Overview
This course has been designed to equip analytics professionals and managers with an understanding of how to solve complex prediction problems beyond what can be done using preliminary methodologies. The course covers a few diverse topics to take care of some of the real-world problems which require non-standard methodologies.
Course Description & Learning Outcomes
This course is part of the Data Science and Graduate Certificate in Advanced Predictive Modelling Techniques Series offered by NUS-ISS. At the end of the course, the participants will be able to: - Develop and implement advanced time series forecasting solution for any domain which can’t be solved using standard time series forecasting techniques - While solving the above it will have all necessary discipline of >Evaluating predictive modelling objective >Design the predictive analytics process >Assess and select the appropriate testing methods to validate the models >Analyse the results and communicate the decision to the senior management and facilitate deployment to support the end-users - Develop & implement TTE (Time to Event) modelling solution using censored/truncated data - Develop Conjoint solution a type of discrete choice modelling that has many industry applications Understand the domain-specific variation needed in each of the topics
Recommended Prerequisites
- Participants with some prior years of experience working within planning teams in an organisation will benefit more from the course. - Participants also need to have a strong interest and knowledge in basic predictive modeling and be familiar with R. - Participants are required to have completed the Statistics Bootcamp II & Predictive Analytics - Insights of Trends and Irregularities prior to attending this course.
Pre-course instructions
- No printed copies of course materials are issued. - Participants must bring their internet-enabled computing device (laptops, tablet etc) with power charger to access and download course materials. Registration close date: 25/09/2023
Schedule
Date: 16 Oct 2023, Monday
Time: 9:00 AM - 5:00 PM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615
Date: 17 Oct 2023, Tuesday
Time: 9:00 AM - 5:00 PM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615
Date: 18 Oct 2023, Wednesday
Time: 9:00 AM - 5:00 PM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615
Date: 19 Oct 2023, Thursday
Time: 9:00 AM - 5:00 PM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615
Date: 20 Oct 2023, Friday
Time: 9:00 AM - 5:00 PM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615
Agenda
| Day/Time | Agenda Activity/Description |
|---|---|
| Day 1 | Module 1: Introduction to Advanced Predictive Modelling Module 2: Revisit Time Series Methods (ACF/PACF Functions, AR/MA) Module 3: ARIMA & Seasonal ARIMA Methods Module 4: Workshop 1: Forecasting using ARIMA/SARIMA methods based on relevant practical case study |
| Day 2 | Module 5: Extending Univariate to Multivariate Time Series – Transfer Functions Module 6: Introduction to ARCH & GARCH Modelling Module 7: Workshop 2: Time Series Forecasting case study using Transfer Functions Quiz 1 |
| Day 3 | Module 8: Introduction To Conjoint Analysis Module 9: Traditional Conjoint Module 10: Adaptive Conjoint Analysis (ACA) Module 11: Workshop 3: Case study: Traditional Conjoint Models development to Solve an Industry Problem |
| Day 4 | Module 12: Choice-Based Conjoint (CBC) Quiz 2 Module 13: Predictive modelling Using Survival Analysis Module 14: Workshop 4: Case Study & Workshop using CBC & ACA to Solve an Industry Problem |
| Day 5 | Module 15: Survival Analysis continued Module 16: Case Study and Workshop on Survival Analysis Modelling Quiz 3 |
Pricing
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