HKU HKU Dept of Statistics & Actuarial Science, HKU
   
 

Master of Data Science (MDASC)

 Graduate Statistician (GradStat) and Quality Mark of Royal Statistical Society (RSS)

 

The University of Hong Kong (HKU) has been awarded the status of an Accredited University by the Royal Statistical Society (RSS) since December 2023. The RSS accreditation provides reassurance that the teaching, learning and assessment within the accredited programme is of high quality and meets the needs of students and employers.

Upon completion of the Master of Data Science (MDASC) programme at HKU and application to RSS via the standard route, graduates are qualified to become a Graduate Statistician (GradStat) designated by RSS (link).

In addition, the RSS has awarded individual HKU courses with the RSS Quality Mark (link), a recognition that these courses teach good statistical literacy. Students who have passed these courses are deemed to have met the academic requirements of the RSS Data Analyst award (link).

 

 Courses

  • MDASC add/drop period:
    September 3 to 17, 2024 (for Semester 1 & Semester 2),
    January 21 to February 11, 2025 (for Semester 2 & Summer Semester),
    June 2 to 8, 2025 (Summer Semester)

  • Timetable for 2024-25 (Updated on October 18, 2024)

  • DASC7600 Data Science Project (12 credits) (June 2024 – Semester 1, 2024-25) (Updated on April 15, 2024)

  • DASC8088 Data Science Practicum (6 credits) (starting from the Summer Semester of 2024-25) (Updated on August 6, 2024)

 

 Regulations and Syllabuses

 

 Prizes/ Scholarships

*To be approved by the University

List of Awardees (Updated on June 28, 2024)

 

 Continuing Education Fund (CEF)

  • COMP7503
Multimedia Technologies(42Z123846) [Effective from January 2020]
  • COMP7506
Smart Phone Apps Development(42Z123870) [Effective from January 2020]
  • COMP7507
Visualization and Visual Analytics(42Z12379A) [Effective from January 2020]
  • COMP7906
Introduction to Cyber Security(42Z123706) [Effective from January 2020]
  • STAT6013
Financial Data Analysis (42Z153761)
  • STAT7008
Programming for Data Science (42Z15377A)
  • STAT8003
Time Series Forecasting (42Z153788)
  • STAT8017
Data Mining Techniques(42Z152706)
  • STAT8019
Marketing Analytics(42Z152714)
 

 Student Feedback Channels

The University strives to provide quality education and training to students, and feedback from students is valuable for the evaluation of the result of the endeavor. The Student Feedback on Teaching and Learning (SFTL), and Student Learning Experience Questionnaire (SLEQ) allow students to rate and comment on the courses they take near the end of the courses. To further enhance communication between teaching staff and students, the Department also supports the half-yearly Staff-Student Consultative Committee (SSCC) meetings, during which student course representatives and respective teaching staff exchange their views on certain aspects of the courses concerned. All feedback from students are collected anonymously.

 

 
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