Advanced R Programming for Data Analytics in Business

ABOUT THE COURSE: Over the next few decades, Data Science (DS), Machine-Learning (ML), and AI (Artificial Intelligence) will play a crucial role in several aspects of business decision-making and management information systems. Leaders in organizations need to capitalize on data analytics to gain a competitive advantage in the modern business landscape. The application of cutting-edge data analytics techniques implemented with R programming (and RStudio, a powerful IDE(Integrated Development Environment) will prepare the learners for business analytics workflow and make them job ready for mid-to-senior managerial positions in various business and industry settings. In this course, you will use advanced data analytics tools to explore, clean, wrangle, visualize, and process business data to generate useful insights and make inferences from raw and unstructured data. The course will also introduce the learners to use cases from business, finance, and management areas and problem sets that require advanced techniques for processing the data and communicating the results, and providing managerial implications. This course has been carefully designed to cater to not only business, finance, and management professionals but also those from other industries and academics that significantly rely on data-driven decision-making. The operating environment for all types of organizations (engineering and management) has become extremely dynamic and data-driven and continues to evolve at an extremely fast pace, with technological innovations at the heart of this change. Against this backdrop, DS, ML, and AI are providing new opportunities for all market participants, i.e., business leaders, policymakers, regulators, and governments. The objective of this course is to help the learners understand and apply these modern DS, ML, and AI techniques in the business, finance, and management industry. This includes solving real-life business, finance, and management problems to improve organizational decision-making. Throughout the course, we have used three kinds of data: 1. In-built datasets: These datasets are readily available within R. 2. Publicly available data: These datasets are sourced from publicly available sources like Yahoo Finance and Google Finance. 3. Proprietary Data: Acquired by the faculty from third-party sources, this data is of a proprietary nature and can only be used by the faculty strictly for academic research purposes. The faculty is not allowed to share these datasets. For the inbuilt datasets, students may follow the respective code (R packages) for acquiring them. Next, for publicly available data, students are expected to obtain data on their own from the sources like Google and Yahoo Finance. For the proprietary data, students can create dummy data on their own using R packages and practice with it. In our historical experience, learning is more effective when students type the code themselves and use datasets they have created (dummy data) or the data sourced from public sources or R packages. INTENDED AUDIENCE: Management students (Ph.D., MBA, BBA), Commerce students (BCom, M.Com.), Chartered Accountants, Science (B.Sc., M.Sc.), and Engineering students (B-Tech, M-Tech), Finance professionals (Investment analysts, banking professionals, accountants, credit analysts), Data Scientists INDUSTRY SUPPORT: Data Science and Business Analytics: Mu Sigma Analytics, Fractal Analytics, Manthan. Latent View, Tiger Analytics, Absolutdata, Convergytics, UST Global; Equity research firms, Credit rating firms, Investment Banks, Corporate Banking sector, Corporate Finance roles across all corporates (ICRA, ICICI, HDFC, Nomura, Lehman Brothers, SBI Capital Markets, Deutsche bank, HSBC Bank, etc.)