Programme Structure

Credit requirements: 44 units

 

(i)    Core Courses: 24 units (Code: T)     

(a)

CDS501/4 – Principles and Practices of Data Science and Analytics

(b)

CDS502/4 – Big Data Storage and Management

(c)

CDS503/4 – Machine Learning

(d)

CDS504/4 – Enabling Technologies and Infrastructures for Data Science

(e)

CDS505/4 – Data Visualisation and Visual Analytics

(f)

CDS506/4 – Research, Consultancy and Professional Skills

 

(ii)   Elective Courses: 12 Units (Code: E)

        Choose any three (3) courses from the table below:

 

Business Analytics

(a)

CDS511/4  –   Consumer Behavioural and Social Media Analytics

(b)

CDS512/4  –   Business Intelligence and Decision Analytics

(c)

CDS513/4  –   Predictive Business Analytics

Multimodal Analytics

(a)

CDS521/4  –    Multimodal Information Retrieval

(b)

CDS522/4  –    Text and Speech Analytics

(c)

CDS523/4  –    Forensic Analytics and Digital Investigations

 

 (iii)  Project (Core): 8 units (Code: T)

         CDS590 – Consultancy Project and Practicum

This experiential work-based learning course prepares students to be a data scientist/analytics consultant by enhancing the students’ knowledge and skills in research, planning and implementation of a consultancy project in the field of data science/analytics, which can be applied to real-life situation.  Students are required to complete the practicum at their respective workplace or their chosen/assigned organisation. Students work under the supervision of a lecturer and an industry mentor. The students are required to solve a real-world problem or tap opportunities related to data science and analytics during their practicum. The prerequisite of this course is CDS506 which must be taken in the preceding semester. The students are required to secure practicum placement together with project proposal during CDS506.