Course co-ordinator(s): Dr Rosalind Deena Kumari (Malaysia).
Aims:
The primary objectives of student industrial training are to enable aspiring graduates to experience the real working environment, increasing their cognizance on work ethics and organizational behavior along with an opportunity to relate the classroom knowledge to various practices in industry. The student industrial training programme creates an awareness among the students on present skills and practices there by improving their employability. This course also intends to create a symbiotic relationship between various industries and the university.
Detailed Information
Course Description: Link to Official Course Descriptor.
Pre-requisites: none.
Location: Malaysia.
Semester: 3.
Syllabus:
- Lecture and meeting by the industrial supervisor
- Work assigned by the industrial supervisor
- Log book
- Report writing
Learning Outcomes: Subject Mastery
1) Perform in a team within a company in which computational knowledge and skills in data science can be integrated with other types of expertise.
2) Value professional attitude and create solutions to problems associated with a specific data science project.
Learning Outcomes: Personal Abilities
Present reports to an audience with a different background appropriate for a professional environment adapting their computational knowledge and practical skills.
Assessment Methods: Due to covid, assessment methods for Academic Year 2021-22 may vary from those noted on the official course descriptor. Please see the Computer Science Course Weightings and the Maths Course Weightings for 2020-21 Semester 1 assessment methods.
SCQF Level: 10.