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 133 credit hours, including all those courses that have been specified as core courses
- Obtained a CGPA of at least 2.00
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 1 |
||
| 1 | CL1000 Introduction to Information and Communication Technology | 0+1 |
| 2 | CS1002 Programming Fundamentals | 3+1 |
| 3 | NS1001 Applied Physics | 3+0 |
| 4 | MT1003 Calculus and Analytical Geometry | 3+0 |
| 5 | SS1012 Functional English | 2+1 |
| 6 | SS1013 Ideology and Constitution of Pakistan | 2+0 |
| Total | 13 + 3 | |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 2 |
||
| 1 | CS1004 Object Oriented Programming | 3+1 |
| 2 | EE1005 Digital Logic Design | 3+1 |
| 3 | MT2008 Multivariable Calculus | 3+0 |
| 4 | SS1007 Islamic Studies/Ethics | 2+0 |
| 5 | SS1014 Expository Writing | 2+1 |
| Total | 13 + 3 | |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 3 |
||
| 1 | CS2001 Data Structures | 3+1 |
| 2 | DS2001 Introduction to Data Science | 3+1 |
| 3 | CS1005 Discrete Structures | 3+0 |
| 4 | MT1004 Linear Algebra | 3+0 |
| 5 | MT2005 Probability and Statistics | 3+0 |
| 6 | CSxxxx Computing Internship | 0+1 |
| Total | 15 + 3 | |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 4 |
||
| 1 | CS2005 Database Systems | 3+1 |
| 2 | DS2003 Advanced Statistics | 3+0 |
| 3 | EE2003 Computer Organization and Assembly Language | 3+1 |
| 4 | CS2004 Fundamentals of Software Engineering | 3+0 |
| 5 | SS/MG Elective - I | 2+0 |
| 6 | SS3002 Civics and Community Engagement | 2+0 |
| Total | 16 + 2 | |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 5 |
||
| 1 | DS2003 Data Warehousing and Business Intelligence | 3+1 |
| 2 | DS3001 Data Analysis and Visualization | 3+1 |
| 3 | CS2006 Operating Systems | 3+1 |
| 4 | CS2009 Design and Analysis of Algorithms | 3+0 |
| 5 | SS2007 Technical and Business Writing | 3+0 |
| Total | 15 + 3 | |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 6 |
||
| 1 | AI2002 Artificial Intelligence | 3+1 |
| 2 | CS3006 Parallel and Distributed Computing | 3+0 |
| 3 | DS3002 Data Mining | 3+0 |
| 4 | DSxxxx DS Elective - I | 3+0 |
| 5 | DSxxxx DS Elective - II | 3+0 |
| Total | 15 + 1 | |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 7 |
||
| 1 | DS4091 Final Year Project - I | 0+3 |
| 2 | CS3001 Computer Networks | 3+1 |
| 3 | DSxxxx DS Elective - III | 3+0 |
| 4 | DSxxxx DS Elective - IV | 3+0 |
| 5 | SS/MG Elective - II | 3+0 |
| Total | 12 + 4 | |
| Sr. No | Course Name | Crdt Hrs. |
|---|---|---|
Semester 8 |
||
| 1 | DS4092 Final Year Project - II | 0+3 |
| 2 | CS3002 Information Security | 3+0 |
| 3 | MG4011 Entrepreneurship | 3+0 |
| 4 | CS4001 Professional Practices | 3+0 |
| 5 | DSxxxx DS Elective - V | 3+0 |
| Total | 12 + 3 | |
| Domain | Cr. Hrs |
|---|---|
| Computing Core | 52 |
| General Education | 30 |
| Maths and Supporting Courses | 12 |
| Domain Core | 21 |
| Domain Elective | 15 |
| Elective Supportive Courses | 3 |
| Total | 133 |
Note 1: Total: 133
Note 2: Registration in “Project-I” is allowed provided the student has earned at least 94 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 |
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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 |
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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 |
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Program Objectives |
PLO |
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