Bachelor of Science (Data Science)

Program Info
Award of Degree

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


  • 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
Candidates having taken NTS-NAT IE or NAT ICS exam

  • Cut-off marks in the NTS-NAT IE exam to be determined by the University
Tentative Study Plan
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)

  1. 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.
  2. 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.
  3. Communication Skills - A graduate who is effective in oral and written communication of technical and managerial information.
  4. Leadership - A graduate who is effective in a leadership role of a group/team assigned to him/her or in an entrepreneurial environment.
  5. 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. Computing and Data Science Knowledge Apply knowledge of mathematics, statistics, natural sciences, computing fundamentals, and a data specialization to the solution of complex data science problems.
  2. Problem Analysis Identify, formulate, research literature, and analyze complex data problems, reaching substantiated conclusions using first principles of mathematics, statistics, natural sciences, computing and data sciences.
  3. Design/Develop Solutions Design solutions for complex data science problems and design systems, components, and processes that meet specified needs with appropriate consideration for public health and safety, cultural, societal, and environmental considerations.
  4. Investigation & Experimentation Conduct investigation of complex data science problems using research based knowledge and research based methods.
  5. Modern Tool Usage Create, select, and apply appropriate techniques, resources and modern data science tools, including prediction and modelling for complex data science problems.
  6. Society Responsibility Apply reasoning informed by contextual knowledge to assess societal, health, safety, legal, and cultural issues relevant to context of complex data science problems.
  7. Environment and Sustainability Understand and evaluate sustainability and impact of data professional work in the solution of complex data science problems.
  8. Ethics Apply ethical principles and commit to professional ethics and responsibilities and norms of computing practice.
  9. Individual and Team Work Function effectively as an individual, and as a member or leader in diverse teams and in multi-disciplinary settings.
  10. Communication Communicate effectively on complex data science activities with the data professionals’ community and with society at large.
  11. Project Management and Finance Demonstrate knowledge and understanding of management principles and economic decision making and apply these to one's own work as a member or a team.
  12. Life Long Learning Recognize the need for, and have the preparation and ability to engage in independent and life-long learning in the broadest context of technological changes.