Key Takeaways
At the end of the course, the participants will be able to:
- Analyse data that involves time-to-event outcomes using Survival Analysis
- Analyse time series data, which consists of observations recorded over time intervals using ARIMA & Seasonal ARIMA Methods
- Extend time series analysis by using transfer functions
- Analyse time series data with volatility clustering using ARCH & GARCH Modelling
- To ventures into the applications of AI such as deep learning (RNN, LSTM, LLMs), a powerful subset of machine learning using artificial neural networks
- To explore a popular marketing research technique using Conjoint Analysis
Who Should Attend
This is an advanced level analytics course, suitable for professionals with 2-3 years of experience, with an interest or requirement to develop advanced predictive models and provide input to improve service quality and apply predictive modeling in health space.
It is applicable for the following professionals who are engaged in the planning/forecasting and service innovation area:
- Government
- Banking
- Telecom
- Insurance
- Retail
- Travel
- Healthcare
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/Python.
- Participants are required to have completed the Statistics Bootcamp II &
Predictive Analytics - Insights of Trends and Irregularities prior to attending this course.
NUS-ISS also offers a range of other basic courses in Data Science for participants new to data science.
What to Bring
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.
If you are bringing a laptop, please see below for the tech specs:
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Minimum
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Recommended
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Operating Systems
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• Windows 7, 8, 10 or
• Mac OS
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Laptop running the latest
version of either Windows or
Mac OS
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System Type
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32-bit
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64-bit
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Memory
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8 GB RAM
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16+ GB RAM
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Hard Drive
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256 GB disk size
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Others
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• An internet connection – broadband wired or wireless
• Installation permissions (non-company laptops)
• Keyboard
• Mouse/Trackpad
• Display
• Power adapter (laptop battery might run out) |
DirectX 10 graphics card for graphics hardware acceleration
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Additional Software Requirements:
What Will Be Covered
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Day 1
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Module 1: Introduction to Advanced Predictive Modelling
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Module 2: Revisit Time Series Methods (ACF/PACF Functions, AR/MA)
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Module 3: ARIMA & Seasonal ARIMA Methods
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Module 4: Workshop 1: Forecasting using ARIMA/SARIMA methods based on relevant practical case study
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Day 2
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Module 5: Extending Univariate to Multivariate Time Series – Transfer Functions
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Module 6: Introduction to ARCH & GARCH Modelling
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Module 7: Workshop 2: Time Series Forecasting case study using Transfer Functions
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Quiz 1
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Day 3
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Module 8: Introduction To Conjoint Analysis
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Module 9: Traditional Conjoint
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Module 10: Adaptive Conjoint Analysis (ACA)
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Module 11: Workshop 3: Case study: Traditional Conjoint Models development to Solve an Industry Problem
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Day 4
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Module 12: Choice-Based Conjoint (CBC)
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Quiz 2
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Module 13: Predictive modelling Using Survival Analysis
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Module 14: Workshop 4: Case Study & Workshop using CBC & ACA to Solve an Industry Problem
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Day 5
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Module 15: Survival Analysis continued
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Module 16: Case Study and Workshop on Survival Analysis Modelling
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Quiz 3
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Topic 1:
Advanced Time Series Forecasting: There are some complex industry forecasting problems which can’t be solved using time series regression,dummy variable regression, decomposition methods, Variations of Exponential Smoothing & ARIMA. Much more sophisticated methods are needed to solve some of the problems that arise in the industry.
We focus on some of the advanced versions of ARIMA: SARIMA, ARIMAX/ Transfer Function Models. ARCH & GARCH Modelling for finance-related modelling
Topic 2:
Conjoint Analysis: Conjoint analysis is a statistical method for finding out how consumers make trade-offs and choose among competing products or services. It is also used to predict (simulate) consumers’ choices for future products or services.
There are three types of methods available: 1) Traditional Conjoint 2) Adaptive Conjoint and 3) Choice-Based Conjoint. This advanced technique has been used in market research for a long time. It is an effective tool to improve service quality, enhance product features, understand competition and predict market share.
Topic 3:
Survival Analysis: In the past, this topic used to come under reliability theory primarily used in the manufacturing sector and biomedical industry. The technique was used to predict the lifetime of machines as well as humans. It is an area of predictive analytics where the dependent variable is truncated and hence requires special treatment. The application of this Methodology is also known as Time to Event Modelling. In the modern world, this Methodology is used to predict many scenarios like playtime prediction of popular games, choosing stocks for investment decision by a firm’s performance in stressed situation etc.
Fees & Subsidies
Fees for 2025
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Full Fee |
Singaporeans & PRs
(self-sponsored) |
| Full course fee |
S$4500 |
S$4500 |
| ISS Subsidy |
- |
(S$450) |
| Nett course fee |
S$4500 |
S$4050 |
| 9% GST on nett course fee |
S$405 |
S$364.50 |
| Total nett course fee payable, including GST |
S$4905 |
S$4414.50 |
Note:
- All fees and subsidies are valid from January 2024, unless otherwise advised.
- All self-sponsored Singaporeans aged 25 and above can use their SkillsFuture Credit to pay for course fees. For more information about SkillsFuture Credit, click here.
- From 1st January 2024, the GST will be increased to 9%.
Certificate
Certificate of Completion
Participants have to meet a minimum attendance rate of 75% and are required to pass the assessment to be issued a Certificate of Completion.
Join Us
Register now and learn to perform predictive and forecasting for real world complex problem
Preparing for Your Course
NUS-ISS Course Registration Terms and Conditions
Find out more.
NUS-ISS and Learner’s Commitment and Responsibilities
Find out more.
WIFI Access
WIFI access will be made available to participants.
Venue
NUS-ISS
25 Heng Mui Keng Terrace
Singapore 119615
Click HERE for directions to NUS-ISS
In the event of a change of venue, participants are advised to refer to the acceptance email sent one week prior to the commencement date.
Course Confirmation
All classes are subject to confirmation and NUS-ISS will send an acceptance email to participants one week prior to the commencement date. Confirmed registrants are to attend and complete all lectures, class exercises, workshops and assessments (where applicable). Additionally, all responses to feedbacks and surveys conducted by NUS-ISS and its partners must be submitted. All training and assessments will be delivered as described in the course webpage.
General Enquiry
Please feel free to write to ask-iss@nus.edu.sg if you have any enquiry or feedback.