Practice Module for Specialized Predictive Modeling & Forecasting

Objectives

The objective of the practice module is twofold:

• Firstly, to expose participants to real world problems so that they may practice the use of the skills they have learned during the component courses in a holistic manner.

• Secondly, to enable participants to demonstrate their proficiency across all of the skills that they have learned in the course modules and hence obtain a grade at the Graduate Certificate Level.

Intended Audience

This practice module is targeted at participants who wish to complete the certification process for the Graduate Certificate in “Specialised Predictive Modelling and Forecasting”.

Prerequisites

Participants must have successfully obtained a competent score (or have been exempted) from the three component courses for the Specialised Predictive Modelling and Forecasting as listed in the introduction to the Graduate Certificate page.

Components

There are two parts to the Practice Module.

1. Practice Project: Participants will need to undertake one project to gain practical experience and demonstrate their understanding and mastery of the skills taught in the three component courses. The practice project will require each participant to expend an estimated 10 man days of effort. These days are not expected to be contiguous and may stretch over many weeks. These projects may be conducted by individual participants or in teams depending on the nature of the project requirements.
2. Examination: Each participant is required to sit for an examination on a stipulated date and time.
3. The overall grade for the participant will be based on the Practice project and Examination.

      Typical examples of projects to be undertaken

1. Problem description:

This project aims to help a hospital better manage their Intensive Care Unit (ICU). With limited manpower, equipment, supplies, and bed/ward availability in hospitals, resource allocation is often an issue that hospitals face. This is especially relevant to ICU department because patients are considered most vulnerable and have highly unpredictable health conditions, i.e. diagnosed most at risk to keep alive. This project attempts to build a model which can better predict individual patient’s in-hospital mortality using different variables / health indicators. By predicting the type of patients who survived and those who do not, the hospital can better allocate available resources and attention to where care is needed most, so that timely action and specific level of care an individual ICU patient requires can be provided.

Deliverables and success criteria:

• Devise an approach to apply various advanced analytical techniques taught

  within the certificate

• Interpret model results and compare your results with existing results by other

  people

• Discuss the work done and its implications
• Identify the limitations in your work and how to address them in future

   

• Problem description:

 

San Francisco International Airport (FAA LID: SFO) is the main international airport serving the city of San Francisco. SFO is the second busiest in California after Los Angeles Airport. Moreover, SFO is the seventh busiest airport in the United States and the 23rd worldwide. In 2018, SFO was admitted the third most delayed airport in the U.S. in terms of arrivals, with about 25 percent of arriving flights delayed. In addition, according to surveys, customer satisfaction of SFO is not very high. With the growing competition in the airport industry, SFO should develop strategies to deal with the existed issues and enhance service quality. In order to deal with issues like high delay rate and low passenger satisfaction, SFO develops strategies according to the analysis of airport and passenger data:

• To achieve a better understanding of the customers' needs to improve customer satisfaction.
• To determine the most reasonable number of gates.
• To predict the monthly passenger flow to optimize airport staff allocation.

   

   

   

  Deliverables and success criteria:

• Devise an approach to apply various advanced analytical techniques taught

  within the certificate

• Interpret model results and compare your results with existing results by other

  people

• Discuss the work done and its implications
• Identify the limitations in your work and how to address them in future

Application (For Stackable Students)

• Participants are only allowed to take the practice module after completing all courses in the Grad Cert.
• Participants who wish to take the practice module concurrently in the same semester with the courses in the same Grad Cert must write to ask-iss@nus.edu.sg citing reasons by the application deadline. Email requests received after the deadline will not be considered. Requests will be reviewed after the deadline and approved on a case-by-case basis.
• Participants who miss the application window will have to apply for the practice module in the next semester.
• Participants who do not attend the briefing will be withdrawn from the practice module.