BS Data Science
Introduction
Data Science is an interdisciplinary field that combines statistics, mathematics, computer science, and analytical techniques to extract meaningful insights from structured and unstructured data. It involves the systematic collection, processing, analysis, and interpretation of large volumes of data to identify patterns, discover trends, and support informed decision-making. The BS Data Science program aims to equip students with the theoretical knowledge and practical skills required to work with data throughout its life cycle, from data collection and preparation to analysis, visualization, predictive modeling, and interpretation of results.
Data Science spans a wide range of areas, from statistical analysis and data management to advanced developments in artificial intelligence, machine learning, big data analytics, and predictive modeling. The overall scope of Data Science may be viewed in the following three categories:
- To extract meaningful insights from data: Data Science develops effective methods to collect, organize, process, and analyze data to solve complex problems. It uses statistical techniques, mathematical models, and computational tools to identify patterns, relationships, and trends within large datasets. These techniques help organizations understand past performance, predict future outcomes, and make evidence-based decisions in fields such as business, healthcare, education, finance, and scientific research.
- To develop intelligent and predictive solutions: Data Science combines machine learning, artificial intelligence, and statistical modeling to develop systems that learn from data and make predictions. These technologies support applications such as recommendation systems, fraud detection, disease prediction, customer behavior analysis, and intelligent automation. Through the effective use of data-driven techniques, Data Science enables researchers and organizations to discover new opportunities, improve existing processes, and develop innovative solutions to real-world challenges.
- To design and implement data-driven systems: Data Science provides students with the knowledge and practical skills required to design, develop, and deploy systems that transform raw data into useful information. It covers database management, data visualization, big data technologies, data engineering, and cloud-based analytical systems. The program also emphasizes the ethical and responsible use of data, including privacy, security, and data quality. This comprehensive foundation prepares graduates to adapt to emerging technologies and pursue careers in data analysis, machine learning, business intelligence, artificial intelligence, and other data-intensive fields.
Eligibility Criteria
- Minimum 50% marks in Intermediate/12 years schooling/A- Level (HSSC) or Equivalent with Mathematics are required for admission in all BSCS
- Minimum 60% marks in Intermediate/12 years schooling/A- Level (HSSC) or Equivalent with Mathematics are required for admission in BS Computer Engineering Program.
- The students who have not studied Mathematics at intermediate level have to pass deficiency courses of Mathematics (06 credits) in first two semesters.
Duration of the Program and Award of Degree
At minimum 130 credit hours are required for award of BSDS degrees in any computing discipline mentioned in this document.
The minimum duration for completion of BSDS is four years. The HEC allows maximum period of seven years to complete BS degree requirements.
A minimum 2.0 CGPA (Cumulative Grade Point Average) on a scale of 4.0 is required for award of BS Computing Degree.
The students after successful completion of 04 semesters in BS Computing Programs may exit with Associate Degree in Computing subject to completion of all requirements for the award of associate degree, i.e., Credit Hours, CGPA, and compulsory courses.
Program Mission
To equip students with the knowledge and skills to extract insights from data through analytical, statistical, and computational methods. The program nurtures data-driven decision-makers who can apply data science principles to real-world problems while adhering to ethical standards and promoting sustainable development.
Program Educational Objectives (PEOs)
Graduates will analyze and interpret data to solve real-world problems and support informed decision-making.
Graduates will follow ethical data practices and communicate results effectively in multidisciplinary environment.
Graduates will continue learning to stay updated with data science tools, techniques, and trends.
Graduate Attributes
To prepare graduates as computing professionals
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.
Identify, formulate, research literature, and solve complex computing problems reaching substantiated conclusions using fundamental principles of mathematics, computing sciences, and relevant domain disciplines.
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.
Create, select, adapt and apply appropriate techniques, resources, and modern computing tools to complex computing activities, with an understanding of the limitations.
Function effectively as an individual and as a member or leader in diverse teams and in multi-disciplinary settings.
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.
Understand and assess societal, health, safety, legal, and cultural issues within local and global contexts, and the consequential responsibilities relevant to professional computing practice.
Understand and commit to professional ethics, responsibilities, and norms of professional computing practice.
Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional.
Mapping of PEOs to GAs
| S. No. | GAs | PEO1 | PEO2 | PEO3 |
|---|---|---|---|---|
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1. |
Academic Education |
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2. |
Knowledge for Solving Computing Problems |
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3. |
Problem Analysis |
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4. |
Design / Development of Solutions |
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5. |
Modern Tool Usage |
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6. |
Individual & Team Work |
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7. |
Communication |
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8. |
Computing Professionalism and Society |
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9. |
Ethics |
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10. |
Life-Long Learning |
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Generic Structure for BSDS
| Credit Hours | Courses |
|---|---|
|
46 |
14 |
|
18 |
6 |
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21 |
7 |
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12 |
4 |
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3 |
1 |
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30 |
12 |
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Total = 130 |
Total = 44 |
Scheme of Study BS Data Science Program
Total Credit Hours = 130
| Courses | |||
|---|---|---|---|
|
Introduction to ICT |
Programming Fundamentals |
Discrete Structures |
Calculus & Analytical Geometry |
|
English Composition & Comprehension |
Object Oriented Programming |
Database Systems |
Linear Algebra |
|
Probability & Statistics |
Communication & Presentation Skills |
Data Structures & Algorithms |
Information Security |
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Artificial Intelligence |
Digital Logic & Design |
Differential Equations |
Computer Networks |
|
Computer Organization & Assembly Language |
Analysis of Algorithms |
Programming for AI |
AI – Elective 1 |
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Operating Systems |
Artificial Neural Networks |
Machine Learning |
Knowledge Representation & Reasoning |
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AI – Elective 2 |
Parallel & Distributed Computing |
Computer Vision |
Natural Language Processing |
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University Elective -1 |
AI – Elective 3 |
Software Engineering |
AI – Elective 4 |
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University Elective -2 |
University Elective -3 |
Technical & Business Writing |
Final Year Project – 1 |
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University Elective – 4 |
Professional Practices |
Pakistan Studies |
Islamic Studies |
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Final Year Project – 2 |
AI Electives (Domain Electives) |
Fuzzy Systems |
Theory of Automata & Formal Languages |
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Data Mining |
Deep Learning |
Advance Statistics |
Speech Processing |
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Reinforcement Learning |
Evolutionary Computing |
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