The programme will use a combination of case studies, discussions, exercises and lectures.

• To equip experienced analytics professionals with new concepts and skills • To guide these professionals in usage of advanced techniques for better business decisions • To address the shortage of skilled professionals capable of implementing advanced analytical solutions for Big Data.

Module 1: Big Data and its Business Applications 

In the last decade we have seen an unprecedented adoption of information technologies (IT) and analytics by organizations cutting across industry domains. The Covid-19 pandemic has hastened the pace for several industries who were initially reluctant adopters. The falling cost of storage, increased data transmission speeds, wide availability of sensors etc. has enabled organizations to develop excellent business applications that can support complicated decision making in real time. In this module we discuss some of these business applications across multiple industry domains that are powered by Big Data Analytics..

Module 2: Techniques of Big Data Analytics

In management applications dependent data occurs very often. With such data it is important to analyze all the variables together and derive insights from their dependence on one another. In this module we aim to discuss techniques that are used to visualize, summarize and draw actionable insights from such data. Some of the techniques that are covered in this module are Regression, Dimensionality Reduction, Clustering, Decision Trees, Random Forests and, Artificial Neural Networks.

Module – 3: Variety - Analytics with Complex Data

In this module we deal with analysis of Complex Data which is at the heart of the “Variety” challenge referred to in Big Data Analytics literature. Modern data comes in a variety of forms both structured and unstructured which cannot be analysed using the commonly used tools and techniques for Euclidean data. In this module we introduce the participants with techniques for visualizing and analysing Text data, Functional data and Spatial data with applications and case studies drawn from different areas of management.

Module – 4: Velocity - Stream Data analytics

In this module we deal with analysis of data streams which is often referred to as “Velocity” challenge in the Big Data Analytics literature. Such data is continuously accumulated and the underlying data generation process is prone to abrupt changes for various reasons some of which may be unknown to the analyst. In this module we discuss techniques of clustering, classifying and predicting with stream data. 

Module – 5: Big Data Challenges – Quality,  Confidentiality, Bias and Ethics

While the presence of Big Data has generally been beneficial for the businesses it has opened up some raging debates, Since the effectiveness of the techniques of Big Data Analytics depend crucially of the quality of data, monitoring Big Data quality is of critical importance. Since data is often collected for purposes which are different from that for which it is used it opens up important questions related to privacy and confidentiality. It is now recognized that the role of data is as important (if not more) as the techniques used to effectively eliminate bias from the Big Data based AI systems. In this module we discuss these important issues of quality, privacy, bias and ethics with regard to Big Data based AI systems for business applications.