Bachelor of Science (Data Science)
Those who have taken the HSSC or an equivalent examination and are awaiting result are also eligible to apply. The four-year undergraduate programs of full time study are divided into eight semesters.
For the award of BS (Data Science) degree, a student must have:
- Passed courses with a total of at least 132 credit hours, including all those courses that have been specified as core courses
- Obtained a CGPA of at least 2.00
Offered Campuses
| Chiniot-Faisalabad | Islamabad | Karachi | Lahore | Peshawar |
|---|---|---|---|---|
Eligibility:
- At least 60% marks in SSC (Matric) or an equivalent examination (such as O-levels) AND
- Should have studied for HSSC or an equivalent qualification, for at least two years AND
- At least 50% marks in HSSC or an equivalent qualification AND
- studied Mathematics at the HSSC or equivalent level.
Selection Criteria:
- 50% weight to marks obtained in Admission Test AND
- 10% weight to higher percent score of SSC (or an equivalent exam) AND
- 40% weight to higher percent score of HSSC (or an equivalent exam)
-
Weightage of HSSC marks shall be calculated based on (which ever is applicable) at the time
of compilation of merit list
- HSSC part I and II OR
- HSSC part I if HSSC part II not available OR
- IBCC equivalence of A-level OR
- IBCC equivalence of O-level
- Cut-off marks in the NTS-NAT IE exam to be determined by the University
NOTE:All documents/transcripts will be checked at the time of admission.
Any
incorrect/false information submitted by the applicant or any attempt to hide information
will lead to disqualification of the candidate.
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 1 |
||
| 1 | Introduction to ICT | 0+1 |
| 2 | Programming Fundamentals | 3+1 |
| 3 | Linear Algebra | 3+0 |
| 4 | Calculus & Analytical Geometry | 3+0 |
| 5 | Pakistan Studies | 3+0 |
| 6 | English Composition & Comprehension | 2+1 |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 2 |
||
| 1 | Object Oriented Programming | 3+1 |
| 2 | Digital Logic Design | 3+1 |
| 3 | Differential Equations | 3+0 |
| 4 | Islamic Studies/Ethics | 3+0 |
| 5 | Communication & Presentation Skills | 2+1 |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 3 |
||
| 1 | Introduction to Data Science | 3+0 |
| 2 | Data Structures | 3+1 |
| 3 | Discrete Structures | 3+0 |
| 4 | Computer Organization & Assembly Language | 3+1 |
| 5 | Probability & Statistics | 3+0 |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 4 |
||
| 1 | Advanced Statistics | 3+0 |
| 2 | Fundamentals of Big Data Analytics | 3+1 |
| 3 | Fundamentals of Software Engineering | 3+0 |
| 4 | Database Systems | 3+1 |
| 5 | University Elective I | 3+0 |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 5 |
||
| 1 | Data Warehousing & Business Intelligence | 3+0 |
| 2 | Data Analysis & Visualization | 3+1 |
| 3 | Design & Analysis of Algorithms | 3+0 |
| 4 | Technical & Business Writing | 3+0 |
| 5 | Operating Systems | 3+1 |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 6 |
||
| 1 | Data Mining | 3+1 |
| 2 | Parallel & Distributed Computing | 3+0 |
| 3 | University Elective II | 3+0 |
| 4 | Data Science Elective – I | 3+0 |
| 5 | Artificial Intelligence | 3+1 |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 7 |
||
| 1 | Final Year Project-I | 0+3 |
| 2 | Information Security | 3+0 |
| 3 | Professional Practices | 3+0 |
| 4 | Data Science Elective-II | 3+0 |
| 5 | Computer Networks | 3+1 |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 8 |
||
| 1 | Final Year Project-II | 0+3 |
| 2 | Data Science Elective-III | 3+0 |
| 3 | Data Science Elective-IV | 3+0 |
| 4 | University Elective III | 3+0 |
Note: Registration in “Project-I” is allowed provided the student has earned at least 100 credit hours, and his/her CGPA is equal to or greater than the graduating CGPA (2.0).
Program Educational Objectives (PEO)
- Fundamental Computing and Data Science Knowledge - A graduate who is performing his/her professional roles with understanding of fundamental computing and data science knowledge acquired during his/her studies.
- Ethical and Societal Responsibilities - A graduate who is fulfilling his/her professional responsibilities taking into account ethical and societal concerns with special emphasis to data protection and usage.
- Communication Skills - A graduate who is effective in oral and written communication of technical and managerial information.
- Leadership - A graduate who is effective in a leadership role of a group/team assigned to him/her or in an entrepreneurial environment.
- Continuous Improvement - A graduate who keeps on exploring new fields and areas in data science for his/her organization or conduct research for academic pursuits.
Program Learning Outcomes (PLOs)
|
1 |
Academic Education |
Completion of an accredited program (BS DS) of study designed to prepare graduates as computing professionals |
|
2 |
Knowledge for Solving Computing Problems |
Apply knowledge of computing fundamentals, knowledge of a computing specialization, and mathematics, science, and domain knowledge appropriate for the computing specialization to the abstraction and conceptualization of computing models from defined problems and requirements |
|
3 |
Problem Analysis |
Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines |
|
4 |
Design/ Development of Solutions |
Design and evaluate solutions for complex computing problems, and design and evaluate systems, components, or processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations |
|
5 |
Modern Tool Usage |
Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations |
|
6 |
Individual and Team Work |
Function effectively as an individual and as a member or leader in diverse teams and in multi-disciplinary settings |
|
7 |
Communication |
Communicate effectively with the computing community and with society at large about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions |
|
8 |
Computing Professionalism and Society |
Understand and assess societal, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practice |
|
9 |
Ethics |
Understand and commit to professional ethics, responsibilities, and norms of professional computing practice |
|
10 |
Life-long Learning |
Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional |
|
Program Objectives |
PLO |
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