Higher Diploma in Information Technology and Data Science (Mandarin)

(E-Learning)

Aims & Learning Outcomes

The Higher Diploma in Information Technology and Data Science (Mandarin) (E-Learning) is an online course intended to teach students in a virtual learning environment using Mandarin as the teaching medium.  

The course is designed to enhance academic and vocational advancement by fostering the acquisition of knowledge, essential skills, and the ability to engage in independent and lifelong learning. It aims to equip learners with problem-solving and decision-making capabilities that will prove valuable both in academic pursuits and professional endeavors related to accounting and big data coursework. The course offers an engaging and easily accessible learning experience, providing students with a comprehensive grasp of accounting and big data principles, analysis techniques, and contemporary accounting and big data issues, which they can subsequently apply in their future careers.  

The programme aims to: 

  1. Develop students’ skills and equip them with the ability to apply mathematics, statistics, and fundamental big data principles to identify, express, analyse,  draw reasonable and effective conclusions through information synthesis and solve complex problems in the context of big data applications.  
  2. Foster innovative thinking in learners taking into consideration social, health, safety, legal, cultural, and environmental factors in designing solutions for big data application problems, including the creation of big data system architectures, modules, and implementation processes that align with user needs.  
  3. Develop students’ abilities to select, develop, and utilize appropriate technologies, resources, modern engineering tools, and information technology tools to analyse, design, simulate, or implement solutions in big data applications.  
  4. Provide students with knowledge in finance, e-commerce, or a specific application field, and equip them with the skills to apply this knowledge within a multidisciplinary environment. 
  5. Develop students’ understanding of and ability to assume various roles, including individuals, team members, and leaders, within a team; cultivate their skills in actively listening to the opinions and suggestions of other team members and leveraging the advantages of teamwork.  
  6. Enhance students’ ability to develop their international perspective and effectively communicate complex issues with peers and the public. 
  7. Instil in students the awareness of independent learning and lifelong learning , stay updated with the latest advancements and adapt continuously to the development in the field of data science. 

               

              Learning Objectives 

              At the end of this programme, students will be able to: 

              • iDevelop knowledge and understanding of information technology and big data concepts, terms and theories
              • iAnalyse problems related to the modules studied
              • iDetermine the suitability of several approaches to resolving challenges presented within the modules
              • iBe equipped with the ability to apply mathematics, statistics, and big data engineering fundamentals to solve related problems in big data applications
              • iDevelop the ability to analyse problems and design solutions for big data applications, taking into consideration social, health, safety, legal, cultural, and environmental factors
              • iDemonstrate personal effectiveness, communication, interpersonal skills, and time management skills as they collaborate with their peers

              Course/ Assessment Structure

              This Higher Diploma programme is of thirty six  (36) months duration and students are required to study thirteen (13) Core plus  four (4) specialist modules in the specialism of interest. There is a total of 240 credits for the entire programme and each credit is equal to 10 learning hours. 

              Core Modules

              1. Fundamentals of Computers and Big Data
              2. Computer Mathematics 
              3. Language Programming 
              4. Java Programming 
              5. Data Structure and Algorithm Design 
              6. Database and Data Warehouse 
              7. Python Language Programming 
              8. Operating Systems 
              9. Big Data Batch Processing Technology and Its Platform 
              10. Data Mining Techniques  
              11. Big Data Analysis Technology 
              12. Big Data Real-Time Processing Technology and Platform  
              13. Machine Learning Fundamentals 

                            Elective Stream A

                            Financial Big Data Stream 

                            1. Financial Data Processing Technology
                            2. Financial Big Data Application  
                            3. Blockchain Technology and Application  
                            4. Internet Finance 

                            Elective Stream B

                            E-commerce Stream 

                            1. Big Data Marketing
                            2. Text Mining and Analysis  
                            3. Intelligent Recommendation technology and application   
                            4. E-Commerce Big Data Application Practice
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                              Admission Requirement

                              1. a. Normal Entry 

                              1. 1. Min age: 17 years PLUS 

                              1. 2. Academic Level: 

                              • Completed A-Level with 2 passes OR  
                              • Completed equivalent to at least 12 years of formal education  

                              1. 3. Min language requirement: 

                              • Obtained at least GCE O-Level B4 in Chinese language or equivalent 

                              b. Alternative entry 

                              Mature candidate of minimum age of thirty (30) with at least eight (8)  years working experience. 

                              Delivery and Assessment

                              1. Students will be taught in a virtual learning environment, where they will study a digital-based curriculum taught by qualified and trained educators. Students will typically attend live-streamed online lectures/lessons. Self-paced (asynchronous) instruction will supplement the live online classes.  

                                The teaching, learning and assessment strategy of each module has been designed to ensure that teaching methods are appropriate for achieving the learning objectives of the module, students’ transferable skills are utilised in the learning and assessment strategies of the module. 

                                A range of teaching and learning strategies are employed to suit the various types of learners. Typically, lectures are supported by smaller group tutorials and seminars, case workshops, project and group-based activities, gaming simulations, etc. Students are also given independent learning activities. Where appropriate, student learning is supported by materials, tasks and activities provided via a virtual learning environment. 

                                Assessment Strategy: 

                                Assessments will be conducted via a robust online examination platform, integrated into the LMS, to ensure that online examinations are systematically scheduled, conducted and evaluated with integrity. 

                                A wide variety of assessment methods will be employed suitable with the respective module being assessed. 

                              1.  

                              Duration

                              1. This is a part-time course and will run for 36 months. Delivery will be for 3 hours per day for 3 days every week for a total of 672 hours. Students are expected to complete the course in 36 months.  

                                Minimum class size is at 30 students. 

                                Maximum candidature period for a student to complete the course is 36 months. 

                              Intake Dates

                              1. Semestral intake

                              Target Students

                              1. The target students are existing students of Wuhan Technology and Business University in China who intend to acquire a USC certification on a part-time basis while also undergoing their degree education at the university.  

                                 

                              Fee Structure

                              1. Course Fee: SGD 13,000.00

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