Admission Fee | 15,000 |
Security Deposit(refundable) | 5,000 |
Total at the time of admission | 20,000 |
Per Month Fees 04 Years | 8,500 |
“Ziauddin University Data Science degree program provides students with the technical skills, analytical knowledge, and practical experience necessary to succeed in the growing field of Data Science”.
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.
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 emphasizes 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.
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 & analyze small and large-scale database 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 individual or team and show the managerial, entrepreneurial and leadership skills.
PEO 4: To produce graduates that can understand the importance of life-long learning through professional development, specialized certifications and pursue postgraduate studies and succeed in industrial and research careers.
Lecture Rooms & Instructional Facilities
Additional Amenities: The rooms are fully air-conditioned for a comfortable learning environment.
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 |
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:
Salient Features
Campus Location: North Campus (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.
Sr.# | Course Code | Course Title | Th. | Lab | Cr. Hr |
1 | CS-107 | Introduction to Info. & Comm. Technologies | 2 | 1 | 2+1 |
2 | CS-104 | Programming Fundamentals | 3 | 1 | 3+1 |
3 | CS-103 | Discrete Structures | 3 | 0 | 3+0 |
4 | NS-115 | Basic Mathematics | 6 | 0 | N/C |
5 | NS-201 | Linear Algebra | 3 | 0 | 3+0 |
6 | HS-100 | English Composition & Comprehension | 3 | 0 | 3+0 |
7 | HS-103 | Pakistan Studies | 2 | 0 | 2+0 |
Total | 16 | 2 | 18 |
Sr.# | Course Code | Course Title | Th. | Lab | Cr.Hr |
1 | CS-112 | Object Oriented Programming | 3 | 1 | 3+1 |
2 | CS-233 | Introduction to Database System | 3 | 1 | 3+1 |
3 | NS-109 | Calculus and Analytical Geometry | 3 | 0 | 3+0 |
4 | NS-206 | Probability and Statistics | 3 | 0 | 3+0 |
5 | HS-114 | Communication & Presentation Skills | 3 | 0 | 3+0 |
Total | 15 | 2 | 17 |
Sr.# | Course Code | Course Title | Th. | Lab | Cr .Hr |
1 | CS-211 | Data Structures and Algorithms | 3 | 1 | 3+1 |
2 | CS-214 | Computer Org. & Assembly Language | 3 | 1 | 3+1 |
3 | CS-227 | Introduction to Data Science | 2 | 1 | 2+1 |
4 | EE-212 | Digital Logic Design | 3 | 1 | 3+1 |
5 | NS-112 | Differential Equations | 3 | 0 | 3+0 |
Total | 14 | 4 | 18 |
Sr.# | Course Code | Course Title | Th. | Lab | Cr.Hr |
1 | CS-355 | Computer Communication and Networks | 3 | 1 | 3+1 |
2 | CS-351 | Automata Theory and Formal Language (DS Elective-1) | 3 | 0 | 3+0 |
3 | CS-226 | Analysis of Algorithms | 3 | 0 | 3+0 |
4 | CS-213 | Artificial Intelligence | 3 | 1 | 3+1 |
5 | NS-211 | Advance Statistics | 3 | 0 | 3+0 |
Total | 15 | 2 | 17 |
Sr.# | Course Code | Course Title | Th. | Lab | Cr.Hr |
1 | CS-234 | Operating Systems | 3 | 1 | 3+1 |
2 | CS-336 | Data Mining | 2 | 1 | 2+1 |
3 | CS-331 | Data Warehousing & Business Intel. | 2 | 1 | 2+1 |
4 | CS-355 | Machine Learning (DS Elective-2) | 2 | 1 | 2+1 |
5 | MS-306 | Managerial Economics (University Elective-1) | 3 | 0 | 3+0 |
Total | 12 | 4 | 16 |
Sr.# | Course Code | Course Title | Th. | Lab | Cr.Hr |
1 | C-332 | Parallel & Distributed Computing | 2 | 1 | 2+1 |
2 | CS-456 | Big Data Analytics | 2 | 1 | 2+1 |
3 | CS-333 | Data Visualization | 2 | 1 | 2+1 |
4 | CS-352 | Digital Image Processing (DS Elective 3) | 3 | 0 | 3+0 |
5 | CS-454 | Cloud Computing (DS Elective-4) | 2 | 1 | 2+1 |
6 | MS-203 | Human Resource Management (University Elective-3) | 3 | 0 | 3+0 |
Total | 14 | 4 | 18 |
Sr.# | Course Code | Course Title | Th. | Lab | Cr.Hr |
1 | DS-451 | Final Year Project –I | 0 | 3 | 0+3 |
2 | CS-212 | Introduction to Software Engineering | 2 | 1 | 2+1 |
3 | MS-414 | Entrepreneurship and Leadership (University Elective-2) | 3 | 0 | 3+0 |
4 | HS-331 | Technical and Business Writing | 3 | 0 | 3+0 |
5 | HS-100 HS-102 | Islamic Studies / Ethical Behavior | 2 | 0 | 2+0 |
Total | 10 | 4 | 14 |
Sr.# | Course Code | Course Title | Th. | Lab | Cr.Hr |
1 | DS-451 | Final Year Project –II | 0 | 3 | 0+3 |
2 | HS-107 | Psychology (University Elective-4) | 3 | 0 | 3+0 |
3 | HS-401 | Professional Practices | 3 | 0 | 3+0 |
4 | CS-304 | Information Security | 3 | 0 | 3+0 |
Total | 9 | 3 | 12 |
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 analyze and interpret complex data to help organizations make better decisions. They apply machine learning, statistical analysis, and data visualization 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 visualization tools to create reports and dashboards. They help organizations 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 specialized 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 organization’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 analyze 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 analyze 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 modeling through experimentation and innovation.
Marketing Analyst
Marketing analysts use data to evaluate campaign performance, customer behavior, and market trends. They help businesses optimize 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 organizations. 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 analyze transaction patterns and use machine learning models to identify suspicious behavior.
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 analyze 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 analyze 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 multi-disciplinary 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 | Recognize the need, and have the ability, to engage in independent learning for continual development as a computing professional. |
Admission Fee | 15,000 |
Security Deposit(refundable) | 5,000 |
Total at the time of admission | 20,000 |
Per Month Fees 04 Years | 8,500 |