Faculty Profile


Dr. Muhammad Usman
Head of Department (Computer Science)
Program Manager PhD (Computing)

PhD-Computer & Information Sciences (Auckland University of Technology, New Zealand)
Dr. Azhar Mahmood
Program Manager MS - Data Science

PhD - Computer Applied Technology (Huazhong University of Science and Technology, Wuhan P.R China)
Dr. Muhammad Imran

Program Manager MS - CS

PhD - Information Technology (UTHM, Johor Malaysia)
Dr. Tazeen Athar

Program Manager BS - CS

PhD - Mathematics (University of Saarland, Saarbruecken, Germany)
Syed Muhammad Usman

Program Manager BS - SE

MS - Computer Engineering (College of Electrical & Mechanical Engineering NUST, Islamabad)

MS (Data Science)




MS Data Science Program

The MS (Data Science) program is of 2-years duration offered in the evening. It requires 30 credit hours including 3 core courses (3x3=9) and 2 specialized data science courses (2x3=6). Thesis of 6 credit hours is mandatory.


The maximum time limit to complete the MS (Data Science) degree is 4 years.

Why Study Data Science?

The amount of data is growing so rapidly as well as its significance in the emerging societal setups such as the pervasive Internet of Things. The way one imagines data is going to change in the coming years. Both Big Data Analytics and pervasive computing hinge on the principle axis of data analytics. MS (Data Science) program is going to be relevant in terms of job creation and artisanal smart business generation. Graduates from this program would definitely avail the early-bird advantage.



Program Objectives

The MS (Data Science) program has been designed to give students the option to be part of a data science endeavor that begins with the identification of business processes, determination of data provenance and ownership, understanding the ecosystem of the business decisions, skill sets and tools that shape the data, making data amenable to analytics, identifying sub-problems, recognizing the technology matrix required for problem resolution, creating incrementally-complex data-driven models and then maintaining them to ultimately leverage them for business growth.

Individual objectives include:

  • To equip students to transform data into actionable insights to make complex decisions
  • To enable students to understand and analyze problems and arrive at computable solutions
  • To expose students to the set of technologies that match those solutions.
  • 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.
  • To understand the implications of handling data in terms of data security and business ethics

Eligibility

A degree of BS(CS) as per HEC curriculum.

  • 16 years of education in a related field with minimum 60%/ marks or CGPA 2.00.
  • GAT General/ HAT relevant with min. 50% score


Deficiency Courses


  1. Programming Fundamentals
  2. Database Systems
  3. Data Structures & Algorithms
  4. OR
  5. Design & Analysis of Algorithms

The candidates shall have to submit GRE (General)/GAT (General)/HAT relevant score of minimum 50%. The maximum time limit to complete the MS degree is four years.

For Courses Details Click here










MS (Data Science)


Master of Data Science


FIRST YEAR


FIRST SEMESTER
Sr. No. Course Code Course Title
1 DSC 5101 Statistical and Mathematical Methods for Data Science
2 DSC 5102 Tools and Techniques in Data Science
3 DSC 6xxx Elective-I
SECOND SEMESTER
Sr. No. Course Code Course Title
1 DSC 5201 Machine Learning
2 DSC xxxx Specialization-Elective-I
3 DSC xxxx Specialization-Elective-II

SECOND YEAR


Third SEMESTER
Sr. No. Course Code Course Title
1 DSC xxxx Elective II
2 DSC xxxx Thesis (Part-I)

Fourth SEMESTER
Sr. No. Course Code Course Title
1 DSC xxxx Elective III
2 DSC xxxx Thesis (Part-II)



Course type No. of courses x Credits Cumulative credits
Core Courses 09
Specialization Courses 2 x 3 06
Electives 3 x 3 09
Thesis (Part-I & Part-II) 2 x 3 06
Total 30


Three Core Courses Cr.Hrs
Tools and Techniques in Data Science 2 + 1*
Statistical and Mathematical Methods for Data Science 3
Machine Learning 3

* 2+1 means 2 hours of lecture + 3 hours of lab work



Specialization Courses Cr.Hrs
Big Data Analytics 3
Deep Learning 3
Natural Language 3
Distributed Data Processing 3

Electives Courses


DSC 5121 Cloud Computing
DSC 5221 Advanced Computer Vision
DSC 5222 Research Methodology
DSC 5241 Natural Language Processing
DSC xxxx Algorithmic Trading
DSC xxxx Bayesian Data Analysis
DSC xxxx Big Data Analytics
DSC xxxx Bioinformatics
DSC xxxx Computational Genomics
DSC xxxx Data Visualization
DSC xxxx Deep Learning
DSC xxxx Deep Reinforcement Learning
DSC xxxx Distributed Data Processing and Machine Learning
DSC xxxx High Performance Computing
DSC xxxx Inference & Representation
DSC xxxx Probabilistic Graphical Models
DSC xxxx Scientific Computing in Finance
DSC xxxx Social Network Analysis
DSC xxxx Time series Analysis and Prediction
DSC xxxx Graph Analytics for Big Data
DSC xxxx Mining Massive Datasets
DSC xxxx IoT for Smart Cities and Smart Homes
DSC xxxx Data Science with R
DSC xxxx Python Programming for Data Science
DSC xxxx Implementing
DSC xxxx Data Science in Cyber Security
DSC xxxx Business Context Modelling
DSC xxxx Advanced Topics in Decision Support Systems
DSC xxxx Pattern Recognition

All courses may not be offered in every semester. Elective courses may vary from time to time. Alternative courses may be substituted as and when required.