Following objectives are primary motives of this graduate program.

1. To enhance the advance knowledge for graduates to get the actionable results by applying statistical and modeling techniques for complex business decisions.
2. Graduated students can be able to analyze a solid problem and reached up to computable solutions.
3. Students can be able to expose against the state-of-the-art technologies that matches with designed solutions.
4. To gain hands-on experience on data-centric tools for statistical analysis, visualization, and big data applications at the same rigorous scale as in a practical data science project.
5. Students understand handling data specially for data security and business ethics.

Registration in “MS Thesis/Project” will be allowed to register after completing minimum of 6 credit hours i.e., core courses:

This program equips students with latest tools, technologies and methodologies to solve complex data related problems. This is all about making sense of raw and structured data to extract meaningful information. The program covers mathematical and statistical foundations, Data Science Tools, Big Data Analytics, Natural Language Processing, and Information Visualization. In this course, there will be different case studies belonging to different areas such as: Telecom, Health sector, scientific domain, social media, customers’ data etc. The successful candidates will be able to pursue jobs in the following areas:

• Software development focusing acquiring, processing and visualizing data.
• Researchers focusing in data centric research methodologies by either innovating or effective usage of data strategies.
• Cyber Security Data Analyst
• Social Media Data Analyst
• Healthcare Data Analyst
• Enterprise Data Analyst
• Financial Data Analyst.

• A degree of BS (CS) as per HEC curriculum.
• Students with 16 years of education in following domains (Information Technology, Software Engineering, Computer Engineering, Electrical Engineering, Statistics, or Mathematics) are eligible to apply provided that they have taken following deficiency courses.
• Degree in relevant subject of Science or Engineering, earned from a recognized university after 16 years of education AND
• At least 60% marks or CGPA of at least 2.0(on a scale of 4.0).
• GAT-General conducted by ETS/NTS or any other recognized testing body including university admission test with at least 50%. marks

Minimum of 2.5 CGPA[1]

Completion of 30 SCH

1. Programming Fundamentals (Core Programming Course)
2. Data Structures & Algorithms OR Design & Analysis of Algorithms
3. Database Systems

The minimum duration to complete this degree is 2 years and not more than 4 years or 8 semesters. The degree will be terminated after completion of maximum duration.

The classes will be held on campus. This will be an evening program usually having classes from 6:00 pm to 9:00 pm.  

The mode of instruction will be in English. Each course may be evaluated based on quizzes, assignment, mid-term, semester projects, viva, reports, and final terms exams.

University regulation along with directives/guidelines of HEC/relevant council issued from time to time.

The program would be spread over 4 semesters. 24 credit hour course work is compulsory for everyone. There are two options to complete the remaining 6 credit hours.

1. Statistical and Mathematical Methods for Data Science (3)
2. Tools and Techniques in Data Science (2+1)
3. Machine Learning (3)

1. Big Data Analytics (3)
2. Deep Learning (3)
3. Natural Language Processing (3)
4. Distributed Data Processing (3)

According to the current rules of HEC, a thesis would enable students to have their degree vetted equivalent to an M.Phil. degree.

Following is a non-exhaustive list of elective courses. New elective courses may be added to this list. Students may be recommended to make their choice of electives, in the light of a soft specialization within the field of data science.

1. Algorithmic trading
2. Advanced Computer Vision
3. Bayesian Data Analysis
4. Big Data Analytics
5. Bioinformatics
6. Cloud computing
7. Computational Genomics
8. Data Visualization
9. Deep Learning
10. Deep Reinforcement Learning
11. Distributed Data Processing and Machine Learning
12. Distributed Machine Learning in Apache Spark
13. High performance computing
14. Inference & Representation
15. Natural Language Processing
16. Optimization Methods for Data Science and Machine Learning
17. Probabilistic Graphical Models
18. Scientific Computing in Finance
19. Social network analysis
20. Time series Analysis and Prediction