SDST1018 Foundations of data science (6 credits) Academic Year 2025
Offering Department SCDS (Department of Statistics and Actuarial Science) Quota 80
Course Co-ordinator Dr E A L Li, SCDS (Department of Statistics and Actuarial Science) < ericli11@hku.hk >
Teachers Involved (Dr E A L Li,Statistics & Actuarial Science)
Course Objectives - The course introduces basic concepts and methodology of data science to junior undergraduate students. The teaching is designed at a level appropriate for all undergraduate students with various backgrounds and without pre-requisites.
- Students will engage in a full data work-flow including collaborative data science projects. They will study a full spectrum of data science topics, from initial investigation and data acquisition to the communication of final results.
- Specifically, the course provides exposure to different data types and sources, and the process of data curation for the purpose of transforming them to a format suitable for analysis. It introduces elementary notions in estimation, prediction and inference. Case studies involving less-manicured data are discussed to enhance the computational and analytical abilities of the students.
Course Contents & Topics - Data management and exploration
* Computational thinking: Coding without computers
* Data visualisation with Tableau
* Machine Learning: Supervised Learning vs Unsupervised Learning
* Supervised Learning: Linear regression in Microsoft Excel
* Evaluation of Model: Overfitting & Underfitting

- Data analytics
* Statistics (1): data visualization and data exploratory analysis
* Statistics (2): random variables and probability
* Statistics (3): estimation of mean and variance, distributions, confidence interval and independent samples
* Statistics (4): hypothesis testing with p-value
* Statistics (5): regression models for forecasting
Course Learning Outcomes
On successful completion of this course, students should be able to:

CLO 1 Explore and wrangle over data; summarize and visualize data.
CLO 2 Apply exploratory data analysis techniques to gain insights into the data and identify patterns, trends, and outliers.
CLO 3 Formulate real life problems in a mathematical setting to bring out elementary concepts in estimation, prediction, and inference.
CLO 4 Work collaboratively in a team to design and implement a data science project, from problem formulation to data analysis and presentation of findings.
Pre-requisites
(and Co-requisites and
Impermissible combinations)		 Not for students who have passed or already enrolled in any of the following courses: SDST1005, SDST1015, SDST1016; and  
Not for Year 2 or above BSc(ActuarSc), and BEng(CompSc), BEng(AI&DataSc) and BEng(DS&E) students; and
Not for Year 2 or above students majoring in Computer Science/Decision Analytics/Risk Management/Statistics; and
Not for Year 4 or above students from any curriculum.
Only for students admitted in 2025 and thereafter.
Course Status with Related Major/Minor/Professional Core 2025 Minor in Statistics ( Disciplinary Elective )
Course to PLO Mapping
Offer in 2025 - 2026 Y        2nd sem    Examination No Exam     
Offer in 2026 - 2027 Y
Course Grade A+ to F
Grade Descriptors
A Demonstrate thorough mastery at an advanced level of extensive knowledge and skills re- quired for attaining all the course learning outcomes. Show strong analytical and critical abilities and logical thinking, with evidence of original thought, and ability to apply knowledge to a wide range of complex, familiar and unfamiliar situations. Apply highly effective organizational and presentational skills.
B Demonstrate substantial command of a broad range of knowledge and skills required for attaining at least most of the course learning outcomes. Show evidence of analytical and critical abilities and logical thinking, and ability to apply knowledge to familiar and some unfamiliar situations. Apply effective organizational and presentational skills.
C Demonstrate general but incomplete command of knowledge and skills required for attaining most of the course learning outcomes. Show evidence of some analytical and critical abilities and logical thinking, and ability to apply knowledge to most familiar situations. Apply moderately effective organizational and presentational skills.
D Demonstrate partial but limited command of knowledge and skills required for attaining some of the course learning outcomes. Show evidence of some coherent and logical thinking, but with limited analytical and critical abilities. Show limited ability to apply knowledge to solve problems. Apply limited or barely effective organizational and presentational skills.
Fail Demonstrate little or no evidence of command of knowledge and skills required for attaining the course learning outcomes. Lack of analytical and critical abilities, logical and coherent thinking. Show very little or no ability to apply knowledge to solve problems. Organization and presentational skills are minimally effective or ineffective.
Communication-intensive Course N
Course Type Lecture-based course
Course Teaching
& Learning Activities
Activities Details No. of Hours
Group work 32.0
Lectures 36.0
Project work 42.0
Tutorials 10.0
Reading / Self study 60.0
Assessment Methods
and Weighting
Methods Details Weighting in final
course grade (%)		 Assessment Methods
to CLO Mapping
Presentation 30.0 1,2,3,4
Project reports 20.0 1,2,3,4
Test Two tests 50.0 1,2,3
Required/recommended reading
and online materials		 A Step-by-step Guide for University Students - Tableau Made Easy.  Natalie Wong & Rachel Lui, 2023.  Will be made available on Moodle.
Course Website http://moodle.hku.hk
Additional Course Information Teaching and Assessment  

This course uses problem-based, information acquisition, innovation, collaborative, and peer learning teaching methods.  Teaching is made up of a three-hour lecture and a one-hour tutorial per week. Teaching materials will be uploaded to the course Moodle for reference and review. Full attendance in lectures and tutorials are expected. Student engagement is expected via class participation and email communication.  

Assessment includes two class tests (50%), and a group project (50%).  Unless an acceptable reason is given, penalty will be applied to any late submission of the project. Partially or wholly copied work in the project will be penalized and/or reported as plagiarism.