MS
Data Science
Department
Computer Science
Level
Graduate
College
College of Computer Sciences & Information Systems
Program Detail
The MS Data Science (MS DS) program is a gateway to a world of opportunities. It prepares students to convert data into actionable insights, enabling them to make complex business decisions. Students will learn to handle large, complex data sets using computational, statistical, and machine learning techniques. The program offers exposure to the latest trends and technologies in data science, producing skilled professionals to meet the growing demand for data science products in national and international markets. The demand for data scientists is not just increasing; it’s rising steeply, and there is a significant need for data science specialists across the public and private sectors worldwide.
The program is designed in accordance with Higher Education Commission (HEC) guidelines. It spans a minimum of 2 years, comprising at least 4 semesters, and requires the successful completion of 32 credit hours.
The following Program Objectives are articulated for the MS Data Science program:
- To translate data analysis into actionable business insights.
- To collaborate effectively in diverse teams.
- To design, implement, and evaluate machine learning models to solve complex data problems.
- To create clear and impactful data visualizations and reports to present findings effectively.
- To effectively operate and communicate as leaders or team members while understanding professional ethics and social responsibility.
- To embrace technological advancements in data science research and development and actively engage in lifelong learning.
Learning Outcomes for MS Data Science students include:
- The capacity to use knowledge to solve data science research and practical challenges efficiently.
- The capacity to evaluate intricate issues and find and create creative, data-driven solutions.
- The capacity to obtain the necessary information and abilities to preprocess and assess data efficiently.
- The capacity to regulate data science initiatives while being thoroughly aware of social concerns, professional ethics, and obligations.
- The capacity to interact with a variety of audiences effectively.
MS Data Science students learn to:
- Students will be able to translate data analyses into actionable business insights.
- Students will collaborate effectively in diverse teams.
- Students will design, implement, and evaluate machine learning models to solve complex data problems.
- Students will create clear and impactful data visualizations and reports to present findings effectively.
- Prepare students to effectively operate and communicate as leaders or team members while understanding professional ethics and social responsibility.
- Prepare students to embrace technological advancements in data science research and development and actively engage in lifelong learning.
Career Path:
1. | Machine Learning Engineer | 2. | Data Scientist | 3. | Data Analyst/Engineer |
4. | Business Intelligence Analyst | 5. | Big Data Engineer | 6. | Computer Vision Engineer |
7. | Data Product Manager | 8. | Data Mining Engineer | 9. | Healthcare Data Scientist |
10. | Data Architect | 11. | Operations Research Analyst | 12. | Data Science Consultant |
Prospective Employers:
1. | Technology Companies | 2. | Research Institutions | 3. | Automotive Industry |
4. | Healthcare Industry | 5. | Financial Services | 6. | E-commerce and Retail |
7. | Manufacturing and Logistics | 8. | Educational Institutions | 9. | Aerospace and Defense |
Eligibility:
To be eligible for the MS in Data Science (MSDS) program, candidate must have a 4-year Bachelor’s degree (16 years of education) in a relevant computing discipline (such as BS Data Science, BS Computer Science, BS IT, etc.) from an HEC-recognised university, with a minimum 2.5 CGPA on a 4.0 scale or a 2nd Division. If the student is coming from irrelevant background, then the student must pass 4 deficiency courses.
Program Requirements:
To fulfill the degree requirements, students must complete eight taught courses along with 6 credit hours of research thesis for MS by Thesis, or 6 credit hours of Independent Research Studies, namely IRS-I and IRS-II, for MS by Independent Research Study, or 6 credit hours through two courses, Elective IV and Elective V, each carrying 3 credit hours, for MS by Coursework. The MS by Coursework option shall be subject to the approval of the departmental committee. For MS Thesis requirement, the students must refer to the MS Policy Manual available at https://basr.iobm.edu.pk/ms-mphil-and-phd-policy-and-thesis/
Course Structure:
Course Structure | Course Structure |
Semester One Research Methodology Advanced Data Mining and Machine Learning Statistics & Probability for Data Science Elective I | Semester Two Deep Learning and Neural Networks Advanced Big Data Analytics, Tools, And Techniques Elective II Elective III |
Semester Three IRS-1/ Elective IV */ MS Thesis-I Understanding of Holy Quran-I **/ Comparative Religion-I ** | Semester Four IRS – II / and Elective V * / MS Thesis-II Understanding of Holy Quran-II **/ Comparative Religion-II ** |
* Subject to the approval of departmental committee
** Muslim students are required to take Understanding of Holy Quran-I (1 Credit hour) and Understanding of the Holy Quran-II (1 Credit hour) and Non-Muslim students are required to take Comparative Religion-I and Comparative Religion-II.
Core Courses:
Course Code | Course Title | Credit Hours |
CSP 511 | Research Methodology | 3+0 |
DSP 531 | Statistics & Probability for Data Science | 3+0 |
DSP 561 | Advanced Big Data Analytics | 3+0 |
DSP 581 | Advanced Data Mining and Machine Learning | 3+0 |
DSP 582 | Deep Learning and Neural Networks | 3+0 |
Elective Courses:
Course | Course Title | Credit | Course | Course Title | Credit |
DSP 512 | Data Ethics and Privacy | 3+0 | DSP 521 | Programming for Data Science (Python/R) | 3+0 |
DSP 532 | Numerical Linear Algebra | 3+0 | DSP 533 | Design and Analysis of Algorithms | 3+0 |
DSP 534 | Optimization Methods | 3+0 | DSP 535 | Advanced Modeling and Simulation | 3+0 |
DSP 541 | Distributed Computing | 3+0 | DSP 542 | Cloud Computing for Data Science | 3+0 |
DSP 562 | Advanced Database Techniques | 3+0 | DSP 563 | Advanced Data Warehousing | 3+0 |
DSP 564 | Advanced Data Analytics and Visualization | 3+0 | DSP 565 | Financial Data Analysis | 3+0 |
DSP 566 | Geospatial Data Analysis | 3+0 | DSP 567 | Business Intelligence | 3+0 |
DSP 583 | Time Series Analysis & Forecasting | 3+0 | DSP 584 | Pattern Recognition | 3+0 |
DSP 585 | Knowledge-Based System | 3+0 | DSP 586 | Text Processing | 3+0 |
AIP 584 | Information Retrieval Techniques | 3+0 | DSP 587 | Natural Language Processing | 3+0 |
DSP 588 | Speech Processing | 3+0 | DSP 681 | Image Processing and Computer Vision | 3+0 |
DSP 682 | Generative AI | 3+0 | DSP 683 | Web Mining | 3+0 |
DSP 684 | Social Media Analysis | 3+0 | DSP 685 | Web Intelligence and Big Data | 3+0 |
DSP 686 | Distributed Intelligence System | 3+0 | DSP 687 | Deep Reinforcement Learning | 3+0 |
Note: Students must select at least three electives from the above list
DSP 691 | MS Thesis – I | 3+0 | DSP 692 | MS Thesis – II | 3+0 |
DSP 693 | Independent Research Study – I | 3+0 | DSP 694 | Independent Research Study – II | 3+0 |
Note: Students must choose either the MS Thesis, the Independent Research Study, or two elective courses from the Elective Courses List. If a student opts for the MS Thesis, enrollment in MS Thesis–II is mandatory to maintain continuous registration until the thesis is formally submitted to the Board of Advanced Studies and Research (BASR) for further processing.