Computer Science and Engineering

Computer Science and Engineering

Graduate

 

Program Objectives

 The objectives of the M. Sc. program in Computer Science and Engineering are:

  • To produce engineers with the ability to apply technical knowledge and skills with creativity.
  • To promote the intellectual growth of the students admitted to the program.
  • To develop competence necessary for effective computing involving computer hardware and software.
  • To develop the research and analytical skills necessary for computer science and engineering.

Admission Requirements 

The requirements for admission in Master’s degree program are:

  • Completion of the bachelor’s degree from a university or an accredited institution of higher education.
  • The applicant must have a CGPA of 2.5 or above (in a scale of 4.0), or at least second class in the bachelor’s
  • The applicant must have completed the enlisted prerequisite courses or their
  • Applicant, not completing the enlisted prerequisite courses, will be admitted on condition that she/he completes these courses in one or two

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.

Course Requirements

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.

Prerequisite Courses: 24 Credits

(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.

Major Core Courses: 12 Credits

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

 

Elective Courses: 15 Credits for project students and 6 credits for thesis students

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

 

 

Requirement & Specifications for M.Sc. in CSE with Major in Data Science

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

 

Thesis Committee and Oral Examination:

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.

Grading and Performance Evaluation:

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