GSSP Courses
Check out the 2025 GSSP Course Schedule
Courses offered for 2026 GSSP
*Finalised course offerings will be updated at the end of December!*
- All courses are taught on-site (no online option) and conducted in English.
- Courses are taught on a graded basis and grade(s) will be reflected on your transcript.
- The Faculty of Science reserves the right to cancel any course, if there is insufficient enrolment to start a class.
- Courses and curriculum listed below may be subject to changes.
- NUS students can read up to 2 courses.
- All SP2718 courses can only fulfil Unrestricted Electives (UE).
- International students can read up to 4 units of courses.
DSA1361 Introductory Data Science with Python and Tableau (2 units)
Introductory Data Science with Python and Tableau
This course will provide participants with a foundation on what data science is and will focus on linking business questions to statistical techniques, and linking analytical results to business value. Participants will learn how to make sense of data using simple statistical techniques and how best to visualize data. Tableau for data visualisation and presentation, and Python for data analysis will be introduced in the class.
(Can be read together with DSA2362)
Course summary
This course will provide participants with a foundation on what data science is. There will be a focus on linking business questions to statistical techniques, and linking analytical results to business value. By the end of the course, participants will know how to make sense of data using simple statistical techniques and how best to visualize data. Two software that are very widely used in the data science industry will be introduced in this class: Tableau for data visualisation and presentation, and Python for data analysis.
Syllabus
1. Ideas for data visualisation. In this topic we cover some general recommendations when making visualisations. For instance, we discuss the use of colours, types of plots, good graphics and bad graphics.
2. Methods for Data Visualisation. We introduce Tableau; an intuitive software for creating multivariate interactive graphics.
3. Exploratory Data Analysis. We introduce data summaries,transformations, outlier inspection and other such tools to understand the data we have before we proceed to a deeper analysis.
4. Hypothesis testing. Tests based on linear models (t-tests, ANOVA) will be used to introduce the concepts of hypothesis testing and statistical logic.
5. Linear regression. We introduce the assumptions behind the linear regression model. We then demonstrate model fitting and residual analysis to fully comprehend the model and analysis.
6. Topics 3 to 6 will be covered in Python, through the use of Jupyter notebooks, which are widely used in the data science industry. This will enable students to easily pick up and use source code from repositories such as github and bitbucket. Thus, the course will also introduce git – the version control software that is used by almost all data scientists.
Preferred basic knowledge
NIL
Assessments
Quizzes/ Tests: 60% (3 quizzes, 20% each)
Project/ Group Project: 30%
Class Participation: 10%
Prerequisites and preclusions (for NUS students)
NIL
Instructor
Dr Chan Yiu Man
DSA2362 Decision Trees for Machine Learning and Data Analysis (2 units)
Decision Trees for Machine Learning and Data Analysis
Decision tree methods predict the value of a target variable by learning simple decision rules from the data. Use real data to compare the strengths and weaknesses of decision tree models with those obtained by linear and logistic regression and discriminant analysis. Possible applications include economic surveys, credit card data, vehicle crash tests data and precision medicine.
(Can be read together with DSA1361)
Course summary
Decision tree methods predict the value of a target variable by learning simple decision rules from the data. In this course, participants will learn decision tree methods and how to use software to build predictive models and score variables in terms of their importance. They will use real data to compare the strengths and weaknesses of decision tree models with those obtained by linear and logistic regression and discriminant analysis. Participants will also learn how to handle data with missing values without requiring prior imputation. Possible applications include economic surveys, credit card data, vehicle crash tests data and precision medicine.
Preferred basic knowledge
- Enrolled students should bring a laptop to use during class.
- Those without prior experience with using R (write functions, install and use packages) will be asked to take a few DataCamp R courses to pick up such necessary skills. DataCamp courses are online courses.
Assessments
Class Participation: 10%
Essay: 15%
Quizzes/Tests: 75% (3 tests, 25% each)
Prerequisites and preclusions (for NUS students)
Prerequisite: DSA1361 or department approval (Can be read together with DSA1361)
Instructor
Prof Loh Wei-Yin
FSC2101 Forensic Science (4 units)
Forensic Science
Crime is one feature of human behaviour that fascinates our community. How crimes impact our society and how crimes are investigated and solved in the Singapore context is the focus of the course. The course is designed to enable students to appreciate why and how crimes are committed, to understand how crimes are solved in Singapore using investigative, and the latest scientific and forensic techniques, and to learn the role of the major stakeholders in the Criminal Justice System. Experts from law, pharmacy, statistics, the Health Sciences Authority and the Singapore Police Force will cover topics related to forensic science.
More course details will be updated in due time.
FSC4203 Forensic Toxicology and Poisons (4 units)
Forensic Toxicology and Poisons
Ever wondered how much of the coffee you consumed is subsequently metabolised? Find out using forensic toxicology! This multidisciplinary course aims to support medical and legal investigations into the cause of death, poisoning and adverse responses to substances. Drawing from foundational principles in toxicokinetics, participants will be able to: study the physicochemical properties of substances and their effect(s) on the host; and consider the toxicological outcomes of exposure due to the unique handling of substances by organ systems. The lectures will conclude with real-life applications led by practitioners.
Preferred basic knowledge
Forensic science, pharmacy, pharmacology, law and/or chemistry
Assessments
Practical report: 10 %
Assignments: 25 %
Class participation: 25 %
Moot Court (Team presentation) based on CSI Practical: 15%
Oral viva: 25 %
Prerequisites and preclusions (for NUS students)
Prerequisite: FSC2101/LSM1306 or department approval
Preclusion: LSM4211 or SP4263
Instructors
Prof Stella Tan
Prof Ho Han Kiat
Prof Koh Hwee Ling
Dr Shawn Lee
