BS (Data Science)


Program Info
Learning Outcomes
By the time of graduation, the students develop an ability to:
  1. Apply knowledge of computing and mathematics that is appropriate for the program.
  2. Analyse a problem and define computing requirements that are appropriate to its solution.
  3. Design, implement, and evaluate a computer-based system, process, component or program to meet desired needs.
  4. Work in a team to accomplish a common goal.
  5. Understand professional, ethical, and social issues and responsibilities.
  6. Communicate effectively with different audiences.
  7. Learn programming for large-sized datasets
  8. Identify useful and hidden patterns from data.
  9. Improve decision making skills by mining data from various aspects.
  10. Solve real world problems by applying mathematical and computational approaches.
  11. Change the world for the better – in areas like healthcare, transportation, and education etc.
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

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
  • Should have EITHER
    • studied Mathematics at the HSSC or equivalent level. OR
    • pass HSSC level Mathematics exam within one year of admission, conducted by any one of the following:
      • Local Board of Intermediate & Secondary Education
      • A recognized Foreign Board (Oxford, Cambridge, etc.)
      • NUCES (FAST)

Selection Criteria:

  • 50% weight to higher percent score of SSC (or an equivalent exam) OR HSSE (or an equivalent exam) AND
  • 50% weight to marks obtained in Admission Test
Candidates having taken NTS-NAT IE exam
  • Cut-off marks in the NTS-NAT IE exam to be determined by the University
Candidates having taken SAT examination
  • Combined score of 1,000 or more in the SAT-I examination AND
  • At least 550 in the SAT-II (Math Level IIC) examination.
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).