|Offered to students admitted to Year 1 in||ALL|
|Offer in 2023 - 2024||Y N|
|Enquiry for Course Details|
|STAT1016 Data science 101 (6 credits)||Academic Year||2023|
|Offering Department||Statistics & Actuarial Science||Quota||150|
|Course Co-ordinator||Dr E K F Lam, Statistics & Actuarial Science < firstname.lastname@example.org >|
(Dr E K F Lam,Statistics & Actuarial Science)
(Dr R K W Lui,Faculty of Science)
- 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:
(and Co-requisites and
Not for students who have passed or already enrolled in any of the following courses: STAT1005, STAT1015, STAT1018; and
This course is exclusive for BASc and BA(HDT) students.
|Course to PLO Mapping|
|Offer in 2023 - 2024||Y 2nd sem||Examination||No Exam|
|Offer in 2024 - 2025||Y|
|Course Grade||A+ to F|
|Course Type||Lecture-based course|
& Learning Activities
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.|
|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.
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