Eligibility Criteria

• FA/F. Sc or Equivalent qualifications with at least second division, securing 50% marks in aggregate.
• The students who have not studied Mathematics at intermediate level must pass deficiency courses of Mathematics of 6 credit hours within one year of their regular studies.
• Qualifying for the admission test and interview is compulsory. A candidate scoring less than 40% marks in the test and interview will stand disqualified for admission.
• Candidates who have secured at least 40% in the NTS-NAT are also eligible to apply.
• The merit of a candidate shall be measured by a 50 % weight age to the marks obtained in HSC or equivalent, 40 % to the marks obtained in the written test, and 10% to the marks obtained in the interview.
• A candidate shall be given a special credit of thirty marks for admission in each program mentioned above if he/she has studied Computer Science and/or statistics at intermediate level (for BS-Data Science program only) at intermediate level or has done A level.
• The Hafiz Quran shall be given a special credit of 20 marks.
• The credit marks shall be added to the marks obtained at HSC or equivalent, subject to fulfilment of basic eligibility criteria of 50% marks.

Degree Requirements

For a BS-Data Science 4-year degree, a student is required to complete a minimum of 130-140 credit hours including a 6-credit hour research thesis/project. The normal duration for completion of BS-Data Science degree is 8 semesters over a period of 4 years. The maximum duration for obtaining BS-Data Science degree shall be 7 years.

Program Educational Objectives (PEOs) for BS-Data Science

PEO 1: To Enable a competitive learning environment to encourage critical thinking for evidence-based policy making and to develop students’ data analytical, programming and machine learning skills by providing them theoretical and practical knowledge of data analysis, data analysis software’s and computer language applicable in wide variety of domains.
PEO 2: To groom its students to communicate effectively, demonstrate leadership qualities and professional integrity.
PEO 3: To inculcate the ability in its students to continue enhancing their computing knowledge and skills after graduation and excel in their careers as researchers, professionals, and entrepreneurs.
PEO 4: To groom its students to be effective, after graduation, in society and diverse professional environments maintaining high ethical standards.

Graduate Attributes (GAs) for BS Data Science Program

The Graduates Attributes (GAs) are exemplars of the qualities and attributes expected of a graduate from an accredited program. Graduates Attributes (GAs) are the components indicative of the graduate’s potential to acquire competence to practice at the appropriate level.
The following GAs for undergraduate computing programs has been adopted from the Seol Accord as recommended by the National Computing Education Accreditation Council (NCEAC).

GA 1:  Gain an understanding of the underpinning theories of fundamental principles and technologies within the area of computer science (Academic education).
GA 2:  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 (Knowledge for Solving Computing Problems).
GA 3:  Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines (Problem Analysis).
GA 4:  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 (Design/ Development of Solutions)
GA 5:  Create, select, adapt, and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations (Modern Tool Usage)
GA 6:  Function effectively as an individual and as a member or leader in diverse teams and in multi-disciplinary settings (Individual and Teamwork)
GA 7:  Communicate effectively with the computing community and with society about complex computing activities by being able to comprehend and write effective reports, design documentation, make effective presentations, and give and understand clear instructions (Communication)
GA 8:  Understand and assess societal, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practice (Computing Professionalism and Society)
GA 9:  Understand and commit to professional ethics, responsibilities, and norms of professional computing practice (Ethics)
GA 10:Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional (Life-long Learning)

Curriculum Model for Bachelor of Science in Data Science

The generic structure for computing degree program given before is mapped with the BSCS program in the following tables.

Structure for BS Data Science Program

Mapping of BS Data Science Program on the Generic Structure:

Semester/Study Plan for BS Data Science