Program Name: Mathematics and Informatics
Training qualifications: Master of Science
Major: Mathematics and Informatics
Training orientation: Research
Diploma: Master of Science
1. Training objectives
1.1 General objective
- To create high-quality human resources in the field of Mathematics and Informatics to meet the demands of agencies, organizations, enterprises… to serve the developing requirements of economy, society, security and national defense, international integration;
- To train masters with solid knowledge of applied mathematics majors, mathematical foundations for informatics, inherit and promote at a higher level than university-level competencies in computer science majors: understanding and mastering the foundations required mathematics, advanced specialized knowledge of a number of applied models of mathematics and informatics, strong in theory and good practical skills in algorithm implementation and scientific computation, access to scientific research activities;
- To train math-informatics experts with high professional qualifications, research ability and creative ability, apply knowledge to solve theoretical or practical problems, be able to teach at university level, adapt and effectively meet the requirements of society in the process of globalization or continue to study and research at a higher level.
1.2 Specific objective
At the end of the program, students major in research-oriented Mathematics and Informatics have the following competencies:
- Having solid foundational and professional knowledge, capable of participating in solving related problems in the field of applied mathematics or computer science;
- Having the ability to teach Maths – Informatics and good expression of applied Maths – Informatics problems;
- Having professional skills and personal qualities necessary for career success: scientific and professional working methods, good systems thinking and analytical thinking; being able to integrate with an international environment;
- Having social skills necessary to work effectively in a multidisciplinary team and integrate with an international environment;
- Having the ability to self-train, self-update knowledge and self-doing scientific research; ability to explore practical problems, apply knowledge and creative scientific and technical achievements to solve real-life problems.
2. Learning outcomes
Graduates of the Mathematics and Informatics program have the following professional knowledge, skills and competencies:
| Learning Outcomes | Competency Level (*) | |
| 1 | Apply professional knowledge to be able to work effectively in the field of application of Mathematics and Informatics to meet the requirements of modern society | 5 |
| 1.1 | Have the ability to effectively apply basic knowledge of mathematics, computer science and basic science, know how to explore, process and evaluate the value of scientific information. | 5 |
| 1.2 | Have the ability to well apply core professional knowledge, adapt to different jobs in the field of computer science (describe, calculate and simulate systems, processes and build software; research, analyze, develop solutions, design processes…). | 4 |
| 1.3 | Have the ability to teach and research mathematics and informatics in universities and research institutes; or continue to study as a doctoral student majoring in mathematics – informatics. | 4 |
| 1.4 | Have the ability to apply knowledge of mathematics in analyzing and solving a specific theoretical or practical problem. | 4 |
| 2 | Professional skills and personal qualities required for career success | 5 |
| 2.1 | Have skills in idea-discovery, argument, analysis, synthesis, questioning and problem-solving in theory or in practice. | 6 |
| 2.2 | Have systematic, synthetic, logical and critical thinking. | 5 |
| 2.3 | Creative, persistent and serious, with ethics and professional responsibility. | 5 |
| 2.4 | Have skills to research, experiment and explore knowledge, self-training skills and quickly adapt to the development of science and technology and to real life. | 5 |
| 2.5 | Have a good understanding of contemporary issues and a sense of lifelong learning. | 5 |
| 3 | Social skills needed to work effectively in a multidisciplinary, multicultural and multinational working environment | 5 |
| 3.1 | Have the ability to work independently and have organizational skills and teamwork skills. | 5 |
| 3.2 | Communicate effectively through writing, presentation, discussion, negotiation, mastering of situations, proficient and effective use of modern information processing tools and means. | 4 |
| 3.3 | Have good English skills, English level equivalent to B1 level. | 4 |
| 4 | Ability to analyze, form ideas, participate in the design, implementation and administration of mathematical and informatical models to solve problems of organization and society | 4 |
| 4.1. | Have the ability to detect problems, synthesize, analyze and exploit scientific, social and economic information domestically and internationally. | 4 |
| 4.2. | Understand the environment and operations of domestic and international organizations, financial institutions, and laws. | 4 |
| 4.3 | Have the ability to discover ideas, build and develop projects, systems as well as implement applied math – informatics solutions and products according to the requirements of economic and social organizations. | 4 |
(*) Notes on Bloom’s capacity scale:
| Meaning | |
| 1 | Have the ability to remember |
| 2 | Have the ability to understand |
| 3 | Have the ability to apply |
| 4 | Have the ability to analyze |
| 5 | Have the ability to synthesize |
| 6 | Have the ability to evaluate |
3. Whole courses mass of knowledge
| Group of knowledge | Master of science | |
| 1 | General Knowledge:- Philosophy- English (not counting the number of credits, requiring students to get the standard output) | 3 credits |
| 2 | Foundation major courses, advanced major courses (required) | 15 credits |
| 3 | Research-oriented or apply-oriented major courses (optional) | 12 credits |
| 4 | Graduation thesis | 15 credits |
| Total: | 45 credits |
4. Admission and enrollment
4.1 About the entrance exam
Candidates must take the entrance exam with 3 following subjects:
- Advanced Maths
- English
- Computational Algebra
4.2 About the candidates
Candidates must have graduated from university in one of the following groups:
| CONVENTION CODE OF STUDENTS GROUP | ||||||
| Graduated major | Graduated university | |||||
| Graduated from Hanoi University of Science and Technology (*) | Other universities, | |||||
| Right major | Mathematics, Mathematics – Informatics, Information Technology | A1 | A2 | |||
| Near major | Electronics and Telecommunications, Mecha-informatics, Mechatronics | B1 | B2 | |||
| (*) and other universities that HUST recognizes credits in the university curriculum | ||||||
- Candidates who are exempted from courses and those who have to take additional courses will be considered and decided by the School of Applied Mathematics and Informatics.
- Other candidates are decided by the School of Applied Mathematics and Informatics.
- For those who register for application-oriented learning: no seniority is required.
5. Training duration
- The training program follows the credits-based system.
- The standard designed training program is 1.5 years (3 main semesters).
6. Exemption
List of students considered for exemption will be considered by the council on a case-by-case basis for the students in group A1 having the engineering degree from University of Science and Technology (*) according to the list of courses of the practical program but not more than 15 credits.
7. Training process and graduation conditions
The training process is organized according to the credits-based system, in accordance with the Regulations on organization and management of postgraduate training of Hanoi University of Science and Technology, promulgated under Decision No ………./QĐ-ĐHBK-SĐH on …… month ….. year …………. of the Principal of Hanoi University of Science and Technology.
8. Grading scale
Letter grading scale (A, B, C, D, F) and corresponding 4-point scale are used to assess official learning outcomes. A 10-point scale is used for the component scores of the module.
| 10-point scale (component) | 4-point scale | |||||
| Letter grading | Score | |||||
| Pass* | from | 8.5 | to | 10 | A | 4 |
| from | 7.0 | to | 8.4 | B | 3 | |
| from | 5.5 | to | 6.9 | C | 2 | |
| from | 4.0 | to | 5.4 | D | 1 | |
| Not Passed | Below 4.0 | F | 0 |
* Graduation Thesis Only: A grade of C or higher is considered a pass.
9. Curriculum
| Group of knowledge | Course code | Course name | Credits | Volume |
|---|---|---|---|---|
| General knowledge | SS6010 | Philosophy | 3 | |
| FL6010 | English | Self-study | ||
| Required knowledge (15 credits) | MI5032 | Optimal Control | 2 | 2(2-1-0- 4) |
| MI5042 | Random Models and Applications | 2 | 2(2-1-0-4) | |
| MI5022 | Computer Security | 2 | 2(2-1-0-4) | |
| MI6230 | Graph Theory | 3 | 3(3-1- 0-6) | |
| MI5060 | Artificial Intelligence | 2 | 2(2-1-0-4) | |
| MI5050 | Modeling and Simulation | 2 | 2(2-1-0-4) | |
| MI5142 | Advanced Database | 2 | 2(2-1- 0-4) | |
| Elective knowledge (12 credits) divided into 2 modules | Applied Mathematics Module | |||
| MI6132 | Advanced Numerical Methods | 3 | 3(2-2-0-6) | |
| MI6010 | Applied Algebra | 3 | 3(2-2- 0-6) | |
| MI4150 | Identification Theory | 3 | 3(3-1-0-6) | |
| MI6060 | Financial Math Modeling | 3 | 3(2-2-0-6) | |
| MI6090 | Multi-Objective Optimization | 3 | 3(2-2- 0-6) | |
| MI6040 | Multidimensional Statistics | 3 | 3(2-2-0-6) | |
| MI6050 | Advanced Algorithms and Parallel Computing | 3 | 3(2-2-0-6) | |
| MI6310 | Integral Transform of Convolution Type and Applications | 3 | 3(2-2-0-6) | |
| MI6351 | Seminar I | 3 | 3(1-2-2-6) | |
| MI6352 | Seminar II | 3 | 3(1-2-2-6) | |
| Mathematical foundations for Informatics Module | ||||
| MI6100 | Image Processing | 3 | 3(2-2-0-6 ) | |
| MI6150 | Geographic Information Systems (GIS) | 3 | 3(2-2-0-6) | |
| MI6140 | Data Mining | 3 | 3(2-2-0-6) | |
| MI4010 | Automata Theory and Formal Languages | 3 | 3( 3-1-0-6) | |
| MI6050 | Advanced Algorithms and Parallel Computing | 3 | 3(2-2-0-6) | |
| MI4312 | Mathematical foundations of fuzzy systems | 3 | 3(2-2-0-6) | |
| MI6070 | Machine Learning | 3 | 3(2-2-0-6) | |
| MI6080 | Internet of Things | 3 | 3(2-2-0-6) | |
| MI6351 | Seminar I | 3 | 3(1-2-2-6) | |
| MI6352 | Seminar II | 3 | 3 (1-2-2-6) | |
| Thesis | LV6001 | Graduation Thesis | 15 | 15(0-0-30-50) |
Students A1: Engineering graduates according to the 2009 training model are considered exempt from 15 credits in elective knowledge group; Engineering graduates according to the training model in 2017 are considered exempted from no more than 15 credits in the elective knowledge group.
Supplemental courses Catalog
Students A2, B1, and B2 must take supplementary (preparatory semester) from 9 to 15 credits of courses in the following category. Specific students and additional courses are decided by the School of Applied Mathematics and Informatics.
| Contents | Courses | Module Name | Credits | Volume |
| Additional Major courses (9 – 15 credits) | MI3020 | Functional Calculus | 3 | 3(3−1−0−6) |
| MI3040 | Numerical Analysis | 3 | 3(3−1−0−6 ) | |
| MI3060 | Data Structures and Algorithms | 3 | 3(3−1−0−6) | |
| MI4090 | Programming Techniques | 3 | 3(3−1−0−6) | |
| MI3090 | Database | 3 | 3(3−1−0− 6) | |
| MI3030 | Probability and Statistics | 3 | 3(3−1−0−6) |