Program Objectives
The objectives of the M. Sc. program in Computer Science and Engineering are:
The requirements for admission in Master’s degree program are:
Evaluation of applicants for admission is based primarily on the student’s academic record in relevant undergraduate coursework. Applicants are expected to have sufficient knowledge in undergraduate-level mathematics and be familiar with common software packages. Provisional admission can be given to an applicant awaiting the result of her/his bachelor’s degree.
The degree requirements for Masters’ program in Computer Science and Engineering for students with four-year degrees in CS/CSE or equivalent subjects are 36 credits. The program is either thesis-based or project-based. The project is 8 credits and the thesis is 17 credits. Students from academic disciplines, other than CS/CSE or equivalent will be required to complete a maximum of 24 credit hours prerequisite courses in addition to the 36 credit hours mentioned above. However substantial real-life work experience in the ICT sector may be considered to waive some prerequisite courses. The summary of the program is given below:
|
|
Courses |
Credits |
Total credits |
|
Project-based |
9 Courses |
(9 x 3) = 27 |
36 credits |
|
Project |
8 |
||
|
Seminar |
1 |
||
|
Thesis based |
6 Courses |
(6 x 3) = 18 |
36 credits |
|
Thesis |
17 |
||
|
Seminar |
1 |
The duration of the course may vary from three to six semesters, depending on how many prerequisite courses, a student has to undertake. In general, students who have completed the prerequisite courses prior to admission should be able to complete the required program in three semesters.
Theoretical courses are organized under three categories: prerequisite course, core course, and elective course. Prerequisite courses are offered for students without graduation in Computer Science/Engineering or equivalent subject.
(Refer to Undergraduate Program for Computer Science and Engineering for the details of the courses)
Students with bachelor’s degrees in Computer Science/Engineering will not need to do prerequisite courses. Students without graduation in Computer Science/Engineering or equivalent subject will have to complete at least 24 credits of prerequisite courses before starting the Master’s program; these students must complete prerequisite courses listed below with at least C grade.
|
Course Code |
Course Title |
Credit Hours |
Class hours (Per week) |
|
CSE131 |
Discrete Mathematics |
3 |
3 |
|
CSE133 |
Data Structures with Lab |
3+1=4 |
3+2 |
|
CSE212 |
Digital Logic Design with Lab |
3+1=4 |
3+2 |
|
CSE221 |
Theory of Computing |
3 |
3 |
|
CSE222 |
Object-oriented Programming with Lab |
3+1=4 |
3+2 |
|
CSE233 |
Data Communication |
3 |
3 |
|
CSE311 |
Database Management System with Lab |
3+1=4 |
3+2 |
|
CSE321 |
Systems Analysis and Design |
3 |
3 |
|
CSE322 |
Computer Architecture and Organization with Lab |
3+1=4 |
3+2 |
|
CSE323 |
Operating Systems with Lab |
3+1=4 |
3+2 |
|
CSE331 |
Compiler Design with Lab |
3+1=4 |
3+2 |
The student must complete all 100 and 200 level courses before starting with any of the courses in core and non-core. The rest of the courses may be taken in combination with Master's Courses. Prerequisite courses are normal undergraduate courses and Master's Students with pre-requisite requirements will attend these courses with undergraduate students.
|
Course Code |
Course Title |
Credit Hours |
Class Hours (per week) |
|
CSE501 |
Advanced DBMS |
3 |
3 |
|
CSE502 |
Advanced Artificial Intelligence |
3 |
3 |
|
CSE503 |
Research Methodology |
3 |
3 |
|
CSE504 |
Software Development Methodology |
3 |
3 |
|
CSE505 |
High-speed Computer Networks |
3 |
3 |
|
CSE506 |
Advanced-Data Analytics |
3 |
3 |
|
CSE507 |
Advanced Graph Theory |
3 |
3 |
|
CSE508 |
Fundamentals of Data Science |
3 |
3 |
|
CSE509 |
Statistical and Mathematical Foundations for Data Analytics |
3 |
3 |
|
CSE510 |
Data and Information Ethics |
3 |
3 |
|
CSE511 |
Algorithms for Data Science |
3 |
3 |
The students pursuing M. Sc. with project work should select five courses (5 x 3 credits) and the students with thesis work should select two courses (2 x 3 credits) from the following courses. The course offering however depends on the availability of teachers and requirements of the time.
|
Course Code |
Course Title |
Credit Hours |
Class Hours (per week) |
|
CSE601 |
Computational Geometry |
3 |
3 |
|
CSE602 |
Parallel and Distributed Systems |
3 |
3 |
|
CSE603 |
Object-Oriented Analysis and Design |
3 |
3 |
|
CSE604 |
Speech and Language Processing |
3 |
3 |
|
CSE605 |
Machine Translation |
3 |
3 |
|
CSE606 |
Cryptography and Information Security |
3 |
3 |
|
CSE607 |
Distributed Database System |
3 |
3 |
|
CSE608 |
Wireless and Mobile Systems |
3 |
3 |
|
CSE609 |
Computer Graphics & Visualization |
3 |
3 |
|
CSE610 |
Electronic Commerce |
3 |
3 |
|
CSE611 |
Web Programming |
3 |
3 |
|
CSE612 |
Computer Vision |
3 |
3 |
|
CSE613 |
Embedded System Design |
3 |
3 |
|
CSE614 |
Parallel Algorithms |
3 |
3 |
|
CSE615 |
Advanced Digital Signal Processing |
3 |
3 |
|
CSE616 |
Software Analysis and Design |
3 |
3 |
|
CSE617 |
Advanced Optical Communication Systems |
3 |
3 |
|
CSE618 |
Software Engineering Research Method |
3 |
3 |
|
CSE619 |
Computer Systems Verification |
3 |
3 |
|
CSE620 |
Software Project Management |
3 |
3 |
|
CSE621 |
Machine Learning Technique |
3 |
3 |
|
CSE622 |
Interactive Multimedia Design and Development |
3 |
3 |
|
CSE623 |
Advanced Computer Architecture |
3 |
3 |
|
CSE624 |
Neural Network and Fuzzy Systems |
3 |
3 |
|
CSE625 |
Pattern Recognition and Visualization |
3 |
3 |
|
CSE626 |
Blockchain and CryptoCurrency |
3 |
3 |
|
CSE627 |
Human-Computer Interaction |
3 |
3 |
|
CSE628 |
Data Visualization |
3 |
3 |
|
CSE629 |
Data Science for Health Care |
3 |
3 |
|
CSE630 |
Social Media Data Management and Analytics |
3 |
3 |
|
CSE631 |
Cloud Computing for Data Analytics |
3 |
3 |
|
CSE632 |
Data Engineering |
3 |
3 |
|
CSE633 |
Data Science and Strategic Decision Making |
3 |
3 |
|
CSE634 |
Data Modeling |
3 |
3 |
|
CSE635 |
Advanced Time Series Analysis |
3 |
3 |
|
CSE636 |
Advanced Geographic Information System |
3 |
3 |
|
CSE637 |
Data Science for Finance |
3 |
3 |
Fourteen Courses have been included in the Curriculum and Syllabus to introduce M. Sc. in Computer Science and Engineering with a Major in Data Science. Students who want to complete the degree in M.Sc in CSE with a Major in Data Science will have to complete at least 12 credits from the below list of courses. Two core (6 credits) courses and two elective courses (6 credits) must be taken from the following list of courses:
List of Core Courses for major in Data Science:
|
Course Code |
Course Title |
Credit Hours |
Class Hours (per week) |
|
CSE508 |
Fundamentals of Data Science |
3 |
3 |
|
CSE509 |
Statistical and Mathematical Foundations for Data Analytics |
3 |
3 |
|
CSE510 |
Data and Information Ethics |
3 |
3 |
|
CSE511 |
Algorithms for Data Science |
3 |
3 |
List of Elective Courses for major in Data Science:
|
Course Code |
Course Title |
Credit Hours |
Class Hours (per week) |
|
CSE628 |
Data Visualization |
3 |
3 |
|
CSE629 |
Data Science for Health Care |
3 |
3 |
|
CSE630 |
Social Media Data Management and Analytics |
3 |
3 |
|
CSE631 |
Cloud Computing for Data Analytics |
3 |
3 |
|
CSE632 |
Data Engineering |
3 |
3 |
|
CSE633 |
Data Science and Strategic Decision Making |
3 |
3 |
|
CSE634 |
Data Modeling |
3 |
3 |
|
CSE635 |
Advanced Time Series Analysis |
3 |
3 |
|
CSE636 |
Advanced Geographic Information System |
3 |
3 |
|
CSE637 |
Data Science for Finance |
3 |
3 |
The Faculty of Science and Information technology would set up a Thesis/Project Committee for M. Sc. students. The Thesis/Project Committee for Master’s degree program shall consist of at least three, but not more than five, members. At least one member of the Thesis/Project Committee shall be from outside the Department of Computer Science and Engineering of the university. The Thesis/Project Committee will conduct the final oral examination of the thesis or the project report and evaluate the performance of the seminar.
The final grade in each course will be given on the basis of performance on class attendance, in-course examinations, assignments, midterm tests, and final examination as indicated below:
|
Class attendance |
7% |
|
Quiz Marks |
15% |
|
Assignment |
5% |
|
Class presentation |
8% |
|
Midterm Test |
25% |
|
Semester Final Examination |
40% |
|
Total |
100% |
Each student will deliver a seminar talk on the topic of her/his thesis/project or a selected topic. The seminar will be attended by the supervisor(s) of the thesis/project, faculty members, and other research students.
A student will earn letter grades on the basis of his/her performance of the course. The following letter grades are awarded to the students after the completion of the program. The numerical equivalents of the grades and grade points are given below:
|
Marks out of 100 |
Grade |
Grade point Equivalent |
Remarks |
|
80 – 100 |
A+ |
4.00 |
Outstanding |
|
75 – 79 |
A |
3.75 |
Excellent |
|
70 – 74 |
A- |
3.50 |
Very Good |
|
65 – 69 |
B+ |
3.25 |
Good |
|
60 – 64 |
B |
3.00 |
Satisfactory |
|
55 – 59 |
B - |
2.75 |
Above Average |
|
50 – 54 |
C+ |
2.50 |
Average |
|
45 – 49 |
C |
2.00 |
Below Average |
|
40 – 44 |
D |
1.00 |
Pass |
|
00 – 39 |
F |
0.00 |
Fail |