Introduction
As a consequence of decades of information technology deployment, organizations today have more information at hand than ever before. But in many cases the information is not being utilized to out-think their rivals. Thus organizations are missing out on a potent competitive tool.
Competence in analytics has become a critical skill for managers of the new age business organizations. Business Analytics is about quantitative analysis and predictive modeling towards data-driven competitive strategies. This course covers methodologies and practices that are important for data analytics and discuss their relevance in various functional domains. It is designed for professionals who need a managerial introduction to this discipline.
This course will introduce participants to some of the most widely used modeling techniques and their core principles. By taking this course, participants will form a solid foundation of analytics, which refers to tools and techniques for building statistical models to make predictions based on data. This course is designed for those who are interested in using data to gain insights and make better business decisions. It would also improve managerial and leadership capabilities of participants.
Through interactive case discussions, assignments and experience sharing, the participants will be introduced to several elements of data analytics tools and execution techniques. Participants will be provided with selected reading materials before the program. All sessions will be of 90 minutes each. The program will provide an analytical view of decision making and insights into descriptive and predictive analytics with an introduction to prescriptive analytics. The program outline will follow the following schedule
Program Content
| Session | Topic | Description |
| DAY 1 | ||
| Session 1 | Analytics on Spreadsheets – Data Visualization and Exploration | Learn applications of business analytics, understand how data is used in business decisions, learn data types and decisions models |
| Session 2 | Descriptive Statistics, probability measures, sampling and estimation | Understand statistical notations, learn sampling techniques and conduct statistical tests. |
| Session 3 | Trend lines and Regression Analysis | Learn application of regression analysis in business decisions, apply a systematic approach to build good regression models |
| Session 4 | Case Study and Analysis | Apply the tools learnt to a real life business case, understand data analysis methods and undertaken strategic decisions based on analytics |
| DAY 2 | ||
| Session 1 | Forecasting | Learn subjective and objective approaches to forecasting for demand forecasting, planning, and management |
| Session 2 | Forecasting Application | Learning forecasting application |
| Session 3 | Decision Making under Uncertainty | Discuss real life examples of how some industries deal with uncertainty by applying strategies that address uncertainty. A gentle introduction to Decision Trees when Risk and Uncertainty are involved |
| Session 4 | Case Study and Analysis | Apply the tools to a real life case, discuss that while technology can help, it takes more than that to better forecast, plan and match supply with demand. Human cognition needs to be understood and accounted for. |
Faculty
Assistant Professor
Dr. Ghosh holds a PhD from Indian Institute of Management Bangalore. Prior to joining academics, he worked in consulting in the retail and consumer packaged goods domain.
Director of MISI Center for Sustainable Value Networks
Dr. Ata is an Assosiate Professor at the MIT Global Supply Chain and Logistics Excellence Center at the Malaysia Institute for Supply Chain Innovation (MISI) in Shah Alam, Malaysia.