has rich experience in several challenging data science problems across domains, gaining experience in architecting solutions and end-to-end project execution. He has led teams in developing algorithms and delivering solutions on both structured and unstructured data.
Data scientist, INSOFE
Mr. Koushik is a data scientist at INSOFE. He completed his post-graduation in Big Data Analytics and Optimisation in 2018. He has rich experiences in data science consultation projects such as automated resume parsing tool, automation tool for detecting defects in semiconductor wafers, an automation tool for GDPR analytics. He is an expert in NLP, Image Processing, Programming Languages, and Databases.
Food classification for grocery store management using Computer Vision.
A grocery store wants to automate the process of identifying the item vacancies in the racks and then immediately reporting them to the respective departments for rack-refill. It achieves it by identifying the vacancies first, and then figuring out to which food category that particular part of the rack belongs to. As part of the project the company wants to classify various images into different classes of food using Machine Learning and Computer Vision.
Detecting driver’s attention level from eyeball movement.
Driver distraction is one of the primary causes of accidents on roads.AI systems that are able to identify unsafe driver or passenger behaviour can
help a long way in reducing accidents. These can be used in intelligent transport systems,accident control mechanisms, an even automobile insurance industry. The purpose of this project is to use Computer Vision and Machine Learning to identify driver’s attention level from the eyeball movement.
Portfolio Wealth Management.
Feasibility of setting up a new line of business in insurance BD.
Credit Fraud Detection.
We are assigned to build a model to detect credit fraud. The challenge is to recognize fraudulent credit card transactions so that customers of credit card companies are not changed for items that they did not purchase. Random data has been collected and we created a model, through which It helps to find fraudulent transaction happening around the world.
Credit Card Acquisition Model.
A model to identify potential customers who are eligible and may apply for credit card using behavioral pattern using data from social media.
Scoring Model for employer of an applicant and opportunities for future product diversification.
The objective of this project is to develop a scoring model that can help evaluate the employer of a salaried applicant and his relationship with the employer and analyze other variables /inputs relevant for employer verification from an underwriting perspective.