Programme Learning Outcomes (PLOs) - BSc Data Science
The BSc in Data Science fully ensures that students achieve mastery of the Programme Learning Outcomes (PLOs) by the end of the program, which aligns with the Vision and Mission of the College of Science and Sultan Qaboos University (SQU). The PLOs are clearly defined and mapped to the SQU graduate attributes and OQF Characteristics, by ensuring that graduates develop the necessary knowledge, skills, and competencies expected at OQF Level 8. Throughout the curriculum, courses are designed to progressively build expertise in data science methodologies, statistical modelling, and computational problem-solving, enabling students to apply these skills in real-world contexts. The alignment of the PLOs with the OQF Characteristics is systematically demonstrated in Table 1, and ensures that the programme meets national academic standards while preparing graduates for research, innovation, and professional leadership in the field of data science.
Alignment of Program Learning Outcomes (PLOs) with SQU Graduate Attributes (GA) and OQF Characteristics
|
No.
|
Program Learning Outcomes (PLOs)
|
Graduate Attributes (GA)
|
Oman Qualification Framework (OQF) Characteristics
|
|
1
|
Demonstrate knowledge of the fundamental dynamics and evolution of data science discipline and its anchor in statistics
|
A
|
K
|
|
2
|
Integrate multidisciplinary knowledge to develop problem-specific models to draw data-driven decisions.
|
A, B
|
K, S
|
|
3
|
Develop the ability to manage and process diverse data, ensuring efficient handling of large datasets using computational tools for data science applications.
|
B, F
|
S, E
|
|
4
|
Acquire the skill to handle and analyze diverse data from multiple sources and formats, adapting to various applications and data scales in data science projects.
|
B, F
|
S E
|
|
5
|
Implement algorithms fundamental to data science tasks such as data mining and statistical inference, using high-level computing technologies to develop packages that solve practical statistical problems.
|
A, B, F
|
K, S, E
|
|
6
|
Derive Insights from data visualization and meaningful patterns to facilitate evidence-based decision-making.
|
A, B
|
S, C
|
|
7
|
Communicate data-driven insights effectively, and to engage with research and lifelong learning activities in the field of statistical data science.
|
C, D, F
|
C, A, L
|
|
8
|
Apply ethical standards in data science by ensuring privacy protection and fairness in data collection, processing, and reporting.
|
E
|
A, E
|
OQF Characteristics
K. Knowledge
S. Skills
C. Communication, Numeracy, and Information and Communication Technology Skills.
A. Autonomy and Responsibility
E. Employability and Values
L. Learning to learn
Graduate Attributes
- Cognitive Capabilities: The graduate has sufficient general and specialized theoretical knowledge that enables him/her to deal well with his/her specialty and other related fields.
- Skill and Professional Capability: The graduate has sufficient skill and practical experience that enables him/her to perform all tasks related to the specialization and other related fields.
- Effective Communication: The graduate has the ability to communicate effectively with others to achieve the desired results
- Autonomy and Leadership: The graduate has the ability to lead, make decisions and take responsibility for decisions.
- Responsibility and Commitment: The graduate appreciates the importance of available resources and deals with them effectively and is committed to the ethics of the profession and society.
- Development and Innovation: The graduate has a passion for development and innovation in the field of specialization.