Fast-track Programme - DSA

Fast-track Integrated Bachelor’s and Master’s Degree Programme

The fast-track programme offers a unique opportunity for high-achieving students to complete both undergraduate and taught postgraduate studies in a shorter timeframe while benefiting from reduced tuition fees. Students can enrol in the programme either in Year 1 or Year 3, depending on their academic standing. Typically, students complete their studies within an additional year beyond the undergraduate normal study duration, with the added advantage of being able to double-count up to 30% of the credits from the taught postgraduate programme towards their undergraduate degree. Upon successful completion of this fast-track programme, graduates will receive two parchments, enhancing their qualifications and career prospects as data analysts in various competitive industries, including finance, information technology, marketing and research, consultancy, healthcare, business retail, urban planning, government, and public utilities.

Undergraduate Programme	Taught Postgraduate Programme
BSc (Hons) in Data Science and Analytics *	MSc in Data Science and Analytics *

*BSc DSA and MSc DSA are currently offered under the Department of Applied Mathematics (AMA). They will be hosted by the Department of Data Science and Artificial Intelligence (DSAI) with effect from 2025/26 academic year.

Entry Path and Admission Requirements

Year 1 Entry

JUPAS (Local): At least Level 5** in 2 subjects and with HKDSE Best 5 score of 26 or above (Conversion scale: 5**=8.5; 5*=7; 5=5.5; 4=4);
Non-JUPAS (Local) and Non-JEE (Mainland): IB score of 39 or above (or equivalent);
JEE (Mainland): Total score within the Top 3% of the province of the examination year

Year 3 Entry

Attained a cumulative GPA (CGPA) of 3.30 or above at the end of Semester 2 of Year 2, and
Recommended by the Programme Leader

**** Maintain Enrollment in the Fast-track Programme****

Students are required to maintain a CGPA of 3.30 or above to stay on the Fast-track programme.

Students enrolled in the Fast-track programme can normally complete both undergraduate and taught postgraduate programmes in full-time mode within 5 years.

4 years for BSc (Hons) in Data Science and Analytics + 1 year for MSc in Data Science and Analytics

Students in the fast-track programme can take up to 9 credits (30% of the credit requirement for the TPg curriculum) from the following Level 5 subjects within their Ug curriculum as alternatives to the corresponding undergraduate subjects. These credits will also contribute towards the taught postgraduate programme award.

Below are details of how Level 5 subjects, available to students in the Fast-track programme, can substitute the original subjects in the undergraduate curriculum.

Level 5 subjects #	Credits	Original Ug subjects to be replaced	Credits
AMA505 Optimization Methods	3	AMA4850 Optimization Methods	3
AMA563 Principles of Data Science	3	Elective	3
AMA546 Statistical Data Mining AND / OR AMA564 Deep Learning	3	Elective	3

# Remarks:

Students is expected to complete AMA505 Optimization Methods and will be recognized as having fulfilled the requirement for the DSR compulsory subject AMA4850 Optimization Methods. Students are no longer required to enrol AMA4850.
AMA546, AMA563, AMA564 will be included as elective subjects for students admitted to the fast-track programme.
Students have to enrol in AMA505, and select up to 2 elective subjects amongAMA546, AMA563 and AMA564 (AMA563 is the prerequisite subject of AMA546 & AMA564).

For further details of the programme requirements, please refer to the PRD below.

Enquiries

For further programme information, please reach out to us through the channels listed below:

Ug Programme

TPg Programme

Contact information:

Address: A529, 5/F, Chung Sze Yuen Building, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

Email: dsai.ug@polyu.edu.hk

Contact information:

Address: A529, 5/F, Chung Sze Yuen Building, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

Email: dsai.tpg@polyu.edu.hk