Overview
This course will leverage the combined strengths of predictive and prescriptive techniques (e.g. statistics, econometrics, mathematical optimisation methods etc.) to provide attendees with a practical understanding of Product and Pricing Analytics to derive actionable insights for demand forecasting, commercial excellence and portfolio management.
Course Description & Learning Outcomes
Three key areas to be covered are (i) Analytics for Product and Pricing Excellence including Design of Experiments (ii) Econometric Modelling and Forecasting, and (iii) Decision Science and Optimisation. This course is part of the Data Science Series offered by NUS-ISS. The course provides an intermediate pathway to the ISS portfolio in the Data Science Graduate Certificate in Specialised Predictive Modelling & Forecasting syllabus that will be one of the graduate certificates in analytics, stackable towards a Masters of Technology in Enterprise Business Analytics. At the end of the course, participants will be able to: - Understand product & pricing sensitivity evaluation for making the “go/no-go” decision at the end of new product development process and estimating post launch adoption - Apply experimental design methods for concept testing, factor screening, rapid experimentation and optimisation for new and connected products - Understand basic economic concepts and apply econometric modelling techniques for demand forecasting and resource allocation - Understand the decision science techniques for optimisation - Understand the overall optimisation challenges in handling product from price and cost perspective for a typical business entity (Linear Programming, Non-Linear Programming, Integer Programming) - Apply the strategy and methods for product and price optimisation, inventory planning and optimisation, analytical optimisation vs dynamic simulation (Monte Carlo Simulation and Discrete Event Simulation)
Recommended Prerequisites
Participants should have basic knowledge in business, marketing and be familiar with computer/statistical software (at least perform data analysis using excel). It will be assumed that participants will have foundational knowledge in statistics at the level of the "Statistics Bootcamp II". Knowledge in Predictive Analytics (equivalent to our course Predictive Analytics - Insights of Trends and Irregularities) is strongly recommended. NUS-ISS also offers a range of other basic courses in Data Analytics Process and Best Practice II, Statistics Bootcamp II and Predictive Analytics - Insights of Trends and Irregularities for participants new to Data Science.
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: 10/10/2023
Schedule
Date: 31 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: 01 Nov 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: 02 Nov 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: 03 Nov 2023, Friday
Time: 9:00 AM - 5:00 PM (GMT +8:00) Kuala Lumpur, Singapore
Location: NUS-ISS, 25 Heng Mui Keng Terrace, 119615