Venue/Deadlines | Program Dates | Program Fees |
---|---|---|
Venue : IIMB Campus
Early Bird Discount Date : 16 Dec, 2024
Last date for registration: 27 Dec, 2024 |
Start Date : 06 Jan, 2025 End Date : 11 Jan, 2025 |
Residential Fee(excluding GST) : Rs. 1,55,000
Residential Early Bird Fee(excluding GST) : Rs. 1,39,500 Non-Residential Fee(excluding GST) : Rs. 1,25,000 Non-residential Early Bird Fee(excluding GST) : Rs. 1,12,500 |
Programme Overview
Harnessing the power of predictive analytics, the corporate world has witnessed a surge in the adoption of analytical forecasting tools in the recent decades. This may be attributed to an increase in the complexity, competitiveness, and the rate of change in the business environment. The objective of this programme is to present a comprehensive view of the various tools and techniques used in forecasting for managerial decision-making, including the problem of demand estimation, market size determination, sales projections, analysis, and predicting stock prices. The methodology, covering various time series analysis techniques, and regression methods, is presented with an appropriate mix of case analysis and numerical demonstration with the aid of software package (R) to enable the participants to meet their own forecasting needs.
Participants would be divided into small groups to work on predictive analytics and forecasting projects that would be either decided/brought by the participants or given to them. Learning from these projects will be the key takeaway from the programme. A better part of a programme day is reserved for participants to work on this project. In addition, the participants are expected to work during the evenings of the programme days.
R, a free open-source software, is used throughout the course. Prior experience in working with R will be beneficial. On joining the program, introductory guidelines will be sent to the participants about a week before the programme to help them familiarize themselves with R. In addition, help would be available during the programme days to learn the necessary coding with R.
Programme Contents:
• Basic statistical concepts: standard error, confidence interval estimation, and significance values in testing.
• Simple and Multiple regression
• Logit Probit Models
• Time Series Decomposition Models
• Smoothing Models
• Box Jenkins (ARIMA)
• ARIMA with regression errors and ARIMAX
• Models for time series with multiple-level seasonality.
• Bass Model for new product forecasting
• Combining Forecast and Forecast Evaluation
• Forecasting projects/case studies from Industry
• Project work and presentations
Key benefits/takeaways
On completion of this course, the participants will be conversant in various forecasting models and implementing them using R. In particular, the participants should be able to:
Who Should Attend
The programme is targeted at executives with an analytic mindset intending to use various models in forecasting. While the programme would start with a basic review of statistical techniques, it will be useful if the participants have some introduction to elementary statistics at 10+2 or undergraduate level and previous exposure to forecasting problems in their work. Even if they do not have hands-on experience in solving such problems at any level. Participants are encouraged to bring data that are specific to a forecasting problem of individual interest; otherwise, they will be given alternative datasets to work on during the programme.
Top-notch faculty, contemporary content, a great learning environment, and application orientation make the IIMB Exec Ed programmes world-class.