N-DL/481/6/0829(MQA/PA14226)03/28
Bachelor in Software Engineering (Honours) (Application Development)
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The digital economy is revolutionizing business models, leading to an increased need for professionals who can drive business growth through technology adoption. As Industry 4.0 gains momentum, the demand for software engineering skills is skyrocketing to tackle complex business challenges and bridge the ever-widening digital skills gap by upskilling the workforce.
This comprehensive program is designed to cultivate graduates who possess cutting-edge technological expertise and extensive practical experience in the realms of application design, development, and system integration. By delving into the latest technologies, students will gain proficiency in programming and technical skills, software development processes, web and database development, as well as software testing and reengineering. Armed with this knowledge, they will be equipped to tackle real-world problems and undertake projects with confidence.
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“The expert in anything was once a beginner.”
Helen Hayes
Top Core Courses
Fundamentals that will build you up to stay relevant and excellent today, tomorrow and beyond.
Data and AI Essentials
Students will be introduced to AI, Data Science and Machine learning, followed by exploratory data analysis which focuses on data statistics, extracting data, cleaning data and transforming data. Then they will be acquainted with machine learning and AI models.
Machine Learning
Students will be taught how to create and evaluate a model in scikit learn and Azure Machine Learning and manage imbalanced data by improving the models. Subsequently, they will be introduced to the application of classification in text analytics.
Deep Learning
Students will comprehend an intuitive approach to build complex models through deep learning with uncompromised scaling, speed, and accuracy. These help machines solve real-world classification text analytics problems and time series problems with human-like intelligence using CNN, RNN and LSTM.
Statistics for Data Science and AI
Students will be introduced to statistical methods such as linear equations, systems of equations, quadratic equations, polynomials, derivatives and multivariate functions. They will develop visualisations using vectors, matrices eigenvectors and eigen values and apply statistics fundamentals, probability, sampling distributions and hypothesis testing in Data Science and AI scenarios.
R Programming
Students will learn R syntax and how to handle data structures consisting of vectors, matrices, factors, data frames and lists. They will build visualisations using the graphical capabilities of R.
Learning Path
Students will obtain all the fundamentals in the field of Software Engineering that will furnish them with the skills required in the industry, such as developing an application and website by integrating the database and other applications together.
Students will begin their internship from this year onwards to enhance professional development and prepare them for their future career path. In addition, they will gain intermediate knowledge about Software Engineering and the best practices to design, develop, and test the software.
Students will be equipped with specialisation skills connected to Industrial Revolution 4.0 such as Machine Learning, Artificial Intelligence, Data Science and Software Engineering.
Students will continue to learn specialisation skills related to Industrial Revolution 4.0 and undergo Industrial Training.
Students will demonstrate their skills and knowledge in the Final Year Project prior to graduation.
Upon graduation, students can embark on careers such as:
- Software Developer
- Technical Lead
- Solutions Architect
- Cloud Engineer
- Cloud Architect
- DecOps Engineer
- Product Manager
- UX / UI Developer
- Scrum Master
- Automation QA
BACHELOR IN SOFTWARE ENGINEERING (HONOURS) (APPLICATION DEVELOPMENT)
No. | Course Code | Course Title | Credit Hours |
MPU COURSES | |||
1 | MPU3183/03 | Penghayatan Etika dan Peradaban (for Malaysian students); OR | 3 |
MPU3143/03 | Bahasa Melayu Komunikasi 2 (for foreign students only) | 3 | |
2 | MPU3193/03 | Philosophy and Current Issues | 3 |
3 | MPU3213/03 | Bahasa Kebangsaan A (for students without SPM credit in BM); OR | 3 |
MPU3223/03 | Decision Making Skills (for students with SPM credit in BM and foreign students) | ||
4 | MPU3412/02 | Co-curriculum; OR | 2 |
MPU3422/02 | Integriti dan Anti Rasuah (for all students who have not completed KIAR at the cert/ diploma level) | ||
Subtotal Credit Hours | 11 | ||
CORE COURSES | |||
1 | DSE101/03 | Programming Foundations | 3 |
2 | DSE102/03 | Front-end Web Development | 3 |
3 | DSE103/03 | UI Frameworks | 3 |
4 | DSE105/03 | Web Development Foundations | 3 |
5 | DSE104/03 | Database Design and Implementation | 3 |
6 | DSE201/03 | Web Development using Platforms | 3 |
7 | DSE203/03 | Develop Enterprise Applications | 3 |
8 | DSE204/03 | Application Integration | 3 |
9 | DSE205/03 | Application Development & Processes | 3 |
10 | DSE208/03 | Apps Development | 3 |
11 | TCC243/03 | Data Communication and Networking | 3 |
12 | TCC301/03 | Systems Security | 3 |
13 | DSE115/03 | Capstone Project – Web Development | 3 |
14 | DSE211/03 | Capstone Project – Application Development | 3 |
15 | TCC235/03 | Software Engineering | 3 |
16 | TCC125/03 | Software Development Models | 3 |
17 | TSE313/03 | Systems Analysis and Design | 3 |
18 | TSE304/03 | Software Scalability and Reengineering | 3 |
19 | TSE305/03 | Software Project Management | 3 |
20 | TSE310/03 | Software Reliability and Quality Assurance | 3 |
21 | TCC122/03 | Discrete Structures | 3 |
Subtotal Credit Hours | 63 | ||
ELECTIVE COURSES (choose 5) | |||
1 | DSE221/03 | Data and AI Essentials | 3 |
2 | DSE222/03 | Statistics for Data Science and AI | 3 |
3 | DSE223/03 | R Programming | 3 |
4 | DSE242/03 | Machine Learning | 3 |
5 | DSE243/03 | Deep Learning | 3 |
6 | DSE244/03 | Reinforcement Learning | 3 |
7 | TSE307/03 | Computational Logic | 3 |
8 | TCC223/03 | Ethics and Professionalism in Computing | 3 |
9 | TCC239/03 | Data Structures and Algorithms | 3 |
Subtotal Credit Hours | 15 | ||
INDUSTRIAL TRAINING | |||
1 | DSE209/06 | Industrial Training | 6 |
Subtotal Credit Hours | 6 | ||
FINAL YEAR PROJECT | |||
1 | DSE309/04 | Final Project I | 4 |
2 | DSE399/04 | Final Project II | 4 |
Subtotal Credit Hours | 8 | ||
FREE MODULES | |||
1 | DSE220/04 | Creative and Problem Solving | 4 |
2 | DSE218/04 | Collaborating & Working with Others | 4 |
3 | TEC210/03 | Technopreneurship | 3 |
4 | DSE219/03 | Business Communication Skills | 3 |
5 | DBS103/03 | Digital Commerce Foundations | 3 |
Subtotal Credit Hours | 17 | ||
Total Credit Hours | 120 | ||
Prerequisites:
No. | Course Code & Title | Prerequisite |
a | DSE115/03 Capstone Project – Web Development | DSE102/03 |
b | DSE201/03 Web Development using Platforms | DSE102/03 |
c | DSE211/03 Capstone Project – Application Development | DSE115/03 |
d | TCC122/03 Discrete Structures | DSE101/03 |
e | TCC235/03 Software Engineering | DSE101/03 |
f | TCC243/03 Data Communication and Networking | DSE105/03 |
g | TCC301/03 Systems Security | DSE105/03 |
h | TSE304/03 Software Scalability and Reengineering | TCC235/03 |
i | TSE310/03 Software Reliability and Quality Assurance | TCC235/03 |
j | DSE221/03 Data and AI Essentials | DSE101/03 |
k | DSE222/03 Statistics for Data Science & AI | TCC122/03 |
l | DSE223/03 R Programming | DSE101/03 |
m | DSE242/03 Machine Learning | DSE101/03 |
n | DSE243/03 Deep Learning | DSE101/03 |
o | DSE244/03 Reinforcement Learning | DSE101/03 |
p | TCC239/03 Data Structures and Algorithms | DSE101/03 |
q | DSE209/03 Industrial Training | Completed at least 70 credits |
r | DSE309/03 DSE Project I | Completed at least 90 credits |
s | DSE399/03 DSE Project II | DSE309/04 |
IMPORTANT NOTES:
No. | Description | ||||||||||||
1 | MPU3213/03 Bahasa Kebangsaan A or MPU3223/03 Decision Making Skills Students who possess any of the following academic achievements can be exempted from taking MPU3213/03 Bahasa Kebangsaan A but you are required to take MPU3223/03 Decision Making Skills. If you do not have any of these academic achievements, you must take MPU3213/03 Bahasa Kebangsaan A. It is your responsibility to ensure that you have enrolled for the right MPU course.
| ||||||||||||
2 | Electives a) All students must take FIVE (5) elective course. b) Not all electives are offered in a term and therefore, please consult the Regional Centre staff before enrolment. |
(A) Regular Entry
Academic Qualification:
- Matriculation / Foundation
With a minimal CGPA of 2.00 and a credit in Additional Mathematic at SPM level; OR credit in Mathematic and any Science/Technology/Engineering subject at SPM level.
- STPM (Science Stream or equivalent)
2Cs or CGPA 2.00 with a Credit in a Mathematic subject and a Credit in a Science or ICT subject.
- UEC
5 Credits (5Bs) inclusive of a credit in Additional Mathematics
- Diploma
Diploma in Computer Science or Software Engineering or Information Technology or Information System or
Related Diploma in Science and Technology and pass with with minimum CGPA 2.50.
Mathematics Requirement:
- Students MUST have attained a ‘CREDIT’ in ADDITIONAL MATHEMATICS at SPM/O Levels or equivalent; OR
- MUST have attained a CREDIT in MATHEMATICS and a CREDIT in either a SCIENCE, TECHNOLOGY or ENGINEERING subject for SPM/O Levels or equivalent. Students using requirement (2) for admission will need to enrol and pass a supplementary mathematics subject to be taken concurrently in Degree.
Note: Students who do not fulfill either requirements are advised to seek advice from the school before enrolment.
(B) Accreditation of Prior Experiential Learning (APEL)
Accreditation of Prior Experiential Learning (APEL) provides an opportunity for individuals with working experience but lack of formal academic qualifications to pursue their tertiary studies. APEL involves the identification, documentation and assessment of prior experiential learning to determine the extent to which an individual has achieved the desired learning outcomes, for access to a programme of study.
Applicants should comply with the following admission criteria stipulated by the Ministry of Higher Education (MOHE) and Malaysian Qualifications Agency (MQA):
- The candidate should be at least 21 years of age in the year of application and possess relevant work experience.
- Other equivalent qualifications recognised by the Malaysian Government.
Fee Schedule
Level | Total Number of Courses | Total Number of Credits | Tuition Fees (RM) |
Undergraduate | |||
Bachelor’s Degree | 42 – 45 | 120 | 70,600 – 82,800 |
Notes:
- A one-off payment Processing and Administration fee (P&A) is chargeable for new students and varies according to programme.
- Registration, Resource and Services (RR&S) fee is chargeable every semester and varies according to programme.
- All tuition fees are merely indicative and fees are subject to change without notice.
All of the University’s courses are classified into three categories, i.e., lower level, middle level or higher level. Lower-level courses are basic, introductory or foundation courses designed for freshmen. Middle-level courses may require some knowledge of previous study or a certain amount of intellectual maturity. Higher level courses are specialised courses usually designed for those majoring in a particular discipline. The level of a course is indicated by the first digit of the course code, i.e., lower level courses are indicated by the code 1XX/XX, middle level courses by 2XX/XX and higher level courses by 3XX/XX or 4XX/XX. The 2 digits after the slash indicate the credit value of the course. i.e., WLA101/03 is a lower level course of 3 credits while BBM203/05 is a middle level course of 5 credits and TEC305/10 is a higher level course of 10 credits.
Please click on the below link to download the courses.