數據科學及人工智能榮譽理學士
The Bachelor of Science with Honours in Data Science and Artificial Intelligence programme (BSCHDSAIJS/JUPAS JSSU70) aims to provide graduates with the breadth of an advanced understanding of theories and practices in the field of Data Science and Artificial Intelligence.
BSc (Hons) in Data Science & Artificial Intelligence is the 5th curriculum update of the “BSc (Hons) in Statistical Analysis” program which is launched in 2012. The School of S&T has a good practice of regular inspection of the program curriculum to ensure the offered programs meet the market needs. It is no doubt that effective and efficient usage of data in the digital world nowadays can help to foster a better world/society.
However, people who are competent in harvest useful insights from a vast amount of data require a particular set of skills which is different from those offered by traditional academic program curriculums.
To ensure our students to acquire appropriate skills, regular meetings with various parties are arranged. The curriculum of HKMU BSc(Hons) in Data Science & Artificial Intelligence is resulted from the recommendations given by industry professionals, academic professors and employers in business sectors.
To meet the employers' expectations, our curriculum incorporates SAS Institute official training materials and therefore students can take the Industrial certification (e.g. SAS programmer & SAS predictive modeler) examinations right after the completion of the respective courses.
Furthermore, students in this program are encouraged to expand their horizons via international organizations such as The Royal Statistical Society (RSS), The Institute for Operations Research and the Management Sciences (INFORMS), British Computer Society (BCS), Institute of Electrical & Electronics Engineers (IEEE).
Bachelor of Science with Honours in Data Science & Artificial Intelligence (BSCHDSAIJS) 數據科學及人工智能榮譽理學士 has acquired the following external recognitions:
This programme provides multiple entry points: Year 1 Entry through JUPAS and Senior Year Entry through Direct Application at the HKMU website.
| Entry Points | Application Methods | Code |
|---|---|---|
| Year 1 Entry | JUPAS / Direct Application # | JSSU70 / BSCHDSAIJ1 # |
| Senior Year Entry | Direct Application | BSCHDSAIJS |
#Students who are not sitting the HKDSE this year and have an equivalent qualification such as IB or GCE-A Level should apply through [Direct Application].
More and more information are available in the digital society nowadays and new generations should be able to comprehend and utilize them to solve practical problems. Our curriculum does not focus on teaching 'abstract' theories but emphasizing in enabling the student's capability to deliver a complete package of solution to practical problems.
Data science and AI are interdisciplinary as they draw on various disciplines, such as Mathematics, Statistics and Computer Science. Machine Learning is the core of data science and AI. It employs computer algorithms to interpret data and learn from the results for decision making and forecasting. Data science and AI can push forward business development, optimize business and operations, and create more attractive operating models. Data sources may come from tables, relational databases, texts, videos, audios, images, etc. Data scientists / AI specialists use data science methods, deep learning, machine learning and AI to discover clients, products, services, operations and market insights.
Graduates will be ready for entry-level roles of data scientists, AI specialist, data engineers, and data analysts in commercial firms and public institutions. To achieve this goal, the program aims to equip our graduates with four main competitive edges.
HKMU Computing Graduates regularly show their strength in problem solving and academic paper writing in inter-varsity contests and competitions. Since 2010, they have won over 30 prizes and awards, affirming their competitiveness among the UGC universities.
Please refer to the Student Achievements or Best Projects page for more details.
Data scientist is ranked number 1 in the United States (LinkedIn’s Most Promising Jobs of 2019), whereas AI specialist is ranked number 1 among the top 10 roles in the United States (LinkedIn’s 2020 Emerging Jobs Report).
Hong Kong has been lagging behind the US and the UK in the size of the data science and AI job market, but now the demand is very strong as typically hundreds of job vacancies are available at any one time. While many data science/AI jobs are operating in the business context, there are still a wide range of roles and job titles, reflecting the multi-talented nature of the area.
Graduates will be ready for entry-level roles of data scientists, AI specialists, data engineers, and data analysts in commercial firms and public institutions.
Graduates of this programme have been admitted to various postgraduate programmes in local and overseas universities. Graduates may choose to study for a postgraduate degree in an advanced area in Data Science or Artificial Intelligence or in other areas for the broadening of their exposure and skill set. Even pursuing for a doctoral research degree.
Programme Leader
Dr. Tony Chan
Tel: 3120 2612
Email: tmtchan@hkmu.edu.hk
The 4-year programme consists of a balanced set of subject-area courses, language courses, and general education courses.
Core Courses: Provide training in some of the major pillars in modern computing: processing of information, networking of information, and management of information.
Include programming, software development, software engineering, computing infrastructure, and databases.
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| MATH S141F | Algebra and Calculus | 5 | Foundation | – |
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This course teaches fundamental concepts in calculus and linear algebra. The course aims to provide a transition between school and university mathematics. The course also aims to develop in students understanding the concepts and techniques of differentiation, integration, vectors and matrix operations. |
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| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| STAT S151F | Probability & Distributions | 5 | Foundation | – |
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This course is intended to provide conceptual understandings of Probability & Distributions. Probability & Distributions has wide applications to diverse areas in statistics. |
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| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S202F | Java Programming Fundamentals | 5 | Middle | b |
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Java is one of the most popular languages in the IT professional world. The aim of this course is to provide students with sound foundation in software development using the object-oriented programming language Java. The course will cover fundamental object-oriented programming concepts such as classes and objects, and the structure of text-based Java applications. Students will study how to analyze problems and apply object-oriented methodology in software development. |
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| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S203F | Intermediate Java Programming and User Interface Design | 5 | Middle | b |
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This course aims to provide students with more knowledge in Java programming as well as an introduction to user interface design. Students will study how to design computer user interface, and develop graphical-based computer programs using the Java programming language. Students will also study how to analyse more difficult problems and apply object-oriented methodology in development. |
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| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S208F | Introduction to Computer Programming | 5 | Middle | b |
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This is intended to be a first course in computer programming. In this course, students will study how to write computer programs in the Python language to solve simple computing problems. Students will use fundamental programming and data containers to ease programming effort and to allow writing larger programs to solve problems. Topics include variables, operators, control structures, arrays and strings. |
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| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S209F | Data Structures, Algorithms, and Problem Solving | 5 | Middle | b |
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As a sequel to COMPS208F, the aim of this course is to facilitate students to acquire skills for writing larger programs effectively. Students will study how program structures, software development process, data structures, and algorithms can be used to deal with larger programming tasks. Topics include modular programming, abstract data types, searching and sorting, recursion, and data structures such as linked lists, binary trees, and hash-tables. |
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| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S264F | Discrete Mathematics | 5 | Middle | b |
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The aim of this course is to lay the foundation of discrete mathematics of students which will be used in studying other more advanced programming courses. Students will study the concepts of discrete mathematics, using different techniques for analysing and solving discrete mathematical problems. Topics include sets, functions, equivalence and induction. |
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| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| STAT S251F | Statistical Data Analysis | 5 | Middle | b |
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| STAT S261F | Data Analytics with Applications | 5 | Middle | b |
| This course aims to introduce a range of topics and concepts related to the data science process. Students will learn what Data Science is and the skill sets needed to be a data scientist, how to use R to carry out basic statistical modeling and analysis. Students will also learn the significance of exploratory data analysis (EDA) in data science, the Data Science Process and how its components interact. | ||||
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| STAT S263F | Big Data Analytics with Applications | 5 | Middle | b |
Core Courses: Provide training in some of the major pillars in modern computing: processing of information, networking of information, and management of information.
Include programming, software development, software engineering, computing infrastructure, and databases.
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S320F | Database Management | 5 | Higher | a or b |
|
This course aims to provide an explanation of the concepts underlying all relational databases as well as practical experience in applying the concepts in different situations. Students should develop knowledge about the role of databases and database management systems within the context of information systems. Students should also develop skills in using the database language SQL. Major topics include database environment, database architectures, relational model and database design. |
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| Advisory Pre-requisites: Students should have completed most foundation and middle level computing programming courses. | ||||
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S321F | Advanced Database and Data Warehousing | 5 | Higher | a or b |
|
As a sequel to COMPS320F, this course aims to provide students with more advanced concepts of relational databases and more practical experience in different situations. Students will study more advanced concepts and theories about relational databases. Major topics include Entity-Relation model, normalization, transaction management, and other advanced topics. |
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Advisory Pre-requisites: Students should have completed most foundation and middle level computing courses. |
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| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMPS 333F | Advanced Programming and AI Algorithm | 5 | Higher | a or b |
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This course aims to introduce basic concepts and algorithms of artificial intelligence (AI) and to facilitate students to develop advanced programming skills to tackle sophisticated problems, especially using AI algorithms and techniques. Students will be able to explain the capabilities, strengths and limitations of various AI techniques, as well as AI algorithms and their applications. Students also learn how to apply AI algorithms and programming methods to solve real world problems, and write programs to implement the devised algorithmic solutions. |
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| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S350F | Software Engineering | 5 | Higher | a or b |
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he course aims to develop in learners the terminology, notations and understanding needed for effective communication with team members during software engineering activities. It also aims to equip learners with the skills to apply software engineering methods and tools in a variety of situation. |
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| Advisory Pre-requisites: Students should have completed most foundation and middle level computing programming courses. | ||||
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S351F | Software Project Management | 5 | Higher | a or b |
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As a sequel to COMPS350F, This course aims to develop in learners the know-how of project management recognized as good practices in software development. |
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| Advisory Pre-requisites: Students are expected to have completed at least two semester courses in computer programming and COMP S350F Software Engineering. | ||||
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S381F | Server-side Technologies and Cloud Computing | 5 | Higher | a or b |
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This course introduces some of the contemporary techniques, technologies and tools for designing, constructing and deploying flexible server-side Internet applications. One of the main focuses of this course is to explain and demonstrate the use of cloud computing technologies. Students will learn how to design, construct and deploy scalable and cost-effective applications that are readily deployable on commercial cloud platforms.The topic may include tools for cloud app development, essential cloud technologies such as linux & git, NoSQL, server-side scripting, server-side MVC, service-oriented architecture, RESTful web services, virtualization, cloud computing concept, service and pricing models, writing and deploying cloud apps. |
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Advisory Pre-requisites: Students should have completed most foundation, middle and higher level computing courses. |
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| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S382F | Data Mining and Analytics | 5 | Higher | a or b |
|
This course introduces the key concepts, techniques and tools that would allow hidden patterns of data to be uncovered. Key topics of this course include: data warehousing, the data mining process, classification, regression, clustering and association mining. Students will learn how to apply this knowledge to solving typical data mining problems through case studies of real-world applications of data mining techniques. The topics may include data mining and data warehousing concepts, data mining process and software, classification and regression methods, clustering algorithms, and association rule mining. |
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| Advisory Pre-requisites: Students should have completed most foundation, middle and higher level computing courses. | ||||
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S460F | Advanced Topics in Data Mining | 5 | Higher | a or b |
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S492F | Machine Learning | 5 | Higher | a or b |
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This course aims to introduce students to the field of machine learning, and develop them to apply machine learning algorithms to real-world problems. It enables students to have a broad overview of different machine learning and deep learning algorithms with a focus of applying these algorithms into real-world problems through practical activities. |
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| Advisory Pre-requisites: Students should have a solid background in computing. | ||||
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S493F | Deep Learning | 5 | Higher | a or b |
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| STAT S311F | Time Series Analysis and Forecasting | 5 | Higher | a or b |
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| STAT S366F | SAS Programming | 5 | Higher | a or b |
Elective and Project Courses: Expose students to specialized topics related to Internet Technology.
The final year project courses provide an opportunity to develop in-depth knowledge and high-level thinking process in a research and development project or an advanced software development project.
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| STAT S461F | Data Science Project | 10 | Higher | a or b |
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This is a project course. Students will attempt a final year project which should provide an opportunity to integrate knowledge and skills acquired in the programme of study. |
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Elective and Project Courses: Expose students to specialized topics related to Internet Technology.
The final year project courses provide an opportunity to develop in-depth knowledge and high-level thinking process in a research and development project or an advanced software development project.
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| COMP S413F | Application Design and Development on Mobile Devices | 5 | Higher | a or b |
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The course aims to provide students with a foundation in designing and developing applications for mobile devices. It enables students to understand the principle and learn skills to create and deploy mobile applications with a mobile programming platform. |
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| Advisory Pre-requisites: Students should have completed most computing courses and should have a solid knowledge in Java. | ||||
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| ELEC S425F | Computer and Network Security | 5 | Higher | a or b |
|
This course intended for senior students. This course covers principles of computer systems and network security. This courses also discuss various attack techniques and how to defend against them. Topics include network attacks and defenses, malware and social engineering attacks, host security, application security, network security, data security, access control and authentication, and cryptography and encryption. |
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Advisory Pre-requisites: Students should have completed most foundation, middle and higher level computing courses and should have a solid knowledge in networking or have completed ELEC S305F. |
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Other Activities: In addition to the development of technical knowledge and skills, students are expected to develop their soft skills such as teamwork and communication.
Students are encouraged to participate in various contests, seminars, and workshops for sharpening their competitiveness.
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| Course List | 5 | Depends on selection | – | |
| Course List | 5 | Depends on selection | – | |
Other Activities: In addition to the development of technical knowledge and skills, students are expected to develop their soft skills such as teamwork and communication.
Students are encouraged to participate in various contests, seminars, and workshops for sharpening their competitiveness.
| Code | Title | Credits | Course Level | Honours Classification |
|---|---|---|---|---|
| Course List | 5 | – | – | |
| Course List | 5 | – | – | |
| Course List | 5 | – | – | |
| Course List | 5 | – | – | |
Students admitted at the Year 1 Entry Point are required to complete 160 credits and of which no more than 40 credits should be taken at foundation level during the nominal 4-year study period for the degree of Bachelor of Science with Honours in Data Science and Artificial Intelligence.
| Categories | Weightings |
|---|---|
| Core Computing Courses | 110 Credits |
| Project Courses and Elective Courses | 20 Credits |
| English Enhancement Courses | 10 Credits |
| General Education Courses | 20 Credits |
| Total | 160 Credits |
| Code | Title | Category | Credits | Course Level | Honours Classification |
|---|---|---|---|---|---|
| MATH S141F | Algebra and Calculus | Core | 5 | Foundation | - |
| STAT S151F | Probability and Distributions | Core | 5 | Foundation | - |
| COMP S208F | Introduction to Computer Programming | Core | 5 | Middle | b |
| STAT S261F | Data Analytics with Applications | Core | 5 | Middle | b |
| English Enhancement Course | ENG | 5 | Depends on selection | - | |
| English Enhancement Course | ENG | 5 | Depends on selection | - | |
| General Education Course | GE | 5 | - | - | |
| General Education Course | GE | 5 | - | - |
| Code | Title | Category | Credits | Course Level | Honours Classification |
|---|---|---|---|---|---|
| COMP S202F | Java Programming Fundamentals | Core | 5 | Middle | b |
| COMP S203F | Intermediate Java Programming and User Interface Design | Core | 5 | Middle | b |
| COMP S209F | Data Structures, Algorithms, and Problem Solving | Core | 5 | Middle | b |
| MATH S262F | Linear Algebra | Core | 5 | Middle | b |
| STAT S251F | Statistical Data Analysis | Core | 5 | Middle | b |
| STAT S263F | Big Data in Organizations | Core | 5 | Middle | b |
| General Education Course | GE | 5 | - | - | |
| General Education Course | GE | 5 | - | - |
| Code | Title | Category | Credits | Course Level | Honours Classification |
|---|---|---|---|---|---|
| COMP S320F | Database Management | Core | 5 | Higher | a or b |
| COMP S333F | Artificial Intelligence Algorithms | Core | 5 | Higher | a or b |
| COMP S350F | Software Engineering | Core | 5 | Higher | a or b |
| COMP S351F | Software Project Management | Core | 5 | Higher | a or b |
| COMP S382F | Data Mining and Analytics | Core | 5 | Higher | a or b |
| COMP S492F | Machine Learning | Core | 5 | Higher | a or b |
| STAT S311F | Time Series Analysis and Forecasting | Core | 5 | Higher | a or b |
| STAT S366F | SAS Programming | Core | 5 | Higher | a or b |
| Code | Title | Category | Credits | Course Level | Honours Classification |
|---|---|---|---|---|---|
| STAT S461F | Data Science Project | Project | 10 | Higher | a or b |
| COMP S321F | Advanced Database and Data Warehousing | Core | 5 | Higher | a or b |
| COMP S381F | Server-side Technologies and Cloud Computing | Core | 5 | Higher | a or b |
| COMP S460F | Advanced Topics in Data Mining | Core | 5 | Higher | a or b |
| COMP S493F | Deep Learning | Core | 5 | Higher | a or b |
| COMP S413F | Application Design and Development For Mobile Devices | Elective | 5 | Higher | a or b |
| ELEC S425F | Computer and Network Security | Elective | 5 | Higher | a or b |
Students admitted at the Year 3 Entry Point are required to complete 80 credits during the nominal 2-year study period for the degree of Bachelor of Science with Honours in Data Science and Artificial Intelligence.
| Categories | Weightings |
|---|---|
| Core Computing Courses | 60 Credits |
| Project Courses and Elective Courses | 20 Credits |
| Total | 80 Credits |
| Code | Title | Category | Credits | Course Level | Honours Classification |
|---|---|---|---|---|---|
| COMP S320F | Database Management | Core | 5 | Higher | a or b |
| COMP S333F | Artificial Intelligence Algorithms | Core | 5 | Higher | a or b |
| COMP S350F | Software Engineering | Core | 5 | Higher | a or b |
| COMP S351F | Software Project Management | Core | 5 | Higher | a or b |
| COMP S382F | Data Mining and Analytics | Core | 5 | Higher | a or b |
| COMP S492F | Machine Learning | Core | 5 | Higher | a or b |
| STAT S311F | Time Series Analysis and Forecasting | Core | 5 | Higher | a or b |
| STAT S366F | SAS Programming | Core | 5 | Higher | a or b |
| Code | Title | Category | Credits | Course Level | Honours Classification |
|---|---|---|---|---|---|
| STAT S461F | Data Science Project | Project | 10 | Higher | a or b |
| COMP S321F | Advanced Database and Data Warehousing | Core | 5 | Higher | a or b |
| COMP S381F | Server-side Technologies and Cloud Computing | Core | 5 | Higher | a or b |
| COMP S460F | Advanced Topics in Data Mining | Core | 5 | Higher | a or b |
| COMP S493F | Deep Learning | Core | 5 | Higher | a or b |
| COMP S413F | Application Design and Development For Mobile Devices | Elective | 5 | Higher | a or b |
| ELEC S425F | Computer and Network Security | Elective | 5 | Higher | a or b |
Starting from 2018 admission, the JUPAS entry point of the programme is included in the Study Subsidy Scheme for Designated Professions/Sectors (SSSDP). Eligible students will receive $44,240 subsidy per annum.
This SSSDP programme normally admits students through JUPAS. The JUPAS code is JSSU70.
Please refer to SSSDP's website for eligibility and more information.
For Senior Year entry students, they may supported by Non-means-tested Subsidy Scheme (NMTSS). Eligible students will receive $33,200 subsidy per annum.
Please refer to NMTSS's website for eligibility and more information.
This programme provides multiple entry points: Year 1 Entry through JUPAS and Senior Year Entry through Direct Application at the HKMU website.
| Entry Points | Application Methods | Code |
|---|---|---|
| Year 1 Entry | JUPAS / Direct Application # | JSSU70 / BSCHDSAIJ1 # |
| Senior Year Entry | Direct Application | BSCHDSAIJS |
#Students who are not sitting the HKDSE this year and have an equivalent qualification such as IB or GCE-A Level should apply through [Direct Application].
Students should normally have attained in the Hong Kong Diploma of Secondary Education (HKDSE) Examination results of Level 3 or above in Chinese and English, as well as Level 2 or above in Mathematics, Liberal Studies and an elective subject.
Please refer to JUPAS website for more JUPAS admission information
Should there be unfilled intake places after all admission rounds of JUPAS, the SSSDP participating institution will admit local non-JUPAS applicants with a recognized Higher Diploma or Associate Degree via direct admission of no more than 10% of the subsidised places of each selected programme (subject to SSSDP rules that are to be announced).
The amount of subsidy for the JSSU70 programme under the SSSDP is HK$44,240 per annum. The subsidy is tenable for the normal duration of the study programme concerned and is subject to the students' satisfactory fulfilment for progression in the study programme. The government's terms and conditions apply.
Tuition Fee after SSSDP subsidy:
| First Year | HK$34,080* |
| Total | HK$136,320* |
*Please refer to the JUPAS page for updates and details.
*The estimated tuition fees listed above are for reference only. Tuition fees are charged according to the number of course credits taken by a student. A student will normally take 40 credits in an academic year.
*The subsidy is tenable for the normal duration of the study programme concerned and is subject to the students' satisfactory fulfilment for progression in the study programme
This programme has offered degree articulation opportunities for local Higher Diploma and Associate Degree holders.
The curriculum is designed to be academically rich and practically oriented for preparing local sub-degree holders to become highly competent computing professionals.
Students admitted at a senior year entry point will join other students from Year 1 entry in pursuing of the degree. They can make use of the chance to build a strong personal network with their peers and many alumni of this programme who are doing well in their career.
Please refer to the program's website for below or more information
Jonathan handles all external affairs include business development, patents write up and public relations. He is frequently interviewed by media and is considered a pioneer in 3D printing products.
After graduating from OUHK, Krutz obtained an M.Sc. in Engineering Management from CityU. He is now completing his second master degree, M.Sc. in Biomedical Engineering, at CUHK. Krutz has a wide range of working experience. He has been with Siemens, VTech, and PCCW.
Hugo Leung Wai-yin, who graduated from his four-year programme in 2015, won the Best Paper Award for his ‘intelligent pill-dispenser’ design at the Institute of Electrical and Electronics Engineering’s International Conference on Consumer Electronics – China 2015.
The pill-dispenser alerts patients via sound and LED flashes to pre-set dosage and time intervals. Unlike units currently on the market, Hugo’s design connects to any mobile phone globally. In explaining how it works, he said: ‘There are three layers in the portable pillbox. The lowest level is a controller with various devices which can be connected to mobile phones in remote locations. Patients are alerted by a sound alarm and flashes. Should they fail to follow their prescribed regime, data can be sent via SMS to relatives and friends for follow up.’ The pill-dispenser has four medicine slots, plus a back-up with a LED alert, topped by a 500ml water bottle. It took Hugo three months of research and coding to complete his design, but he feels it was worth all his time and effort.
Hugo’s public examination results were disappointing and he was at a loss about his future before enrolling at the OUHK, which he now realizes was a major turning point in his life. He is grateful for the OUHK’s learning environment, its industry links and the positive guidance and encouragement from his teachers. The University is now exploring the commercial potential of his design with a pharmaceutical company. He hopes that this will benefit the elderly and chronically ill, as well as the society at large.
Soon after completing his studies, Hugo joined an automation technology company as an assistant engineer. He is responsible for the design and development of automation devices. The target is to minimize human labor and increase the quality of products. He is developing products which are used in various sections, including healthcare, manufacturing and consumer electronics.
| Course Code | Title | Credits | |
|---|---|---|---|
| COMP S321F | Advanced Database and Data Warehousing | 5 | |
| COMP S333F | Advanced Programming and AI Algorithms | 5 | |
| COMP S351F | Software Project Management | 5 | |
| COMP S362F | Concurrent and Network Programming | 5 | |
| COMP S363F | Distributed Systems and Parallel Computing | 5 | |
| COMP S382F | Data Mining and Analytics | 5 | |
| COMP S390F | Creative Programming for Games | 5 | |
| COMP S492F | Machine Learning | 5 | |
| ELEC S305F | Computer Networking | 5 | |
| ELEC S348F | IOT Security | 5 | |
| ELEC S371F | Digital Forensics | 5 | |
| ELEC S431F | Blockchain Technologies | 5 | |
| ELEC S425F | Computer and Network Security | 5 |
| Course Code | Title | Credits | |
|---|---|---|---|
| ELEC S201F | Basic Electronics | 5 | |
| IT S290F | Human Computer Interaction & User Experience Design | 5 | |
| STAT S251F | Statistical Data Analysis | 5 |
| Course Code | Title | Credits | |
|---|---|---|---|
| COMPS333F | Advanced Programming and AI Algorithms | 5 | |
| COMPS362F | Concurrent and Network Programming | 5 | |
| COMPS363F | Distributed Systems and Parallel Computing | 5 | |
| COMPS380F | Web Applications: Design and Development | 5 | |
| COMPS381F | Server-side Technologies and Cloud Computing | 5 | |
| COMPS382F | Data Mining and Analytics | 5 | |
| COMPS390F | Creative Programming for Games | 5 | |
| COMPS413F | Application Design and Development for Mobile Devices | 5 | |
| COMPS492F | Machine Learning | 5 | |
| ELECS305F | Computer Networking | 5 | |
| ELECS363F | Advanced Computer Design | 5 | |
| ELECS425F | Computer and Network Security | 5 |
