South Central Railways (SCR) has been at the forefront of leading AI and analytics initiatives in the Indian railways (IR). SCR has been nominated to set up a “Center of Excellence (CoE) for AI and Analytics” with the Indian School of Business (ISB) as a knowledge partner in this effort. As part of this initiative, ISB will offer custom-designed, blended learning program titled “Basics in Artificial Intelligence & Data Analytics” to Indian Railways Officers.  The programme is aimed to empower Indian Railways officials to successfully leverage the potential of Artificial Intelligence and Data Analytics to transform the efficiency and effectiveness towards operations and citizen-centric services.

As per the scope of work enlisted, the engagement is divided into three phases. The first phase consisted of custom-designed, blended learning programme titled “Basics in Artificial Intelligence & Data Analytics” for a group of 40 Officers per batch at South Central Railways’. As part of this programme ISB along with SCR customised the course modules and organised training / workshop/ under the theme of Machine Learning, Big Data and Deep Learning Applications, Analytics –Social, Web and Forecasting.

Subodha Kumar

Subodha Kumar is the Paul R. Anderson Distinguished Chair Professor of Marketing and Supply Chain Management and the Founding Director of the Center for Business Analytics and Disruptive Technologies at Temple University’s Fox School of Business. He has secondary appointments in Information Systems and Statistical Science Departments. He also serves as the Ph.D. Concentration Advisor for Operations and Supply Chain Management. He is a board member for many organizations. He has been awarded a Changjiang Scholars Chair Professorship by the China’s Ministry of Education. He is also a Visiting Professor at the Indian School of Business (ISB). He has served on the faculty of University of Washington and Texas A&M University. He has been a keynote speaker and track/cluster chair at leading conferences. He was elected to become a Production and Operations Management Society Fellow in 2019. He has received numerous other researches and teaching awards. He has published more than 150 papers in reputed journals and refereed conferences. He was ranked #1 worldwide for publishing in a top business school journal. In addition, he has authored a book, book chapters, Harvard Business School cases, and Ivey Business School cases. He also holds a robotics patent. He is routinely cited in different media outlets including NBC, CBS, Fox, Business Week, and New York Post. He is the Deputy Editor of Production and Operations Management Journal and the Founding Executive Editor of Management and Business Review (MBR). He also serves on other editorial boards. 


There are two modules in this course:

  • Social Media and Web Analytics: In this module, you will learn about social media and web analytics. Web and social media analytics have become an important part of the marketing campaign for any firm. The goal of this module is to understand different components of web and social media analytics, and how to design and manage them. We will begin with the discussion of search engine marketing (SEM) and ad  auctions, followed by the analysis of keyword management in web analytics. We will then move to social media analytics. In social media, it is important to track and analyze customer engagement. We will discuss different metrics of social media analytics and how to track them. An important aspect of social media analytics is to manage influencer marketing. We will discuss how to leverage social media and how to optimize an influencer marketing campaign. We will also understand how to do sentiment analysis of social media posts using simple tools in excel. We will complement our understanding with examples/cases.
  • Forecasting Analytics: In this module, you will learn about forecasting analytics. Forecasting is very important for the success of any business. It has become even more important because of the increasing availability of big data from different sources. We will begin with understanding the importance of forecasting. We will then analyze how to use data for forecasting. This module will cover time series decomposition of the data. We will briefly analyze different time series forecasting techniques, such as smoothing techniques (SES, DES, Holt Winter) and ARIMA model. We will also delve into machine learning and neural network-based forecasting models. We will discuss how forecast-as-as-a-service (i.e., cloudbased forecasting solution) works. We will complement our understanding with examples/cases.

Vineeth N Balasubramanian

Dr Vineeth N Balasubramanian is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology, Hyderabad (IIT-H), and currently also serves as the Head of the Department of Artificial Intelligence at IIT-H. His research interests include deep learning, machine learning, and computer vision. His research has resulted in over 100 peer-reviewed publications at various international venues, including top-tier ones such as ICML, CVPR, NeurIPS, ICCV, KDD, ICDM, and IEEE TPAMI. His PhD dissertation at Arizona State University on the Conformal Predictions framework was nominated for the Outstanding PhD Dissertation at the Department of Computer Science. He is an active reviewer/contributor at many conferences such as NeurIPS, CVPR, ICCV, AAAI, IJCAI as well as journals including IEEE TPAMI, IEEE TNNLS, JMLR and Machine Learning, with recent awards as Outstanding Reviewer at CVPR 2019, ECCV 2020, BMVC 2020. He is also a recipient of the Teaching Excellence Award at IIT-H in 2017. His research is funded by various organizations including DST, MeiTY, DRDO, Microsoft Research, Adobe, Intel, and Honeywell. He currently serves as the Secretary of the AAAI India Chapter. For more details, please see 


The success of machine learning models in business applications today is contingent on the availability of large amounts of curated labeled (or annotated) data. However, annotation of data is a very expensive process, but unannotated data by itself is abundantly available. This necessitates the understanding and use of machine learning algorithms that operate on data without labels, viz, unsupervised learning. Unsupervised learning has many applications including finding clusters in data (used for example in advertising), summarizing the data, reducing the dimensionality of data (useful for high-dimensional data such as images), or simply finding representations of data that may be useful in downstream applications. Having covered supervised learning in the first module, we will cover unsupervised learning algorithms in this module including clustering methods, dimensionality reduction methods as well as the more recent unsupervised deep learning methods. These methods can be used to find patterns and insights in data even without the arduous processes of annotating the data.

Manish Gupta

Manish Gupta is a Principal Applied Researcher at Microsoft India R&D Private Limited at Hyderabad, India. He is also an Adjunct Faculty at International Institute of Information Technology, Hyderabad and a visiting faculty at Indian School of Business, Hyderabad. He received his Master’s in Computer Science from IIT Bombay in 2007 and his Ph.D. from the University of Illinois at Urbana-Champaign in 2013. Before this, he worked for Yahoo! Bangalore for two years. His research interests are in the areas of web mining, data mining and information retrieval. He has published more than 100 research papers in reputed refereed journals and conferences. He has also co-authored two books: one on Outlier Detection for Temporal Data and another one on Information Retrieval with Verbose Queries. 


There are two modules in this course:

  • Big data applications: Big data analytics is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis. It is used by organizations committed to data-driven decision-making. It is about using your data to derive information, insights, knowledge, and recommendations. Businesses use data science and data analytics to improve effectiveness and efficiency of their solutions. In this module, I will talk about how analytics has progressed from simple descriptive analytics to being predictive and prescriptive. I will also talk about multiple examples to understand these better and discuss various industry use cases. I will also introduce multiple components of big data analysis including data mining, machine learning, web mining, natural language processing, social network analysis, and visualization in this module.
  • Deep learning: Deep learning has caught a great momentum in the last few years. Research in the field of deep learning is progressing amazingly fast. Machine learning has seen numerous successes but applying learning algorithms today often means spending a long-time hand-engineering the input feature representation. This is true for many problems in vision, audio, NLP, robotics, and other areas. To address this, researchers have developed deep learning algorithms that automatically learn a good representation for the input. These algorithms are today enabling many groups to achieve ground-breaking results in vision, speech, language, robotics, and other areas. I will discuss the basics of artificial neural networks and then focus on other popular deep learning architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs) Networks and Transformers

Shailesh Kumar

Dr. Shailesh Kumar is currently the Chief Data Scientist at AICoE / Reliance Jio. He also serves as a visiting faculty of Machine Learning at the Indian School of Business. Prior to this he was a Distinguished Scientist at Ola, CoFounder at ThirdLeap - an EdTech AI company, Researcher in the Google Brain team, Principal scientist at Microsoft Bing, Senior Scientist at Yahoo! Labs, and Principal Scientist at Fair Isaac Research. Dr. Kumar has over twenty years of research, academic, start-up, industrial, and leadership experience across various areas of AI such as in natural language processing, speech processing, computer vision, fleet management, retail data mining, genomics, remote sensing, agriculture, healthcare, smart cities, etc. He has published over 20 conference papers, journal papers, and book chapters and holds about 20 patents in these areas. Dr. Kumar was recognized as one of the Top 10 data scientists in India in 2015, top 50 Analytics Leaders in India in 2018, and top 10 most influential Analytics leaders in India in 2020. Dr. Kumar received his PhD and Master’s in Computer Science from the University of Texas at Austin and B.Tech. in Computer Science from IIT-Varanasi.


Today many industries, businesses, and organizations have gone through a digital transformation. As a result, they have scaled their operations, built automation into their workflows, and increased their overall efficiency because of this transformation. As we enter the next decade, these institutions are now poised for a an “AI transformation” where they will further increase the accuracy and precision of their decisions and further optimize their operations with the ability to convert the data they are collecting into more intelligent decisions. This is possible with the maturity of technologies such as Artificial Intelligence, Machine Learning, and Deep Learning along with cloud computing and big data engineering and so on. In this course we will explore a wide variety of machine learning algorithms especially the Supervised Learning algorithms that help us make better predictions about what is about to happen and why. These data driven predictions can then be used to improve the efficiency of our operations, reduce their cost, and increase their RoI because of “proactive”, “optimal”, and “real-time” decision making