ABOUT THE COURSE: Business forecasting is the technique used to cast the foremost business scenarios to ease out the business decisions and management in future. The ongoing development in the field of predictive analytics using data analytics and machine learning techniques helps to shape and analyze the historical data to know the future business possibilities. Utilizing the power of business forecasting allows organizations to handle the uncertainty better and find new possibilities for sustainable growth of business. This course 'business forecasting' will help learners to gain the proficiency in predictive analytics applications and develop the skills to analyze historical data for future demand planning and resource allocation. It will help to understand the key role of forecasting in business planning; understand and know when to use various models of forecasting; the analysis of time series data into various components such as seasonality, trend, and cyclical forecast; understand both qualitative and quantitative forecasting approaches and several business applications of forecasting in marketing, inventory and supply chains, project management, finance and retailing.INTENDED AUDIENCE: Students (UG/PG/PhD), faculty, and industry professionalsPREREQUISITES: Basic Probability and Statistics;The students should be at least in their final year of UG and aboveINDUSTRY SUPPORT: All sectors/companies/industries
Overview
Syllabus
Week 1:
Lecture 1: Introduction to Business forecasting
Lecture 2: Data Driven Decision Making and Essentials of Predictive AnalyticsLecture 3: Types of Forecasting: Qualitative Approaches and Quantitative Approaches
Week 2:
Lecture 4: Components of a Time Series and Measures of Forecast AccuracyLecture 5: Moving Average Methods: Simple, Weighted, and Exponential Moving Average
Week 3:
Lecture 6: Exponential SmoothingLecture 7: Trend Projections and Holt Model
Week 4:
Lecture 8: Regression AnalysisLecture 9: Measure of Goodness and Standard Error
Week 5:
Lecture 10: Seasonality, Seasonal Index, and Quarterly Average MethodLecture 11: Seasonality and Trend: Holt Winter MethodLecture 12: Decomposition Method
Week 6:
Lecture 13: ACF and PACFLecture 14: ARIMA
Week 7:
Lecture 15: Introduction to Machine LearningLecture 16: Logistic RegressionLecture 17: Human Judgement in Time Series Analysis
Week 8:
Lecture 18: Monte Carlo Simulation and Risk AnalyticsLecture 19: Predictive Analytics using @Risk software/Python
Lecture 1: Introduction to Business forecasting
Lecture 2: Data Driven Decision Making and Essentials of Predictive AnalyticsLecture 3: Types of Forecasting: Qualitative Approaches and Quantitative Approaches
Week 2:
Lecture 4: Components of a Time Series and Measures of Forecast AccuracyLecture 5: Moving Average Methods: Simple, Weighted, and Exponential Moving Average
Week 3:
Lecture 6: Exponential SmoothingLecture 7: Trend Projections and Holt Model
Week 4:
Lecture 8: Regression AnalysisLecture 9: Measure of Goodness and Standard Error
Week 5:
Lecture 10: Seasonality, Seasonal Index, and Quarterly Average MethodLecture 11: Seasonality and Trend: Holt Winter MethodLecture 12: Decomposition Method
Week 6:
Lecture 13: ACF and PACFLecture 14: ARIMA
Week 7:
Lecture 15: Introduction to Machine LearningLecture 16: Logistic RegressionLecture 17: Human Judgement in Time Series Analysis
Week 8:
Lecture 18: Monte Carlo Simulation and Risk AnalyticsLecture 19: Predictive Analytics using @Risk software/Python
Taught by
Prof. Pankaj Dutta
