| Fee Description | Collection Type | Non Subject Fee |
| Admission Fee | One Time | 10,000 |
| Application Fee | One Time | 2,500 |
| Tuition Fee | Semester | 54,900 |
| Examination Fee | Semester | 5,100 |
| Total Fees | — | 72,500 |
BS Data Science
“Ziauddin University Data Science degree programme provides students with the technical skills, analytical knowledge, and practical experience necessary to succeed in the growing field of Data Science”.
Vision
To be a leading hub of innovation and excellence in computing—shaping future-ready professionals, pioneering research, and transformative solutions in the ever-evolving world of technology.
Mission
Our mission is to develop highly skilled data science professionals with a solid foundation in statistical analysis, machine learning, and computational techniques. We strive to bridge the gap between data and decision-making by equipping students with the tools to extract meaningful insights from complex datasets.
The department emphasises the integration of theoretical knowledge with practical applications, fostering innovation, critical thinking, and ethical responsibility in the handling of data. Through a multidisciplinary approach and a commitment to lifelong learning, we prepare our graduates to drive data-informed solutions that contribute to technological progress, societal development, and informed global decision-making.
Programme Educational Objectives (PEO)
PEO 1: To produce graduates having theoretical and practical knowledge of algorithms, instruments, techniques and methods used in the field of Data Science.
PEO 2: To produce graduates with the ability to design & analyse small and large-scale databases, identifying problematic components, selecting solution strategies for complex computing problems.
PEO 3: To produce graduates with necessary ethics needed to present and communicate effectively and to perform as individuals or team and show the managerial, entrepreneurial and leadership skills.
PEO 4: To produce graduates that can understand the importance of lifelong learning through professional development and specialised certifications and pursue postgraduate studies and succeed in industrial and research careers.
Lab Facilities
Lecture Rooms & Instructional Facilities
- Room Type: Dedicated and Shared Lecture Rooms
- Lecture Room Size: Each room is approximately 350 square feet in size.
- Available Space per Student: 30 square feet per student.
- Instructional Equipment: Each lecture room is equipped with a whiteboard, multimedia projectors, speaker system, computers, and internet connectivity.
Additional Amenities: The rooms are fully air-conditioned for a comfortable learning environment.
Laboratories
Lab Name | Timings | Facilities | Lab Space per Student |
|---|---|---|---|
Computing Lab | Weekdays (8:30am–4:30pm) | 32 Workstations (Core i3/i5, 3rd & 6th Gen), High-end Software, LAN/Wi-Fi, Scanner, Printing, Whiteboard, Multimedia | 40 sq. ft |
Operating System Lab | Weekdays (8:30am–4:30pm) | 5 Workstations (Core i3/i5, 3rd & 6th Gen), High-end Software, LAN/Wi-Fi, Printing Facility | 40 sq. ft |
Final Year Project Lab | Weekdays (8:30am–4:30pm) | 3 Workstations (Core i3/i5, 3rd & 6th Gen), High-end Software, LAN/Wi-Fi, Printing, Sensors, Potentiometer, 22″ LCD with HDMI, Extension Board | Not specified |
Why Choose BS Data Science
Choosing BS Data Science at Ziauddin University is a smart decision for students who want to thrive in one of the fastest-growing and most in-demand fields of the digital age. Here’s why you should consider it:
- Comprehensive Curriculum:
Our Data Science programme covers a wide range of essential topics, including statistics, machine learning, data visualisation, big data, and artificial intelligence. This ensures you’re fully equipped to handle data-driven challenges across industries. - Hands-on Learning:
You’ll gain practical experience through labs, projects, and real-world datasets. We emphasise learning by doing, so you graduate with the skills needed to solve complex problems using data. - Qualified Faculty:
Learn from experienced faculty with academic and industry backgrounds in data science, AI, and analytics. They’ll mentor you and provide insights that bridge classroom knowledge with real-world applications. - Research and Innovation:
Engage in cutting-edge research in areas such as predictive analytics, deep learning, and data ethics. Our programme fosters innovation and critical thinking—key for a successful data science career. - Industry Integration:
Ziauddin University maintains strong ties with tech companies, research organisations, and startups. You’ll benefit from internships, industry-led projects, and networking opportunities to help launch your career. - Collaborative Culture:
Work in a dynamic and collaborative environment that promotes teamwork, creativity, and analytical thinking—skills essential for success in data-centric roles. - Global Readiness:
Our programme is aligned with international standards, preparing you to work anywhere in the world. With a degree in Data Science from Ziauddin University, you’ll be ready to compete globally. - Focus on Future Skills:
We’re committed to excellence in education and skill development. Our goal is to prepare you for a data-driven world with the technical expertise, ethical awareness, and lifelong learning mindset required to lead in tomorrow’s digital economy.
Salient Features
- In-depth knowledge of data analysis techniques: A data science curriculum is very well designed and covers the essential topics as per academia and modern industry requirements. A data science curriculum provides students with a deep understanding of data analysis techniques such as statistical analysis, data mining, data visualisation, machine learning and Database Systems. This knowledge is essential for making sense of large and complex data sets.
- Proficiency in programming: Data scientists use programming languages such as Python, R, and SQL to manipulate, clean, and analyse data. A data science degree programme at Ziauddin University teaches students the programming skills necessary for these tasks.
- Understanding of database systems: Data is stored in various formats and systems, including relational databases, NoSQL databases, and data warehouses. A data science degree programme teaches students how to interact with these systems, manipulate data, and extract useful insights.
- Communication skills: Data scientists work with various stakeholders, including business leaders, developers, and other data professionals. A data science degree programme teaches students how to effectively communicate data insights to different audiences.
Location & Infrastructure
Campus Location: North Site (ZUFESTM), F-103, Block B, North Nazimabad, Karachi.
Covered Area: The ZUFESTM area spans 18,000 square feet (approximately 2,000 square yards), while the SE Department occupies 180 square feet.
Building Ownership: The facilities are located in a university-owned building.
- At least 50% marks in Intermediate (HSSC) examination with Mathematics or equivalent qualification with Mathematics, certified by IBCC. OR At least 50% marks in Intermediate (HSSC) examination with a pre-medical or equivalent qualification, certified by IBCC.
- Deficiency: Students with pre-medical must have to pass deficiency courses of Mathematics of 06 credit hours in first two semesters.
Programme Type
Semester system
Semester Duration
Semester System:
- 16 weeks of Teaching
- 2 weeks for Exams
Programme Duration/Length
- 8 Semesters
- Min: 4 Years
- Max: 7 Years
Weightage for the final examination
- Attendance* 10%
- Continuous assessments 40%
- Final Examination 50%
Semester 1st
| SEMESTER I | ||||||
| S. No. | Course Code | Course Title | Th | Lab | Cr. Hr. | Pre-requisite |
| 1 | DSLA-111 | Linear Algebra | 3 | 0 | 3+0 | |
| 2 | DSAP-112T | Applied Physics (Theory) | 2 | 0 | 2+1 | |
| 3 | DSAP-112L | Applied Physics (Lab) | 0 | 1 | ||
| 4 | ZUGE-004 | Functional English | 3 | 0 | 3+0 | |
| 5 | ZUGE-006T | Application to Information and Communication Technologies (Theory) | 2 | 0 | 3+1 | |
| 6 | ZUGE-006L | Application to Information and Communication Technologies (Lab) | 0 | 1 | ||
| 7 | DSCP-115T | Computer Programming (Theory) | 3 | 0 | 3+1 | |
| 8 | DSCP-115L | Computer Programming (Lab) | 0 | 1 | ||
| 9 | ZUGE-003 | Pakistan Studies | 2 | 0 | 2+0 | |
| 10 | DSBM-117 | Basic Matematics-1 | 3 | 0 | NC | |
| Total | 15 | 3 | 18 | |||
Semester 2nd
| SEMESTER II | ||||||
| S. No. | Course Code | Course Title | Th | Lab | Cr. Hr. | Pre-requisite |
| 1 | ZUGE-001 ZUGE-002 | Islamic Studies Ethical Behavior | 2 | 0 | 2+0 | |
| 2 | ZUGE-008 | Ideology and Constitution of Pakistan | 2 | 0 | 2+0 | |
| DSPA-121 | Principles of Accounting | 3 | 0 | 3+0 | ||
| 6 | DSCG-122T | Calculus and Analytical Geometry | 3 | 0 | 3+1 | |
| 5 | DSOP-123T | Object Oriented Programming (Theory) | 3 | 0 | 3+1 | Computer Programming |
| 6 | DSOP-123L | Object Oriented Programming (Lab) | 0 | 1 | ||
| 7 | DSDT-124 | Discrete Structures | 3 | 0 | 3+0 | |
| 8 | DSBM-125 | Basic Mathematics-II | 3 | 0 | NC | |
| Total | 16 | 1 | 17 | |||
Semester 3rd
| SEMESTER III | ||||||
| S. No. | Course Code | Course Title | Th | Lab | Cr. Hr. | Pre-requisite |
| 1 | ZUGE-009 or ZUGE-011 | Understanding of Holy Quran-1 | 0 | 1 | 0+1 | |
| Philosophy of Life | 1 | 0 | 1+0 | |||
| 2 | ZUGE-007 | Entrepreneurship | 2 | 0 | 3+0 | |
| 3 | DSMC-211 | Differential Equations | 3 | 0 | 3+0 | |
| 4 | DSDA-212T | Data Structures and Algorithms (Theory) | 3 | 0 | 3+1 | Programming Fundamentals (Theory) OOP |
| 5 | DSDA-212L | Data Structures and Algorithms (Lab) | 0 | 1 | ||
| 6 | DSID-213T | Introduction to Data Science (Theory) | 3 | 0 | 3+1 | |
| DSID-213L | Introduction to Data Science (Lab) | 0 | 1 | |||
| 7 | DSCC-214 | Civics and Community Engagement | 0 | 2 | 0+2 | |
| Total | 12 | 5 | 17 | |||
Semester 4th
| SEMESTER IV | ||||||
| S. No. | Course Code | Course Title | Th | Lab | Cr. Hr. | Pre-requisite |
| 1 | ZUGE-010 | Understanding of Holy Quran-II | 0 | 1 | 0+1 | Understanding of Holy Quran- 1 |
| ZUGE-012 | Philosophy of Life-II | 1 | 0 | 1+0 | Philosophy of lIfe-1 | |
| 2 | ZUGE-005 | Expository Writing | 3 | 0 | 3+0 | |
| 3 | DSIM-221 | Introduction to Management | 2 | 0 | 2+0 | |
| 4 | DSDA-222 | Design and Analysis of Algorithms | 3 | 0 | 3+0 | Data Structures & Algorithms |
| 5 | DSPS-223 | Probability and Statistics | 3 | 0 | 3+0 | |
| 6 | DSPP-224 | Professional Practices | 2 | 0 | 2+0 | |
| 7 | DSAD-225T | Advance Database Management System (Theory) | 2 | 0 | 2+1 | Essentials of Database Systems |
| 8 | DSAD-225L | Advance Database Management System (Lab) | 0 | 1 | ||
| Total | 16 | 2 | 18 | |||
Semester 5th
| SEMESTER V | ||||||
| S. No. | Course Code | Course Title | Th | Lab | Cr. Hr. | Pre-requisite |
| 1 | DSAS-311T | Advance Statistics (Theory) | 2 | 0 | 3+0 | |
| 3 | DSAS-311L | Advance Statistics (Lab) | 0 | 1 | ||
| 2 | DSAF-312 | Automata Theory and Formal Language | 3 | 0 | 3+0 | |
| 3 | DSOS-313T | Operating Systems (Theory) | 2 | 0 | 2+1 | |
| 4 | DSOS-313L | Operating Systems (Labs) | 0 | 1 | ||
| 5 | DSCN-314T | Computer Communication and Networks (Theory) | 2 | 0 | 2+1 | |
| 6 | DSCN-314L | Computer Communication and Networks (Labs) | 0 | 1 | ||
| 7 | DSDM-315T | Data Mining (Theory) | 2 | 0 | 2+1 | |
| DSDM-315L | Data Mining (Lab) | 0 | 1 | |||
| 8 | DSDD-316T | Digital Logic Design (Theory) | 2 | 0 | 2+1 | |
| 9 | DSDD-316L | Digital Logic Design (Lab) | 0 | 1 | ||
| Total | 13 | 5 | 18 | |||
Semester 6th
| SEMESTER VI | ||||||
| S. No. | Course Code | Course Title | Th | Lab | Cr. Hr. | Pre-requisite |
| 1 | DSSE-321 | Software Engineering Concepts | 3 | 0 | 3+0 | |
| 2 | DSAI-322T | Artificial Intelligence (Theory) | 3 | 0 | 3+1 | |
| 3 | DSAI-322L | Artificial Intelligence (Lab) | 0 | 1 | ||
| 4 | DSBD-323T | Big Data Analytics (Theory) | 2 | 0 | 2+1 | Data Mining |
| 5 | DSBD-323L | Big Data Analytics (Lab) | 0 | 1 | ||
| 6 | DSML-324T | Machine Learning (Theory) | 2 | 0 | 2+1 | Advance Statistics |
| 7 | DSML-324L | Machine Learning (Lab) | 0 | 1 | ||
| 8 | DSTW-325 | Technical and Business Writing | 3 | 0 | 3+0 | |
| Total | 13 | 3 | 16 | |||
Semester 7th
| SEMESTER VII | ||||||
| S. No. | Course Code | Course Title | Th | Lab | Cr. Hr. | Pre-requisite |
| 1 | DSIS-411 | Information Security | 3 | 0 | 3+0 | |
| 2 | DSDV-412T | Data Visualization (Theory) | 2 | 0 | 2+1 | |
| DSDV-412L | Data Visualization(Lab) | 0 | 1 | |||
| 3 | DSCO-413T | Computer Org. & Assembly Language (Theory) | 2 | 0 | 2+1 | Digital Logic Design |
| 4 | DSCO-413L | Computer Org. & Assembly Language (Lab) | 0 | 1 | ||
| 5 | DSAN-414T | Artificial Neural Network and Deep Learning (Theory) | 2 | 0 | 2+1 | |
| 6 | DSAN-414L | Artificial Neural Network and Deep Learning (Lab) | 0 | 1 | ||
| 7 | DSPD-415T | Parallel & Distributed Computing (Theory) | 2 | 0 | 2+1 | Operating Systems |
| 8 | DSPD-415L | Parallel & Distributed Computing (Lab) | 0 | 1 | ||
| 9 | DSFP-416 | Final Year Project-1 | 0 | 3 | 0+3 | |
| Total | 11 | 7 | 18 | |||
Semester 8th
| SEMESTER VIII | ||||||
| S. No. | Course Code | Course Title | Th | Lab | Cr. Hr. | Pre-requisite |
| 1 | DSBP-421T | Business Process Analysis (Theory) | 2 | 0 | 2+1 | |
| 2 | DSBP-421L | Business Process Analysis (Lab) | 0 | 1 | ||
| 3 | DSCC-422T | Data Warehouse and Business Intelligence-(Theory) | 2 | 0 | 2+1 | |
| 4 | DSCC-422L | Data Warehouse and Business Intelligence (Lab) | 0 | 1 | ||
| 5 | DSBT-423T | Human Computer Interaction | 3 | 0 | 3+0 | |
| 7 | DSMD-424T | Cyber Security and Forensic Data Science (Theory) | 2 | 0 | 2+1 | |
| 8 | DSMD-424L | Cyber Security and Forensic Data Science (Lab) | 0 | 1 | ||
| 9 | DSFP-425 | Final Year Project –II | 0 | 3 | 0+3 | FYP-I |
| Total | 9 | 6 | 15 | |||
Electives
| Domain | Credit Hours | Courses |
| Core Computing | 46 | 14 |
| General Education | 34 | 14 |
| Math’s and supporting | 12 | 4 |
| Domain Core | 18 | 6 |
| Domain Elective | 21 | 7 |
| Elective Supporting Courses | 3 | 1 |
| TOTAL | 134 | 46 |
| S.no. | Domain-Electives |
| 1 | Machine Learning |
| 2 | Deep Learning |
| 3 | Big Data Analytics |
| 4 | Natural Language Processing |
| 5 | Computer Vision |
| 6 | Web Technologies / Web Engineering / Web Development |
| 7 | Mobile Application Development |
| 8 | Internet of Things (IoT) |
| 9 | Cloud Computing |
| 10 | Network Security / Ethical Hacking / Information Security |
| 11 | Software Quality Assurance / Software Testing |
| 12 | Human-Computer Interaction |
| 13 | Game Development |
| 14 | Blockchain and Smart Contracts |
| 15 | Social Computing |
| 16 | Wireless and Mobile Computing / Wireless Networks |
| 17 | Advanced Programming – Visual Programming |
| 18 | Cyber Security |
| 19 | Web Engineering |
| 20 | Numerical Analysis |
| 21 | Applied Data Mining |
| 22 | Digital Image Processing |
| 23 | Enterprise Resource Planning |
| 24 | IT Innovations |
| 25 | Business and Financial Data Science |
| 26 | Advanced Algorithms Analysis |
| 27 | Advanced Database Management System |
Data Science is one of the most in-demand and fast-evolving fields in today’s digital world. It offers a wide range of exciting and high-impact career opportunities across various industries. Below are some popular career paths for Data Science graduates:
Data Scientist
Data scientists analyse and interpret complex data to help organisations make better decisions. They apply machine learning, statistical analysis, and data visualisation techniques to uncover insights and create predictive models.
Data Analyst
Data analysts collect, process, and perform basic to advanced analyses on data. They turn raw data into actionable insights using tools like Excel, SQL, Python, R, Tableau, and Power BI to support decision-making.
Machine Learning Engineer
Machine learning engineers design and deploy algorithms that enable systems to learn from data. They focus on building predictive models and automating decision-making processes for applications like recommendation systems, fraud detection, and more.
Data Engineer
Data engineers build and maintain data pipelines, databases, and ETL (Extract, Transform, Load) systems. They ensure that clean, structured data is available for analysis by creating scalable and reliable data infrastructure.
Business Intelligence (BI) Analyst
BI analysts use data analysis and visualisation tools to create reports and dashboards. They help organisations understand trends, performance metrics, and key business drivers to improve strategic decision-making.
Big Data Engineer
Big data engineers work with massive datasets that require specialised tools and platforms like Hadoop, Spark, and Kafka. They design and maintain scalable systems for processing and storing large volumes of data.
AI Engineer
AI engineers develop artificial intelligence systems, including natural language processing, computer vision, and intelligent automation. They combine programming skills with deep learning and AI frameworks to build smart applications.
Data Architect
Data architects design and structure an organisation’s data systems. They define how data will be stored, integrated, and accessed across different platforms, ensuring it meets performance, security, and compliance standards.
Statistical Analyst
Statistical analysts apply mathematical and statistical methods to analyse data and identify patterns. They work in domains like finance, healthcare, sports, and government to support data-driven strategies and policies.
Quantitative Analyst (Quant)
Common in finance and investment sectors, quants develop mathematical models to analyse financial markets, assess risks, and guide investment decisions using statistical and machine learning techniques.
Data Consultant
Data consultants help companies implement effective data strategies. They assess business needs, design data-driven solutions, and advise on tools and technologies to improve efficiency and innovation.
Research Scientist (Data & AI)
Research scientists work in academic, government, or industrial R&D settings. They push the boundaries of knowledge in AI, deep learning, and statistical modelling through experimentation and innovation.
Marketing Analyst
Marketing analysts use data to evaluate campaign performance, customer behaviour, and market trends. They help businesses optimise their marketing strategies through targeted and data-informed insights.
Operations Analyst
Operations analysts use data to improve internal processes, reduce costs, and increase efficiency within organisations. They work closely with management to support strategic and operational decisions.
Fraud Analyst
Fraud analysts work in banking, finance, and e-commerce to detect and prevent fraudulent activities. They analyse transaction patterns and use machine learning models to identify suspicious behaviour.
Healthcare Data Analyst
Healthcare data analysts interpret medical and patient data to support better healthcare outcomes. They help improve clinical operations, patient care, and hospital management through data insights.
Geospatial Data Analyst
Geospatial analysts work with location-based data (GIS) to analyse patterns related to geography, environment, urban planning, and transportation systems.
| Program Learning Outcomes (PLOs) | Computing Professional Graduate Outcomes |
| 1. Academic Education | To prepare graduates as computing professionals |
| 2. Knowledge for Solving Computing Problems | An ability to identify, formulate, research literature, and analyse complex engineering problems, reaching substantiated conclusions using first principles of mathematics, natural sciences, and engineering sciences |
| 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 consideration. |
| 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. |
| 6. Individual and Team Work | Function effectively as an individual and as a member or leader in diverse teams and in multidisciplinary 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. |
| 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 | Recognise the need and have the ability to engage in independent learning for continual development as a computing professional. |
