Curriculum


JS9780 Programme Structure - BSc(Hons) in Data Science (BSCHDSF) 數據科學榮譽理學士, offered in 2020-2021 cohort

More and more information are available in the digital society nowadays and new generations should be able to comprehend and utilize them to solve practical problems. Our curriculum does not focus on teaching ‘abstract’ theories but emphasizing in enabling the student’s capability to deliver a complete package of solution to practical problems. To achieve this goal, the program aims to equip our graduates with four main competitive edges.
  • Domain knowledge (e.g. Underlying rationale to determine personal expenditure pattern; Decision-making of an economy as a whole; Concerns in mobile computing, information security and network security)
    • Microeconomics & Macroeconomics
    • Information security
    • Mobile computing
    • Network security
  • Adequate hand-on IT skills for solution developments
    • Visualization: Microsoft Power BI, Google Data Studio, Excel
    • Programing: SAS, Python, R, Matlab, JAVA
    • Machine Learning/Artificial Intelligence: SAS Enterprise Miner, Keras (deep neural network), SPSS, RapidMiner
    • Database: Oracle, MangoDB, SQL, noSQL
    • Distributed system: IBM cloud computing service
  • Statistics and Analytics knowledge
    • High dimensional data analysis
    • Optimization
    • Probability and Expectation
    • Hypothesis testing
    • Time series analysis
    • Regression
  • Effective presentation skills (Writing/Speaking/Visualization)
    • Tailored English writing and presentation skills
    • Effective use of visualization tools
Data science is multi-disciplinary as it draws upon techniques and knowledge in various disciplines. Our curriculum is uniquely designed in the way that courses are integrated from statistics, computing, mathematics and economics disciplines. Graduate project experience forms an integral part of the degree. Students of the same group are expected to develop a practical solution for a real-life problem in their graduate projects where in-depth and specialized domain knowledge will be exchanged among students, professors, and domain experts.

Graduates will be ready for entry-level roles of data scientists, data engineers, and data analysts in commercial firms and public institutions.

Year 1 Curriculum

Core STAT S261F Data Science Fundamentals with Applications
COMP S208F Introduction to Computer Programming
STAT S151F Probability and Distribution
MATH S131F Calculus
Elective ENG English Language Enhancement Course I (Writing)
ENG English Language Enhancement Course II (Presentation)
GEN General Education Course I
GEN General Education Course II

Year 2 Curriculum

Core STAT S263F Big Data in Organization
STAT S251F Statistical Data Analysis
COMP S209F Data Structures, Algorithms And Problem Solving
COMP S203F Intermediate Java Programming and User Interface Design
COMP S202F Java Programming Fundamentals
MATH S262F Linear Algebra
Elective GEN General Education Course I
GEN General Education Course II

Year 3 Curriculum

Core COMP S382F Data Mining and Analytics
STAT S366F SAS Programming
STAT S314F Regression in Practice
STAT S313F High Dimensional Data Analysis
STAT S311F Time Series Analysis
IT S320F Database Management
Elective Select two courses
ECON A332F Applied Business Economics
ECON A231F Introduction to Microeconomics
COMP S413F Development on Mobile Devices
COMP S380F Web Applications: Design and Development
ELEC S425F Computer and Network Security
IT S321F Advanced Database
SCI S330F Scientific Research Methods
STAT S315F Stochastic Process

Year 4 Curriculum

Core COMP S492F Artificial Intelligence/td>
STAT S460F Advanced Topics in Data Mining
STAT S461F Data Science Project
STAT S462F Optimization
COMP S356F Software Engineering and Project Management
COMP S381F Server-Side Technologies and Cloud Computing

Year 3 Entry: BSc(Hons) in Data Science (BSCHDSF) 數據科學榮譽理學士, offered in 2019-2020 cohort

Year 3 students must complete the requirements listed in the following table:

Core STAT S251F Statistical Data Analysis
IT S320F Database Management
COMP S208F Introduction to Computer Programming
STAT S366F SAS Programming
COMP S209F Data Structures, Algorithms, and Problem Solving
STAT S413F High Dimensional Data Analysis
STAT S311F Time Series Analysis and Forecasting
COMP S382F Data Mining and Analytics
ENG English Language Enhancement Course

Year 4 students must complete the requirements listed in the following table:

Core STAT S314F Regression in Practice
STAT S315F Stochastic Processes
STAT S460F Advanced Topics in Data Mining
STAT S461F Data Science Project - 2 terms
COMP S492F Artificial Intelligence
STAT S462F Optimization
Elective Course

Year 3 Entry: BSc(Hons) in Statistical Analysis and Data Science (BSCHSADSF), offered in 2018-2019 cohort

Year 3 students must complete the requirements listed in the following table:

Core MATH S201F Finite Mathematics for Business
ENGL S250F   English Writing for Workplace Communication
STAT S310F Statistical Methods for Investment (CEF)
STAT S311F Time Series Analysis with Applications (CEF)
STAT S312F Design and Analysis of Experiments
STAT S314F Statistical Methods for Economics and Finance
STAT S413F Multivariate Statistical Analysis for Data science
Elective
(1 out of 3)
STAT S330F Quantitative Research Methods
COMP S202F Java Programming Fundamentals
STATS251F Statistical Analysis for Testing & Certification

Year 4 students must complete the requirements listed in the following table:

Core STAT S315F Applied Probability Models for Investment (CEF)
STAT S318F Statistical Methods for Data Mining
STAT S410F Quantitative Research Project
STAT S411F Statistical Computing Project
IT S320F Database Management
STAT S460F Advanced Topics in Data Mining
Elective
(2 out of 4)
STAT S316F Decision Analysis
STAT S319F Credit Risk Analysis
MATH S390F Quantitative Models for Financial Risk (CEF)
MATH S391F Mathematical Methods for Finance

*CEF means 'Continuing Education Fund' approved course.