| Admission Fee | 15,000 |
| Security Deposit(refundable) | 5,000 |
| Total at the time of admission | 20,000 |
| Per Month Fees 04 Years | 8,500 |
BS Data Science
Why Data Science at ZU:
“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”.
Salient Features of the Data science program offered at Ziauddin University:
- In-depth knowledge of data analysis techniques: A data science curriculum is very well designed that 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 Visualization, 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 program of 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 program 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 program teaches students how to effectively communicate data insights to different audiences.
Mission
Our mission is to produce graduates with a comprehensive education in Data Science, equipping them with the comprehensive knowledge and skills to extract meaningful insights from vast datasets and develop data-driven decision- making systems.
Through a multidisciplinary curriculum encompassing mathematics, statistics, computer science, and domain expertise, we aim to develop graduates who possess ethical considerations of digital data and lifelong learning to drive positive change in the society.
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 & 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.
Programme Learning Outcomes (PLOs)
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. |
Facilities/Resources
- Computing resources: Data science program at Ziauddin University offers significant computing resources to process and analyze large data sets. This includes access to high-performance computing clusters, cloud computing platforms, and specialized hardware such as graphics processing units (GPUs).
- Software tools: For students of Data Science a wide range of software tools to analyze data, including programming languages such as Python and R, and data analysis tools such as SQL, Tableau, and Excel are available. These Software tools are available for students to access them in various courses.
- Faculty: Ziauddin University is famous for experienced and knowledgeable faculty members with expertise in data science. Our faculty members have a strong background in statistics, computer science, and data analysis, as well as experience working in industry.
- Laboratories: Data science program have laboratories equipped with computing resources, software tools, and data sets. These laboratories are accessible to students outside of class hours to enable them to work on projects and assignments.
Career Prospectus
Data science is a rapidly growing field with a wide range of career opportunities. Here are some popular career paths in data science:
- Data scientist: A data scientist is responsible for collecting, processing, and analyzing large data sets using statistical methods and machine learning techniques. Data scientists use their knowledge to provide insights and recommendations to business leaders and other stakeholders.
- Data analyst: A data analyst is responsible for collecting, cleaning, and analyzing data to provide insights and recommendations. Data analysts typically work with smaller data sets and use basic statistical techniques to find patterns and trends in the data.
- Machine Learning Engineer: A machine learning engineer is responsible for developing and implementing machine learning algorithms to solve specific business problems. Machine learning engineers need to have a strong background in computer science, statistics, and machine learning.
- Business Intelligence Analyst: A business intelligence analyst is responsible for collecting and analyzing data to help business leaders make informed decisions. Business intelligence analysts use data visualization tools and other techniques to create dashboards and reports that provide insights into key business metrics.
- Data Engineer: A data engineer is responsible for building and maintaining the infrastructure that supports data analysis. Data engineers design and implement databases, data pipelines, and other systems to manage and process large data sets.
- Big Data Analyst: A big data analyst is responsible for analyzing large and complex data sets using tools such as Hadoop and Spark. Big data analysts use their expertise to identify patterns and trends in the data that can be used to make informed business decisions.
In summary, data science offers a wide range of career opportunities, including data scientist, data analyst, machine learning engineer, business intelligence analyst, data engineer, and big data analyst. As data continues to play a crucial role in business decision-making, the demand for skilled data professionals is expected to continue to grow in the coming years.
- 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.
Semester 1st
| 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 | ||
Semester 2nd
| 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 | ||
Semester 3rd
| 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 | ||
Semester 4th
| 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 | ||
Semester 5th
| 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 | ||
Semester 6th
| 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 | ||
Semester 7th
| 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 | ||
Semester 8th
| 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 | ||
