THE 2022 CURRICULUM PROGRAM (VIETNAMESE-MEDIUM)

IN COMPUTER SCIENCE

(Issued under Decision No. 62/QĐ-TTU.22 dated April 21, 2022, by the Provost of Tan Tao University)

 

General Information about the Training Program:

  • Program Title in Vietnamese: Khoa học Máy tính
  • Program Title in English: Computer Science
  • Degree Level: Undergraduate
  • Program Code: 7480101
  • Program Duration: 4 years (8 semesters)
  • Type of Education: Full-time
  • Total Credits: 130
  • Degree Awarded: Bachelor
  • Language of Instruction: Vietnamese

PART I. GENERAL INFORMATION ABOUT THE CURRICULUM PROGRAM 2022

1. Introduction to the Curriculum Program 2022

The curriculum program is structured into five blocks including General and Liberal knowledge, Foreign Language - English, Professional Education knowledge, Graduation knowledge and Elective knowledge. The five blocks of the curriculum program ensure the integration of knowledge between the modules in the curriculum program vertically and horizontally to help students practice their critical thinking, analysis, synthesis and effective problem solving skills.

In addition, the curriculum program helps students develop self-study skills, teamwork skills, communication skills, decision-making skills, scientific research, project implementation and promote students' creativity. Students will be exposed to and familiarized with real-life situations and problems through participating in projects in Computer Science majors, which helps students develop the necessary skills before internships and before going to work.

The Computer Science curriculum program also teaches and equips students with the necessary skills for future life and work, emphasizing professional ethics, professionalism, discipline, political qualities, awareness of career development and community responsibility. In addition, students have the opportunity and are facilitated to study English and IT to achieve foreign language and IT proficiency standards before graduation.

2.1. Legal basis

The Computer Science curriculum program is built by the Faculty of Engineering, Tan Tao University based on:

- The policy of the Party and State of Vietnam (Decision 569/QD-TTg, dated May 11, 2022) and the practical needs of today's society on comprehensive educational innovation, bringing liberal education into training, the Faculty of Engineering, Tan Tao University, builds a Bachelor's curriculum program in Computer Science.

- Based on the national qualification framework (Decision 1982/QD-TTg, dated October 18, 2016) and based on the output standards of domestic and foreign universities.

2.2. Information about the curriculum program

- Vietnamese training major name: Computer Science
- English training major name: Computer Science
- Training level: University
- Major code: 7480101
- Training time: 04 years - 08 semesters
- Training type: Full-time
- Number of credits: 130 credits
- Graduation degree: Bachelor of Computer Science
- Language of instruction: Vietnamese

3.1. Mission

With educational philosophy, standards and practices based on the American model of higher education, Tan Tao University encourages independent thinking, perseverance, respect for diversity and language. Tan Tao University will train people who are creative, logical thinkers, lifelong learners, honest, responsible to the country, and have leadership ability.

3.2. Vision

By 2030, Tan Tao University will become a famous university in the ASEAN region and the world, providing high-quality, practical education based on researched knowledge, serving the people of Vietnam, Southeast Asia and the world.

3.3. Core values

- Responsibility (to oneself, family, domestic and international community)

- Cooperation (in all activities)

- Efforts (to work together towards building a sustainable development university)

- Quality (meeting domestic and international standards)

- Creativity (valuable difference)

- Respect (self, community rights)

- Leadership (self, group and organization/business)

3.4. Educational philosophy: Liberal – Lifelong learning

- Open:

The philosophy of liberal education is based on a comprehensive and multidimensional knowledge foundation in many fields of social sciences, humanities and natural sciences before delving into a major. With 25% of liberal arts subjects researched, selected and synthesized from different fields in the entire curriculum, in the spirit of freedom of thought - freedom of thinking to freely choose, helping to train students in the ability to self-study, self-adapt and self-improve in new environments. Therefore, the curriculum program of TTU builds a superior competitive advantage for learners in jobs that require continuous innovation or self-study in a different field of expertise when necessary.

- Lifelong learning:

Graduates of UDTT will be active learners and lifelong learners, aiming to improve their knowledge and professional skills to suit career requirements and improve themselves for lifelong work, specifically:

  • Adapt to continuous learning to find ways to complete different tasks;
  • Proactively build learning goals and life goals;
  • Apply knowledge and skills flexibly, appropriately and meaningfully;
  • Demonstrate a commitment to ongoing and ongoing learning on professional and personal issues;
  • Listen, understand, integrate with your own identity and make continuous efforts for sustainable success in your career.

4.1. General objectives:

The objectives of the curriculum program are built in accordance with the Vision, Mission and Educational Philosophy of Tan Tao University; compatible and consistent with the Vision and Mission of the Faculty of Engineering, aiming to nurture human resources and develop applied scientific research to meet social needs.

Bachelor of Science and Technology training includes:

(i) Logical thinking, good creativity, ability to analyze and solve specific problems from many fields in practice;

(ii) Ability to analyze, design, build and deploy software applications based on knowledge of computers and mathematical guarantees for computers;

(iii) Fluent in English;

(iv) Have professional ethics and skills to work in an international environment;

(v) Have political qualities and high sense of discipline.

4.2. Specific objectives (PO)

Upon completion of this curriculum program, learners will be able to achieve the following objectives:

4.2.1 About knowledge:

- General education knowledge :

PO1. Knowledge of political theory, law, economics, society, culture.

PO2. Good use of foreign languages and computer software in the economic field; ability to read and understand specialized documents, communicate fluently with tourists, partners, and colleagues using English to meet job requirements in an international integration environment.

- Basic knowledge of the industry :

PO3. Equip yourself with knowledge of programming languages, algorithms, data structures, operating systems, computer organization, algorithm construction and mathematical models...

- Professional knowledge :

PO4. Equip yourself with knowledge of narrow specialties such as: artificial intelligence - machine learning, data science and software systems.

PO5. Equip yourself with knowledge about programming thinking and software development.

4.2.2 About skills:

- Hard skills

PO6. Have self-study and self-development skills, have entrepreneurial mindset.

PO7. Ability to form ideas, participate in analysis, design, and implementation of software projects.

PO8. Ability to apply specialized knowledge to solve problems both in practice and in research.

PO9. Possess professional and personal skills, professionalism, management skills, social knowledge as well as different approaches and problem solving appropriate to different aspects of society.

- Soft skills

PO10. Have communication, discussion, negotiation, presentation, teamwork, planning, leadership skills,...

PO11. Achieve international English proficiency TOEFL iBT 61 or IELTS 5.0 or equivalent.

4.2.3 About attitude:

PO12. Have a sense of responsibility and ambition for the trained career.

PO13. Guide and supervise others in performing assigned tasks, taking personal and team responsibility.

4.2.4 Professional ethics:

PO14. Have ethics, professional conscience, sense of discipline, industrial style and good service attitude.

PO15. Have political qualities, awareness of career development, civic responsibility, community responsibility, and good health to meet the requirements of building and defending the Fatherland.

5.1 Knowledge
PLO1 Have basic knowledge of natural sciences, humanities and environment. At the same time understand the importance as well as their applications or impacts in social sectors.
PLO2 Have basic knowledge of economics and management, political theory, understanding of culture, society, law, national security and defense of Vietnam. At the same time, have understanding of culture and society of world civilizations.
PLO3 Proficiency in at least one high-level programming language for implementing computer science solutions for application areas.
PLO4 Have basic knowledge of algorithms, data structures, programming languages, operating systems as well as computer organization and architecture.
PLO5 Have knowledge of algorithm construction, complexity assessment and optimization for specific cases.
PLO6 Have an understanding of mathematical models applied in computer science, their advantages and disadvantages for each case. Have the ability to orient and think entrepreneurially.
PLO7 Depending on the specialized orientation, the knowledge of each direction includes:
PLO7a - Data Science Orientation: Collect/transform/store/extract data, build and evaluate data processing models, process data on distributed and cloud systems, visualize data. Have knowledge of machine learning algorithms, their advantages and disadvantages.
PLO7b - Artificial Intelligence/Machine Learning Orientation: Regression algorithms, supervised and unsupervised learning, deep learning (multi-layer learning), machine learning models based on statistical probability, language processing, computer vision.
PLO7c - Software system orientation: Have knowledge of databases, distributed systems, computer networks and knowledge related to software application development such as: analysis, architectural design, software deployment and maintenance.
5.2 Skills
5.2.1 Professional skills
PLO8 Identify, select and recommend appropriate solutions and technologies to build software applications that operate effectively in different environments (e.g. mobile, IoT - Internet of Things, distributed).
PLO9 Search, evaluate and effectively use professional documents including: books, magazines, open source programs.
5.2.2 Soft skills
PLO10 Communicate effectively through writing, presenting, discussing, negotiating, and mastering situations.
PLO11 Achieve international English proficiency with TOEFL iBT 61 or IELTS 5.0 or equivalent.
PLO12 Have teamwork skills, planning, ability to assign, monitor and evaluate the level of completion of the team's work. Effectively use teamwork tools.
5.3 Level of autonomy and responsibility
PLO13 Recognize professional responsibilities and make sound judgments about the application of computer science to social problems based on law and ethics.
PLO14 Lifelong self-learning to serve work to create lifelong working capacity; have a sense of responsibility for oneself, family, and society; cooperate and be autonomous in work; take responsibility for one's own work results; comply with labor discipline.
PLO15 Honest, upright, confident, flexible, enthusiastic; respect the law, be aware of social issues, actively participate in socio-political activities, fully exercise the rights and obligations of citizens.

 

Matrix of objectives and output standards of the curriculum program

 

TRAINING OBJECTIVES TRAINING OUTPUT STANDARDS
Knowledge Skill Capacity

autonomy and responsibility

PLO

1

PLO

2

PLO

3

PLO

4

PLO

5

PLO

6

PLO

7

PLO

8

PLO

9

PLO

10

PLO

11

PLO

12

PLO

13

PLO

14

PLO

15

PO1   X                          
PO2 X                            
PO3     X X X X                  
PO4             X                
PO5     X       X                
PO6               X X         X  
PO7     X         X              
PO8           X   X              
PO9                         X    
PO10                   X   X      
PO11                     X        
PO12                         X   X
PO13                   X   X      
PO14                           X  
PO15                           X X

Computer science graduates can work in a variety of positions, typically the following:

  • Work in technology companies: programmer, AI engineer, team leader or project manager;
  • Data engineer/data analyst/data scientist in companies/organizations;
  • Researcher/consultant on innovation, digital economic transformation and artificial intelligence application in the research and development department of companies/organizations;
  • Research/teaching in universities/institutes in Vietnam and internationally;
  • Continue to study for master's/PhD.
  • Start-up.

Level of achievement with job positions:

(Level of achievement: 1: Ability to know; 2: Ability to understand and apply; 3: Ability to analyze and evaluate; 4: Ability to create)

STT JOB POSITION NAME Level of achievement
1 2 3 4
1 Programmer, software engineer       X
2 Machine Learning/Artificial Intelligence Engineer       X
3 Data Engineer       X
4 Teaching assistant at universities and colleges     X  
5 Researcher     X  
6 Data Analyst     X  
7 Technology solution consultant   X    

Ability to self-study and research in the working environment to improve professional knowledge and skills in organizing professional activities, meeting the requirements of the country's industrialization and modernization process.

Have the capacity to participate in higher education to develop knowledge and professional skills to meet the needs of oneself and society.

People with a bachelor's degree in Computer Science can work at technology companies/organizations, universities/academies in Vietnam and internationally.

The program structure ensures a reasonable and balanced arrangement in each semester of the school year and each block of knowledge. The program arranges modules from basic to advanced to ensure continuous knowledge, increasing levels and enough time to accumulate knowledge, practice skills, ethics and attitudes necessary for work. At the same time, the program is also designed to ensure depth for each specialized field.

The program content includes the following knowledge blocks: General and Liberal knowledge, knowledge of foreign languages - English, professional education knowledge (compulsory basic knowledge of the major, compulsory knowledge of the major and compulsory knowledge for each major), graduation knowledge and elective knowledge. In addition, students are also taught soft skills to practice skills, train thinking, style and confidence when entering the working environment. The total knowledge volume of the course must accumulate 130 credits and is distributed as follows:

TT Study load Number of credits
TC LT TH
1 General and liberal knowledge 36    
- Liberal knowledge 21 20 1
- General knowledge

(not including Physical Education and National Defense and Security Education content)

15 14 1
2 Knowledge of Foreign Languages - English

(not including intensive English course)

12 12 0
3 Professional educational knowledge, including: 51    
- Required basic industry knowledge 18 15 3
- Required industry knowledge 18 15 3
- Required knowledge for each major 15 10/

11

5/

4

4 Graduate knowledge

Satisfy 2 conditions:

  1. Internship or thesis
  2. Project, essay or alternative course (elective course)
12 12 0
5 Elective knowledge

Students are required to select at least 9 credits outside of the Faculty of Engineering.

minimum 19 (depending on student choice)
Total credits 130 (depending on student choice)

 

 

10.1. Structure and content of the training program

 

TT Course code Course name Number of credits
TC ST LT TH
GENERAL AND LIBERAL KNOWLEDGE BLOCK 36

11*

570

-

34

-

2

-

Liberal Knowledge

Students are required to take at least 01 course per group.

21 330 20 1
Group I: Human civilization
1 HIS101V World Civilization History 3 45 3 0
2 HIS102V Modern times 3 45 3 0
Group II: Culture, literature and arts
1 ENGL108V Introduction to Cultural Studies 3 45 3 0
2 ART101 Contemporary Art 3 45 3 0
3 CUL101 Vietnamese and other world classic cultures 3 45 3 0
4 HUM102V Culture and Literature 3 45 3 0
Group III: Thinking and communication
1 HUM101V Writing and Ideas 3 45 3 0
2 MGT102V Leadership and Communication 3 45 3 0
3 VNL101 Language and Vietnamese 3 45 3 0
Group IV: Humans and the Earth
1 ENV101 Human and Environmental Interactions 3 45 3 0
2 ENV102 Climate Change 3 45 3 0
Group V: Natural sciences and technology 
1 a MATH101V Calculus 1 3 45 3 0
2 a DSP101 Introduction to data science with Python 3 60 2 1
3 EGD101 Engineering Design 3 60 2 1
Group VI: Economics and Management 
1 ENTR01 Entrepreneurship 3 45 3 0
2 PRFN01 Personal Finance 3 45 3 0
General Knowledge 15

11*

240

-

14

-

1

-

1 MACL108 Marxist-Leninist philosophy 3 45 3 0
2 MACL109 Marxist-Leninist Political Economy 2 30 2 0
3 MACL104 Ho Chi Minh Thought 2 30 2 0
4 MACL110 Science Socialism 2 30 2 0
5 MACL111 History of the Communist Party of Vietnam 2 30 2 0
6 LAW102 Fundamentals of Law 2 30 2 0
7 INF102 Introduction to Informatics 2 45 1 1
8 MACL1051 Physical Education 1 1* 30 0 1
9 MACL1052 Physical Education 2 1* 30 0 1
10 MACL1053 Physical Education 3 1* 30 0 1
11 MACL106 National Defense and Security

Education

8*
KNOWLEDGE OF FOREIGN LANGUAGES - ENGLISH 12

8*

180

-

12

-

0

-

1 ESL101 English 1 3 45 3 0
2 ESLi101 Intensive English 1 2* 30 2 0
3 ESL102 English 2 3 45 3 0
4 ESLi102 Intensive English 2 2* 30 2 0
5 ESL103 English 3 3 45 3 0
6 ESLi103 Intensive English 3 2* 30 2 0
7 ESL104 English 4 3 45 3 0
8 ESLi104 Intensive English 4 2* 30 2 0
PROFESSIONAL EDUCATION KNOWLEDGE
Required industry knowledge 18 315 15 3
1 MATH201V Calculus 2 3 45 3 0
2 MATH110V Linear Algebra 3 45 3 0
3 PHYS101V Introductory Mechanics 3 60 2 1
4 PHYS110V Introductory Electricity and

Magnetism

3 60 2 1
5 CS111V Introduction to Computer Science

and Programming in Python

3 60 2 1
6 STA206V Probabilities and Statistics 3 45 3 0
Required industry knowledge 18 315 15 3
1 CS201V Data Structure and Algorithms 3 60 2 1
2 CS202V Discrete Mathematics for CS 3 45 3 0
3 CS203V Computer Organization 3 45 3 0
4 CS204V Design & Analysis of Algorithms 3 60 2 1
5 CS205V Introduction to Operating Systems 3 45 3 0
6 CS206V Object Oriented Programming 3 60 2 1
Required knowledge for each major

(Students need to choose and complete 1 of 3 majors)

15 300 /

285

10/

11

5 /

4

1. Data Science
1 CS311V Introduction to Database 3 60 2 1
2 CS331V Introduction to Data Mining 3 60 2 1
3 CS441V Data Visualization 3 60 2 1
4 CS332V Introduction to Machine Learning 3 60 2 1
5 CS411V Big Data 3 60 2 1
2. Machine Learning/Artificial Intelligence
1 CS330V Introduction to AI 3 60 2 1
2 CS332V Introduction to Machine Learning 3 60 2 1
3 CS431V Advanced machine learning 3 60 2 1
4 CS434V Deep Learning 3 60 2 1
5 STA301V Bayesian statistics 3 45 3 0
3. Software system
1 CS301V Software Design and Implementation 3 60 2 1
2 CS311V Introduction to Database 3 60 2 1
3 CS401V Distributed Systems 3 60 2 1
4 CS440V Computer Network 3 60 2 1
5 CS332V Introduction to Machine Learning 3 60 2 1
GRADUATE KNOWLEDGE 12 540 12 0
1 CS470V Graduation project 8 360 8 0
2 CS480V Graduation thesis 8 360 8 0
3 CS471V Graduation essay 4 180 4 0
4 CS481V Internship 1 4 180 4 0
5 CS482V Internship 2 6 270 6 0
ELECTIVE KNOWLEDGE

Students are required to select at least 9 credits outside of the Faculty of Engineering.

19
1. Data Science
1 STA301V Bayesian statistics 3 45 3 0
2 STA302V Probability & Stochastic Processes 3 45 3 0
3 CS412V Information Retrieval and Web Search 3 60 2 1
4 CS413V Data Preprocessing/cleansing 3 75 1 2
5 CS414V Data science project & deployment 3 75 1 2
6 CS431V Advanced machine learning 3 60 2 1
7 CS364V Cryptography and Secure Applications 3 60 2 1
8 CS440V Computer Network 3 60 2 1
9 CS450V Data science topics 3 90 0 3
10 MATH202V Calculus 3 3 45 3 0
2. Machine Learning/Artificial Intelligence
1 CS333V Introduction to Computer Vision 3 60 2 1
2 CS411V Big Data 3 60 2 1
3 CS435V Practical Deep learning in Natural Language Processing 3 75 1 2
4 CS436V Practical Deep learning in Computer Vision 3 75 1 2
5 CS437V Pattern Recognition 3 60 2 1
6 CS334V Introduction to Natural Language Processing 3 60 2 1
7 CS447V Reinforcement Learning 3 60 2 1
8 MATH202V Calculus 3 3 45 3 0
3. Software system
1 CS333V Introduction to Computer Vision 3 60 2 1
2 CS334V Introduction to Natural Language Processing 3 60 2 1
3 CS302V Web Application Development 3 75 1 2
4 CS303V Mobile Application Development 3 75 1 2
5 CS304V IoT Application Development 3 75 1 2
6 CS305V Cloud computing 3 60 2 1
7 CS411V Big Data 3 60 2 1
8 CS431V Advanced machine learning 3 60 2 1
9 CS434V Deep Learning 3 60 2 1
10 CS408V Software Project 3 75 1 2
TOTAL CREDITS OF THE TRAINING PROGRAM 130
Total required credits 111
Minimum total elective credits 19

 

10.2. Training process

- Teaching plan (tentative)

Built at the beginning of each academic year for student registration

 

TT Course code Course name Number of credits
TC ST LT TH
Semester 1
1 MACL108 Marxist-Leninist philosophy 3 45 3 0
2 LAW102 Fundamentals of Law 2 30 2 0
3 Human Civilization Group 3 45 3 0
4 ESL101 English 1 3 45 3 0
5 ESLi101 Intensive English 1 2* 30 2 0
6 MACL1051 Physical Education 1 1* 30 0 1
7 INF102 Introduction to Informatics 2 45 1 1
8 a MATH101V Calculus 1 3 45 3 0
Total: 16

3*

255

-

15

-

1

-

Semester 2
1 MACL109 Marxist-Leninist Political Economy 2 30 2 0
2 MACL110 Science Socialism 2 30 2 0
4 ESL102 English 2 3 45 3 0
5 ESLi102 Intensive English 2 2* 30 2 0
6 MACL1052 Physical Education 2 1* 30 0 1
7 MATH110V Linear Algebra 3 45 3 0
8 CS111V Introduction to Computer Science

and Programming in Python

3 60 2 1
9 PHYS101V Introductory Mechanics 3 60 2 1
Total: 19

3*

315

-

17

-

2

-

Summer semester
1 MACL106 National Defense and Security

Education

8*
Total: 8* - - -
Semester 3
1 MACL104 Ho Chi Minh Thought 2 30 2 0
3 ESL103 English 3 3 45 3 0
4 ESLi103 Intensive English 3 2* 30 2 0
5 MACL1053 Physical Education 3 1* 30 0 1
6 MATH201V Calculus 2 3 45 3 0
7 CS201V Data Structure and Algorithms 3 60 2 1
8 CS202V Discrete Mathematics for CS 3 45 3 0
9 PHYS110V Introductory Electricity and

Magnetism

3 60 2 1
Total: 20

3*

330

-

18

-

2

-

Semester 4
1 ESL104 English 4 3 45 3 0
2 ESLi104 Intensive English 4 2* 30 2 0
3 MACL111 History of the Communist Party of Vietnam 2 30 2 0
5 CS203V Computer Organization 3 45 3 0
6 CS205V Introduction to Operating Systems 3 45 3 0
7 CS206V Object Oriented Programming 3 60 2 1
Total: 17

2*

270

-

16

-

1

-

Semester 5
1 a DSP101 Introduction to data science with Python 3 60 2 1
2 CS204V Design & Analysis of Algorithms 3 60 2 1
3 STA206V Probabilities and Statistics 3 45 3 0
4 Intensive Course 1 3 45 3 0
5 Intensive Course 2 3 45 3 0
6 Optional 1 3 45 3 0
Total: 18 300 16 2
Semester 6
1 Economics and Management Group 3 45 3 0
2 Intensive Course 3 3 45 3 0
3 Intensive Course 4 3 45 3 0
4 Optional 2 3 45 3 0
5 Optional 3 3 45 3 0
Total: 15 225 15 0
Semester 7
1 Intensive Course 5 3 45 3 0
2 Optional 4 3 45 3 0
3 Optional 5 3 45 3 0
4 Optional 6 3 45 3 0
Total: 12 180 12 0
Semester 8
1 CS482V/

CS480V

Internship 2 or Graduation Thesis

Internship 2 or Graduation Thesis

6

/8

270

/360

6

/8

0
2 Optional 7 3 45 3 0
3 Optional 8 3

/1

45

/15

3

/1

0
Total: 12 360 12 0
TOTAL CREDITS OF THE TRAINING PROGRAM 130
Total required credits 111
Minimum total elective credits 19


10.3. Matrix of output standards of training programs and subjects

 

SST MH CODE Subject name OUTPUT STANDARDS
TC number PL

O1

PL

O2

PL

O3

PL

O4

PL

O5

PL

O6

PLO7 PL

O8

PL

O9

PL

O10

PL

O11

PL

O12

PL

O13

PL

O14

PL

O15

A B C
Tan Tao University's Liberal Arts Group

Students are required to study at least 01 subject from each group.

21
Group I: Human civilization
1 HIS101V World Civilization History 3 X X X X X
2 HIS102V Modern times 3 X X X X X
Group II: Culture, literature and arts
1 ENGL108V Introduction to Cultural Studies 3 X X X X
2 ART101 Contemporary Art 3 X X X X
3 CUL101 Vietnamese and other world classic cultures 3 X X X X
4 HUM102V Culture and Literature 3 X X X X X
Group III: Thinking and communication
1 HUM101V Writing and Ideas 3 X X X X
2 VNL101 Language and Vietnamese 3 X X X X
3 MGT102V n

Leadership and Communication

3 X X X X
Group IV: Humans and the Earth
1 ENV101 Environmental Interactions 3 X X X
2 ENV102 Climate Change 3 X X X
Group V: Natural sciences and technology
1 MATH101V Calculus 1 3 X X X X X X
2 DSP101 Introduction to data science with Python 3 X X X X X X X X X
3 EGD101 Engineering Design 3 X X X X
Group VI: Economics and Management
1 ENTR01 p

Entrepreneurship

3 X X X
2 PRFN01 t

Personal Finance

3 X X X
Knowledge groups according to regulations of the Ministry of Education and Training 15

11*

1 MACL108 Marxist-Leninist philosophy 3 X X X X X
2 MACL109 Marxist-Leninis

Political Economy

2 X X X X X
3 MACL110 Science Socialism 2 X X X X X
4 MACL111 History of the Communist Party of Vietnam 2 X X X X X
5 MACL104 Ho Chi Minh Thought 2 X X X
6 LAW102 Fundamentals of Law 2 X X X
7 INF101 e

Introduction to Informatics

2 X X X
8 MACL1051 Physical Education 1 1* X X X
9 MACL1052 Physical Education 2 1* X X X
10 MACL1053 Physical Education 3 1* X X X X
11 MACL106 n

National Defense and Security Education

8* X X X X
Foreign Language - English Knowledge Group 12

8*

1 ESL101 English 1 3 X X X X X
2 ESLi101 Intensive English 1 2* X X X X X
3 ESL102 English 2 3
4 ESLi102 Intensive English 2 2* X X X X X
5 ESL103 3

English 3

3 X X X X X
6 ESLi103 Intensive English 3 2* X X X X X
7 ESL104 English 4 3 X X X X X
8 ESLi104 Intensive English 4 2* X X X X X
Professional education knowledge group
Required industry knowledge 18
1 MATH201V 2

Calculus 2

3 X X X X X X X X
2 MATH110V Linear Algebra 3 X X X X X X
3 PHYS101V Introductory Mechanics 3 X X X X
4 PHYS110V Introduction to Electricity and Magnetism 3 X X X X
5 CS111V g

Introduction to Computer Science and Programming in Python

3 X X X X X X X
6 STA206V Probabilities and Statistics 3 X X X X X X X X
Required industry knowledge 18
1 CS201V s

Data Structure and Algorithms

3 X X X X
2 CS202V Discrete Mathematics for CS 3 X X X X X X X
3 CS203V s

Computer Organization

3 X X X X X
4 CS204V s

Design & Analysis of Algorithms

3 X X X X X X
5 CS205V Introduction to Operating Systems 3 X X X
6 CS206V Object Oriented Programming 3 X X X X X X
Required knowledge for each major

(Students need to choose and complete 1 of 3 majors)

(i) Data Science

(ii) Machine Learning/Artificial Intelligence

(iii) Software system

15
1 CS311V (i), (iii) e

Introduction to Database

3 X X X X X X X X X X
2 CS331V (i) Introduction to Data Mining 3 X X X X X X X
3 CS332V (i), (ii), (iii) Introduction to Machine Learning 3 X X X X X X X X
4 CS411V (i) Big Data 3 X X X X X X X X
5 CS441V (i) Data Visualization 3 X X X X X X X X
6 CS330V (ii) e

Introduction to AI

3 X X X X X X X
7 CS431V (ii) Advanced machine learning 3 X X X X X X X X
8 CS434V (ii) Deep Learning 3 X X X X X X X
9 STA301V (ii) Bayesian statistics 3 X X X X X X X
10 CS301V (iii) Software Design and Implementation 3 X X X X X
11 CS401V (iii) s

Distributed Systems

3 X X X X X X
12 CS440V (iii) Computer Network 3 X X X X X X
Graduate knowledge group
1 CS470V t

Graduation project

8 X X X X X X X X X X
2 CS480V Graduation thesis 8 X X X X X X X X X X X X X X
3 CS471V Graduation essay 4 X X X X X X X X X X X X X
4 CS481V Internship 1 4 X X X X X X X X X X X X X X X
5 CS482V Internship 2 6 X X X X X X X X X X X X X X X
1 STA301V s

Bayesian statistics

3 X X X X X X X
2 STA302V Probability & Stochastic Processes 3 X X X X X X
3 CS412V Information Retrieval and Web Search 3 X X X X X X X
4 CS413V Data Preprocessing/cleansing 3 X X X X
5 CS414V Data science project & deployment 3 X X X X
6 CS431V Advanced machine learning 3 X X X X X X X X
7 CS364V Cryptography and Secure Applications 3 X X X X X X X X X
8 CS440V k

Computer Network

3 X X X X X X
9 CS450V Data science topics 3 X X X X X X
11 MATH202V Calculus 3 3 X X
12 CS333V Introduction to Computer Vision 3 X X X X X X X X
13 CS411V Big Data 3 X X X X X X X X
14 CS435V Practical Deep learning in Natural Language Processing 3 X X X X X X
15 CS436V Practical Deep learning in Computer Vision 3 X X X X X
16 CS437V Pattern Recognition 3 X X X X X
17 CS334V g

Introduction to Natural Language Processing

3 X X X X X X X X
18 CS447V Reinforcement Learning 3 X X X X X
19 CS302V t

Web Application Development

3 X X X X X X X
20 CS303V t

Mobile Application Development

3 X X X X X X X X X
21 CS304V IoT Application Development 3 X X X X X X
22 CS305V Cloud computing 3 X X X X X X
23 CS434V g

Deep Learning

3 X X X X X X X
24 CS408V Software Project 3 X X X X X X X

 

X: Prerequisite for output standard

(*): not included in cumulative GPA.

( a ): compulsory subjects for students of the Faculty of Engineering.

( i ) Data Science

( ii ) Machine Learning/Artificial Intelligence

( iii ) Software system

11.1. Admission information

All subjects according to the university admission regulations of the Ministry of Education and Training.

11.2. Training process

Implemented according to the University Training Regulations of Tan Tao University (Issued under Decision No. 31/QD-DHTT.21, Long An, dated June 30, 2021 of the President of Tan Tao University).

The training regulations used are credit-based training regulations, creating conditions for students to actively and proactively adapt to the training process to help achieve the best results in learning and training.

The curriculum program is designed with 8 semesters corresponding to 4 academic years, including 130 credits. An academic year is divided into 2 main semesters. In addition to the two main semesters, the Principal considers and decides to organize an additional summer semester so that students have the opportunity to retake, improve and advance their studies. Each main semester has at least 15 weeks of actual study and 2 weeks of exams; Each summer semester has at least 5 weeks of actual study and 1 week of exams.

11.3. Graduation conditions

Implemented according to the University Training Regulations of Tan Tao University (Issued under Decision No. 31/QD-DHTT.21, Long An, dated June 30, 2021 of the President of Tan Tao University).

  1. Accumulate enough credits (minimum 130 credits) and complete other required content as required by the curriculum program;
  2. Cumulative GPA of the entire course must be at least 2.00;
  3. Meet the foreign language output standards as prescribed by the School: TOEFL iBT 61 or IELTS 5.0 or equivalent;
  4. Complete the Physical Education (PE) and National Defense and Security Education (NDE) modules;
  5. Have a Soft Skills certificate provided by the school;
  6. Meet the required number of hours of participation in community service activities as prescribed;
  7. At the time of graduation, not being prosecuted for criminal liability or not being subject to disciplinary action at the level of suspension from school;
  8. Fulfill obligations to the school;
  9. Register for graduation according to regulations at the Training Management Department.

The school has compared the objectives, training objectives and curriculum program framework of the Computer Science major of Tan Tao University with the curriculum programs of prestigious domestic and foreign universities such as: Thang Long University, etc.

13.1. Teaching and learning strategies and methods (TLM)

13.1.1. Direct teaching:

Direct teaching is a teaching method in which information is delivered to learners directly, the lecturer presents and the learners listen. This method is often applied in traditional classrooms and is effective when wanting to convey basic information to learners, explain a new skill.

  • TLM1 . Lecture: The lecturer presents the lesson content and explains the content in the lecture. The lecturer is the one who presents and lectures. Students are responsible for listening to the lecture and taking notes to receive the knowledge that the lecturer imparts.
  • TLM2 . Specific explanation: The lecturer guides and explains in detail the specific content related to the lesson, helping students achieve the teaching objectives of knowledge and skills.
  • TLM3 . Presentation: Students participate in courses where the speakers come from external organizations such as employers, people with extensive experience in the training field... through the exchange of experiences and knowledge of the speakers to help learners form general or specific knowledge about the industry and training major.
  • TLM4 . Open-ended questions: The lecturer uses open-ended questions or problems, and guides students step by step in answering the questions. Students can participate in group discussions to solve exercises and problems together.
  • TLM5 . Practical exercises: After observing the lecturer demonstrate, students will complete the exercises themselves or work in groups to complete them, thereby forming and practicing the skills that students will have to perform in their future career fields.
  • TLM6 . Student presentation: Lecturers assign topics to individuals or groups of students to collect documents, research and present to the class. Help students practice reading comprehension skills, synthesize information, present in front of a crowd,...

13.1.2. Research-based teaching:

Inquiry-based learning encourages a high level of critical thinking. Learners identify research questions, find appropriate methods to solve problems, or report conclusions based on the information gathered.

  • TLM7 . Independent Study: This method develops students' ability to plan, explore, organize, and communicate a topic independently and in detail, under the guidance of a teacher. It also enhances motivation and active participation in learning because students are allowed to choose the material they want to present.
  • TLM8 . Project: Students research a topic related to the subject and write a report.
  • TLM9. Teaching assistants and academic support: Students are allowed to assist lecturers in classes.

13.1.3. Teaching based on experiential activities:

This strategy helps students experience the real environment and future jobs. This strategy not only helps students develop knowledge and skills but also creates career opportunities for students after graduation.

  • TLM10 . Internship at enterprises: Through internships at companies, students can understand the actual working environment of their field of study after graduation, learn about the technologies being applied in their field of study, and develop professional skills and work culture in the company.

13.1.4. Self-study:

Self-study is a method that helps students acquire knowledge and develop skills to be self-directed, proactive and independent in their learning. Students have the opportunity to choose a topic to study, explore and research deeply on an issue. From there, students develop time management skills and self-monitor their learning. The self-study method mainly applies homework.

  • TLM11 . Homework: Students are assigned homework tasks with content and requirements set by the lecturer. Through completing these assigned tasks, students learn how to study independently, as well as achieve the required knowledge and skills.

13.2. Instructor preparation

Lecturers teaching the Computer Science program need to: clearly understand the forms of classroom organization of each subject they teach (theoretical or practical subjects, compulsory or elective subjects, direct learning or online learning); prepare lectures (including practical application examples - if any), exercises (theoretical and practical), prepare open-ended problems/questions, clearly understand the subject assessment methods, students' learning needs (according to the school years), clearly understand the policies and regulations in learning, lecturer regulations, assessment regulations.

 

14.1. Testing and evaluation methods

Assessment methods used in the curriculum program are divided into two main types: process assessment and final assessment. The forms and contents of assessment are specifically regulated in the current training regulations of the School and specifically regulated in the teaching outline of each course. A course with 3 or more credits must have at least 2 scoring components: process assessment and final assessment.

14.1.1. Progress Evaluation

The purpose of process assessment is to provide timely feedback to teachers and learners on progress and areas for improvement that arise during the teaching and learning process.

Specific assessment methods with the type of progress assessment applied by the School may include attendance scores, assignment scores, presentations, mid-term test scores... to assess the progress scores of the courses.

  • AM1 . Assessment of attendance: in addition to self-study time, regular and full participation in lectures, practice rooms, etc. in the course also reflects the learner's learning attitude; full participation in prescribed study hours helps learners access knowledge, practice skills systematically, continuously and form good and correct attitudes, comply with regulations and discipline at school and the employer after the learner graduates. Assessment of attendance is carried out according to rubrics depending on the nature of the prescribed course (theory, practice diary, thesis, etc.).
  • AM2 . Individual/group assignment assessment: learners are required to perform some content related to the lesson in class. These assignments can be performed by an individual or a group of learners and are assessed according to specific criteria (assignment rubric). The content of the individual/group assignment can be theoretical or practical.
  • AM3 . Presentation assessment: in some courses, learners are required to work in groups to solve problems, situations or content related to the lesson and present the group's results to other groups. The activity not only helps learners gain specialized knowledge but also develops skills such as communication, negotiation and teamwork. To assess the level of achievement of these skills, learners can use specific assessment criteria (presentation rubric).
  • AM4 . Assessment through mid-term tests: the assessment methods in section 1.2 - Final assessment (below) can be used to assess students' mid-term grades.

14.1.2. Final assessment (end of term)

The purpose of this assessment is to draw conclusions and classify the level of achievement of goals and output quality, and the progress of learners at a specified time in the teaching and learning process, including end-of-program assessment and end-of-semester assessment.

The assessment methods used by the school for this type of assessment include: written/essay tests, multiple-choice tests, combined multiple-choice and essay tests, reports, presentations, practice, etc. (These methods can be used for mid-semester assessment for courses worth 3 credits or more).

  • AM5 . Written/essay test: in this assessment method, learners are asked to answer a number of questions, situational exercises or personal opinions on issues related to the knowledge output requirements of the course and are assessed based on pre-designed answers. The assessment scale used in this method is a 10-point scale. The number of questions in the assessment is designed depending on the knowledge content requirements of the course.
  • AM6 . Multiple choice and essay combined tests: In multiple choice testing, learners are asked to answer related questions based on pre-designed answers. The difference is that in this assessment method, learners answer the required questions based on suggested answers. In addition, there is also a multiple choice test combined with a writing/essay method.
  • AM7 . Report writing: learners are assessed through report products, including: presentation content in the report, presentation method, illustrations, charts,... in the report. Specific assessment criteria for this method are according to the report writing rubrics of each course.
  • AM8 . Presentation: This method is exactly the same as the presentation assessment method in the formative assessment type. Assessment is carried out periodically: mid-term, final or end of course.
  • AM9 . Practice: in which learners are required to practice writing a program on a computer. To assess the level of achievement, the instructor can use specific assessment criteria in the checklist - scale or specific criteria in the rubric.

14.1.3. Evaluation of internship/graduation thesis/graduation project

The purpose of this assessment is to assess the level of achievement of objectives, output quality, knowledge and skills of students before graduation.

The assessment methods used by the school for this type of assessment include: internship report/graduation thesis/essay/graduation project.

  • AM10 . Internship report/graduation thesis/essay/graduation project: this is a very valuable method of assessing capacity because it can simultaneously assess knowledge, attitudes and many skills such as creative thinking - judgment - reasoning; information searching - selection - use skills; operational skills, organizational and management skills, communication skills, cooperation skills in groups/teams...; data processing and report writing skills; in addition, learners also practice the skill of defending before the council when doing the graduation thesis/graduation project. For the graduation thesis/graduation project, learners will be evaluated by the instructor and the graduation thesis/graduation project evaluation council, the evaluation council using evaluation forms appropriate to the training industry.

14.2. Form, weights and evaluation criteria

Implemented according to the University Training Regulations of Tan Tao University (Issued under Decision No. 31/QD-DHTT.21, Long An, dated June 30, 2021 of the President of Tan Tao University).

The form, weights and specific assessment criteria are shown in detail on the detailed course outline.

14.3. Grading scale

Implemented according to the University Training Regulations of Tan Tao University (Issued under Decision No. 31/QD-DHTT.21, Long An, dated June 30, 2021 of the President of Tan Tao University).

  • The curriculum program is reviewed periodically every 2 years to adjust to meet the needs of learners and stakeholders. More forms of support are added to students in their task of training ethics, manners and necessary skills.
  • Every year, the Faculties develop a plan to observe lecturers, especially young lecturers, to exchange and share knowledge and teaching methods to improve lecturers' capacity.
  • Regularly solicit student feedback on the qualities, talents, ethics and conduct of lecturers.
  • Regularly consult with stakeholders on graduate employment needs.

Selecting graduation courses must satisfy 2 conditions (accumulate at least 12 credits):

Condition 1: Internship or graduation thesis

Condition 2: Project, essay or alternative course (elective course)

16.1. Notes on conditions for doing graduation thesis

  • Accumulated enough credits of the program by the time of review.
  • GPA at the time of registration must be greater than or equal to 3.0.
  • Final year of study without criminal prosecution.
  • There are qualified lecturers (including those from outside the School) to guide and must be approved by the Faculty.
  • The number of students allowed to do their graduation thesis must not exceed 50% of the total number of students in the curriculum program in that review period. For specific specialized majors, based on the proposal of the Faculty, the Board of Directors will consider in specific cases.

Depending on the capacity of the faculty members registering for the topic and the facilities, the Faculty proposes the number of students to graduate for the Principal's approval. The school encourages students to do their thesis and find their own graduate supervisors when the number of topics registered for supervision by faculty members in the Faculty is not enough, but must be approved by the Faculty.

*Graduate thesis topic:

  • Students will be able to choose one of the topics in Information Technology that is suitable for their orientation and career aspirations after graduation to do their graduation thesis. The list of topics is decided by the Principal depending on the conditions of equipment and available instructors.
  • The thesis score is calculated into the cumulative GPA of the entire course.
  • Grading graduation thesis according to regulations of the Ministry of Education and Training and regulations of the School according to the evaluation form.

17.1. Teaching staff

  • Lecturers teaching Computer Science must meet the teaching standards prescribed by the Ministry of Education and Training.
  • Theory and practice teaching in laboratories and practice rooms at school are carried out by full-time lecturers.

17.2. Facilities

  • Training facilities must ensure facilities according to current regulations and guidelines of the Ministry of Education and Training such as lecture halls, libraries, laboratories, practice rooms, modern equipment for teaching, and computer rooms with internet connection.
  • Each course has experimental and practical content that must be studied in a laboratory or practice room with adequate area and equipment according to regulations.

STT Course name Course Objectives TC number Student evaluation method
1 World Civilization History Prerequisite: None

The course provides students with basic and systematic knowledge about the history of formation, development process and some outstanding achievements in culture, science - technology... of prominent civilizations in the ancient and medieval period in the East such as Egypt, India, China and in the West such as Greece, Rome, Western European countries... helping students have basic knowledge about the history of development and progress of humanity.

03 Regulations in the detailed course outline
2 Modern times Prerequisite: None

The course covers world history from the discovery of the New World & the American Revolution to the end of the 20th century. Significant changes throughout history have resulted from trade, military and democracy. These events include the industrial revolution, European imperialism, trade and globalization, world wars, the rise of superpowers…

03 Regulations in the detailed course outline
3 Introduction to Cultural Studies Prerequisite: None

The course provides students with basic theories of cultural studies, including: a system of basic concepts of culture, ways to identify culture, some specific cultural issues (yin-yang philosophy, symbolic culture, island culture, water culture, etc.), some general features of Vietnamese and world culture, applied culture, etc.

03 Regulations in the detailed course outline
4 Contemporary Art Prerequisite: None

The course provides students with a fundamental understanding of art from its origins to the present day. Contemporary art in a world of global influence, cultural diversity and technology. The dynamic combination of materials, methods, concepts and themes continues to challenge the boundaries that were well underway in the 20th century. Contemporary art is part of a cultural dialogue that engages with larger contextual frameworks such as personal and cultural identity, family, community and nationality.

03 Regulations in the detailed course outline
5 Vietnamese and other world classic cultures Prerequisite: None

The course provides students with basic knowledge about Vietnamese culture (identity, value system, culture of some regions, culinary culture...) and some typical world cultures (Korea, Japan, etc.). Vietnamese culture (including Vietnamese, Japanese, Chinese, etc.) helps learners gain a basic understanding of Vietnamese culture and some typical world cultures.

03 Regulations in the detailed course outline
6 Culture and Literature Prerequisite: None

The course provides students with basic knowledge about culture and literature, including: general theory of culture and literature; the role of culture and literature; basic knowledge about Vietnamese culture and some typical world cultures; some classic literary works.

n of Vietnam and the world.

03 Regulations in the detailed course outline
7 Writing and Ideas Prerequisite: None

This course is designed to help students develop their thinking skills, develop their ability to reason, evaluate and respond effectively to information presented. The course is not limited to written and oral communication but focuses on the structure of arguments and how to avoid logical pitfalls. Information will be analyzed from news, public records, films, slides, transcripts and any other media sources and then put into a well-organized essay.

03 Regulations in the detailed course outline
8 Leadership and Communication Prerequisite: None

The course provides students with basic and systematic knowledge of historical, theoretical and practical perspectives on leadership (traits, skills, styles, situations, contingencies, pathways, transformational leadership and team leadership) and communication (communication elements, communication positions of leaders; using social positions and communication positions to communicate effectively in leadership roles). The course will also guide students to apply these theories to practical problems.

03 Regulations in the detailed course outline
9 Language and Vietnamese Prerequisite: None

The course provides students with basic knowledge of language in general (origin, nature, function, etc.) and Vietnamese with its basic characteristics: phonetics, vocabulary, semantics, grammar and pragmatics.

03 Regulations in the detailed course outline
10 People and environment

(Human and Environmental Interactions)

Prerequisite: None

The course provides basic knowledge to build a correct attitude in perceiving the organic relationships between the development needs of human society and the exploitation and use of natural resources. The course aims to educate people to be aware of protecting the living environment and combating pollution problems. The course provides students with an understanding of global environmental issues and solutions. In addition, practical activities in class are integrated into the lectures to make them more vivid and practical.

03 Regulations in the detailed course outline
11 Climate Change Prerequisite: None

The course aims to provide students with basic knowledge about the Earth's climate patterns, causes of climate change, challenges and opportunities of climate change, impacts of climate change on resources and the environment, and how humans respond to climate change.

The course provides knowledge about the process by which global, national, and regional organizations develop climate change response plans.

The module describes how countries educate climate change knowledge to pupils and students.

03 Regulations in the detailed course outline
12 Calculus 1 Prerequisite: None

The course covers differential and integral calculus of a single variable, with emphasis on applications in various contexts. It is the foundation for subsequent courses in mathematics, engineering and social sciences. The basic content covers Chapters 1 - 8 of James Stewart's textbook. Major topics include: functions, limits of functions, continuity, derivatives, differentiation, applications of differentiation, integration, applications of integration in various fields (physics, engineering, economics and biology).

03 Regulations in the detailed course outline
13 ntroduction to data science with Python Prerequisite: None

The course provides students with a basic understanding of data science, including the workflow of working with data, from collection, preprocessing, analysis to visualization. Students will learn how to use Python and popular libraries such as NumPy, Pandas, Matplotlib for data processing and analysis, as well as implementing basic machine learning models. The course concludes with a hands-on project, allowing students to apply what they have learned. knowledge learned into practice

03 Regulations in the detailed course outline
14 Engineering Design Prerequisite: None

This course equips students with the fundamental knowledge, processes, and tools needed to effectively implement engineering design projects. The content focuses on the main stages of the design process, from problem identification and analysis, idea generation to solution development, evaluation, and selection. Students will be guided in the use of important design tools such as modeling, value analysis, and project management techniques. In addition, the course also emphasizes the training of teamwork skills, professional communication, and raising the sense of professional responsibility, especially in the context of sustainable design and social responsibility. Through real-life projects, students will have the opportunity to apply theoretical knowledge to solve practical engineering problems, develop creative thinking and in-depth problem-solving skills.

03 Regulations in the detailed course outline
15 Entrepreneurship Prerequisite: None

The course equips students with basic knowledge and practical skills in entrepreneurship, especially creative thinking and the ability to build a suitable business model in the modern economic context. Students will learn about the concepts, benefits and challenges of creative startups, combined with tools and methods such as Design Thinking, market research, building a Business Model Canvas, marketing strategy and financial management. The course also guides students in planning implementation, project management and pitching skills to raise capital from investors. Through practical activities, teamwork and real-life startup projects, the course helps students from many different majors develop interdisciplinary working skills, innovative thinking, and the ability to apply knowledge to solve practical problems. At the end of the course, students will be able to build and present a complete business idea, creating a foundation for future startup projects.

03 Regulations in the detailed course outline
16 Personal Financial Management Prerequisite: None

The course provides learners with knowledge and tools to enable them to plan their finances; build financial plans; analyze and make important financial decisions related to spending, saving, investing and risk management. It helps learners proactively make financial decisions, as well as develop their careers to become professional financial consultants at financial institutions.

03 Regulations in the detailed course outline
17 Marxist-Leninist philosophy Prerequisite: None

Marxist-Leninist philosophy is one of the three components of Marxism-Leninism. The course content includes 03 chapters, explaining general issues related to the existence and development of the world in general and the existence and development of human society in particular, equipping learners with a correct worldview, a positive outlook on life, as well as a dialectical and scientific methodology, in order to effectively solve problems arising in practice. The course is also the basis for students to well absorb Political Theory subjects, as well as other scientific subjects.

03 Regulations in the detailed course outline
18 Marxist-Leninist Political Economy Prerequisite: MACL108

Based on the course objectives, the content of the Marxist-Leninist political economy course is structured into 6 chapters. It helps students grasp the most basic issues of goods, markets; surplus value in the commodity economy, industrialization, modernization, and integration of Vietnam.

02 Regulations in the detailed course outline
19 Ho Chi Minh Thought Prerequisites: MACL108, MACL109, MACL110

Based on the purpose of the course, the content of the Ho Chi Minh Thought course is structured into 6 chapters, the content discusses the concept of Ho Chi Minh Thought, its origin, stages of development, objects, research tasks and basic ideological contents of Ho Chi Minh. The Ho Chi Minh Thought course has a close relationship with the subjects of the Revolutionary Line of the Communist Party of Vietnam, Basic principles of Marxism-Leninism. Because the Party's line is the creative application and development of Marxism-Leninism and Ho Chi Minh Thought into the practice of the Vietnamese revolution.

02 Regulations in the detailed course outline
20 Science Socialism Prerequisite: MACL108

Based on the purpose of the course, the content of the scientific socialism course is structured into 7 chapters. Providing students with scientific theoretical bases to understand and have revolutionary faith in the path of building and developing the country in the current transitional period to socialism in Vietnam.

02 Regulations in the detailed course outline
21 History of the Communist Party of Vietnam Prerequisites: MACL104, MACL108, MACL109, MACL110

The course History of the Communist Party of Vietnam basically studies the process of formation and development of the Party and the contents of the Party's guidelines set out in the process of leading the Vietnamese revolution from 1930 to the present. Therefore, the main content of the course is to provide students with basic and systematic understanding of the Party's viewpoints, guidelines and policies, especially in the period of renovation. The course History of the Communist Party of Vietnam has a close relationship with the subject Basic principles of Marxism-Leninism and the subject Ho Chi Minh Thought. Because the Party's guidelines are the creative application and development of Marxism-Leninism and Ho Chi Minh Thought into the practice of the Vietnamese revolution.

02 Regulations in the detailed course outline
22 Fundamentals of Law Prerequisite: None

The course provides basic, systematic knowledge of law and some basic branches of law in the Vietnamese legal system to raise legal awareness and form voluntary law-abiding behavior for learners.

02 Regulations in the detailed course outline
23 Introduction to Informatics Prerequisite: None

Provides basic computer skills including an overview of computer systems, searching and using the Internet, online learning, information security, computer protection, and office skills with Microsoft Office.

02 Regulations in the detailed course outline
24 Physical Education 1 Prerequisite: None

This course provides learners with basic knowledge of Physical Education, as well as knowledge of team formation and general development exercises. Through this, learners will know how to organize, manage a group and be able to compose general development exercises.

01* Regulations in the detailed course outline
25 Physical Education 2 Prerequisite: MACL1051

The course equips students with basic knowledge about the history and development of table tennis, basic technical principles in table tennis. The above knowledge helps students to be able to organize their own practice of table tennis techniques as well as practice general and specialized physical qualities.

01* Regulations in the detailed course outline
26 Physical Education 3 Prerequisite: MACL1051, MACL1052

The course equips students with basic knowledge about the history and development of table tennis, basic technical principles in table tennis. The above knowledge helps students to be able to organize their own practice of table tennis techniques as well as practice general and specialized physical qualities.

01* Regulations in the detailed course outline
27 National Defense and Security Education Prerequisite: None

Content issued with Circular No. 03/2017/TT-BGDDT dated January 13, 2017 of the Minister of Education and Training on promulgating the national defense and security education program in secondary pedagogical schools, pedagogical colleges and universities.

08* Regulations in the detailed course outline
28 English 1 Prerequisite: None

This course aims to improve general English skills, meeting the requirements of learning and communicating in English. Lessons are oriented towards integrated skills (Listening, Speaking, Reading, Writing) and are delivered in real-life topics with pictures, stories, and video clips.

03 Regulations in the detailed course outline
29 Intensive English 1 Prerequisite: None

This course aims to improve general English skills, meeting the requirements of learning and communicating in English. Lessons are oriented towards integrated skills (Listening, Speaking, Reading, Writing) and are delivered in real-life topics with pictures, stories, and video clips.

02* Regulations in the detailed course outline
30 English 2 Prerequisites: ESL101, ESLi101

This course continues the English 1 course, aiming to improve general English skills, meeting the requirements of studying and communicating in English. Lessons are oriented towards integrated skills (Listening, Speaking, Reading, Writing) and are delivered in real-life topics with pictures, stories, and video clips.

03 Regulations in the detailed course outline
31 Intensive English 2 Prerequisites: ESL101, ESLi101

This course continues the English 1 course, aiming to improve general English skills, meeting the requirements of studying and communicating in English. Lessons are oriented towards integrated skills (Listening, Speaking, Reading, Writing) and are delivered in real-life topics with pictures, stories, and video clips.

02* Regulations in the detailed course outline
32 English 3 Prerequisites: ESL102, ESLi102

This course is a continuation of General English 2 (ESL102), aiming to improve general English skills, meeting the requirements of learning and communicating in English. Lessons are oriented towards integrated skills (Listening, Speaking, Reading, Writing) and are delivered in real-life topics with pictures, stories, and video clips.

03 Regulations in the detailed course outline
33 Intensive English 3 Prerequisites: ESL102, ESLi102

Students are equipped with the knowledge and skills (Listening and Reading) necessary for the TOEIC test. After completing this course, students will achieve a score of 350-400 on the TOEIC test.

02* Regulations in the detailed course outline
34 English 4 Prerequisites: ESL103, ESLi103

This course is a continuation of English 3 (ESL103), aiming to improve general English skills, meeting the requirements of studying and communicating in English. Lessons are oriented towards integrated skills (Listening, Speaking, Reading, Writing) and are delivered in real-life topics with pictures, stories, and video clips.

03 Regulations in the detailed course outline
35 Intensive English 4 Prerequisites: ESL103, ESLi103

Students are equipped with the knowledge and skills (Listening and Reading) necessary for the TOEIC test. After completing this course, students will achieve a score of 400-450 on the TOEIC test.

02* Regulations in the detailed course outline
36 Calculus 2 Prerequisite: MATH101V

Topics that will be covered in this second semester of introductory mathematics are improper integrals, introduction to probability and distributions, infinite series and sequences, Taylor polynomials, Fourier series, vectors and vector functions, partial differentiation, the Lagrange multiplier method, and topics in differential calculus.

03 Regulations in the detailed course outline
37 (Linear Algebra Prerequisite: None

This course provides knowledge and applications of vectors, vector spaces, systems of linear equations, matrices, determinants, linear transformations, inner products, eigenvalues, eigenvectors, matrix diagonalization, etc.

03 Regulations in the detailed course outline
38 Introductory Mechanics Prerequisite: None

Mechanics is a branch of physics that deals with the motion of objects. The objective of this course is to introduce undergraduate students (mainly freshmen or sophomores) to classical mechanics and its applications to practical problems in science and technology. Laboratory experiments and group activities are also an important part of this course.

03 Regulations in the detailed course outline
39 Introductory Electricity and Magnetism Prerequisite: PHYS101V

This course introduces the basic principles and concepts of electricity, magnetism, and optics, a branch of fundamental physics that continues the previous course on Mechanics. Topics covered include electric charge and electromagnetic fields, electric potential, electric circuits, magnetism, electromagnetic waves, and geometrical optics. In addition, this course will incorporate simulations, hands-on laboratory experiments, and collaborative group activities. to enrich the learning experience.

03 Regulations in the detailed course outline
40 Introduction to Computer Science & Programming with Python Prerequisite: None

An introduction to the practices and principles of Computer Science and Programming and their impact and potential to change the world. Algorithms, problem solving, and programming techniques using high-level languages (Python) and design techniques that emphasize abstraction, encapsulation, problem decomposition, recursion. Design, implement, and test programs. Topics also include object-oriented programming and other Popular Python library. Prerequisite for all other courses in Computer Science.

03 Regulations in the detailed course outline
41 Probability & Statistics Prerequisite: None

This course deals with data analysis and statistical methods used in business and economics. Major topics include: introduction to probability: distributions, expectations, variances, portfolios, central limit theorem; Statistical inference from univariate data: confidence intervals, hypothesis testing; Statistical inference from bivariate data: inference for simple linear regression models; and introduction to statistical computer packages.

03 Regulations in the detailed course outline
42 Data Structure and Algorithms Prerequisite: CS111V

Analyze, use, and design data structures and algorithms using object-oriented languages such as Java to solve computational problems. Emphasis on abstraction including interfaces and abstract data types. Symbols for arrays/lists, trees, sets, tables/maps and graphs and their algorithms.

03 Regulations in the detailed course outline
43 Discrete Mathematics for CS Prerequisite: CS111V

This course introduces the theory and practice of discrete mathematics - the science of discrete objects. Discrete mathematics is an important element in recognizing mathematical structures in objects and understanding their properties. This ability is especially important for computer scientists, software engineers, data scientists, security analysts, financial analysts, etc. Basic topics of discrete mathematics include Mathematical Logic, Sets, Relations, Number Theory, Induction and Recursion, Counting, Boolean Algebra, and Computational Modeling. It is a prerequisite for all other courses in Computer Science.

03 Regulations in the detailed course outline
44 Computer Organization Prerequisite: CS111V

This course provides basic knowledge of hardware technology, C programming language, computer arithmetic, pipelines, memory hierarchy and input/output. In addition, the course helps students grasp the principles of computer operation, from understanding basic number systems and data representation to exploring how computers store and process information to perform calculations.

03 Regulations in the detailed course outline
45 Design & Analysis of Algorithms Prerequisite: CS201V

This course is a study of algorithm design, algorithm complexity analysis, and problem complexity analysis. Design techniques include brute force, reduce, divide and conquer, dynamic programming, greedy algorithms, iterative improvement, backtracking, and branch and bound. The course is organized around some of the basic strategies of algorithm design, and algorithm design will be taught on par with analysis. Some more abstract but important topics will also be covered: NP-completeness, approximation algorithms, and lower bounds.

03 Regulations in the detailed course outline
46 Introduction to Operating Systems Prerequisite: CS111V

This course provides introductory concepts that serve as the basis of operating systems—a key part of any computer system. In particular, the course covers topics such as process and thread management, CPU scheduling, process synchronization and deadlock handling, memory management, I/O devices and storage management, file systems, security, and protection mechanisms.

03 Regulations in the detailed course outline
47 Object Oriented Programming Prerequisite: CS111V

This course introduces the object-oriented approach to programming using the Java language. The goal is to help students gain an understanding of the basic concepts of object-oriented programming such as: objects, classes, methods, inheritance, polymorphism, and interfaces, along with the basic principles of abstraction, modularity, and reuse in object-oriented design.

03 Regulations in the detailed course outline
48 Introduction to Database Prerequisite: CS201V

This course provides students with a solid foundation in database systems. Topics include: data modeling, database design theory, data definition and manipulation languages (e.g., SQL), indexing techniques, query processing and optimization, and database programming interfaces. In addition to relational and semi-structured databases (e.g., JSON), this course also introduces a number of other topics related to data management, distributed storage, and parallel processing.

03 Regulations in the detailed course outline
49 Introduction to Data Mining Prerequisite: CS201V

Data mining is the process of finding descriptive, understandable, and predictive models from large data sets. The main parts of this course include data mining analysis, frequent pattern and association rule mining, clustering, and classification. The course provides these fundamentals, while also covering advanced topics such as kernel methods, multidimensional data analysis, and complex graphs and networks. The course integrates concepts from related disciplines such as machine learning and statistics, and is suitable for a course in data analysis.

03 Regulations in the detailed course outline
50 Data Visualization Prerequisite: CS111V

Data visualization is the graphical representation of data, which plays an important role in representing data at both small and large scales. The main objective of this course is to provide skills to explore data, thereby revealing valuable information by extracting information, gaining insights from data and making effective decisions. In the course, various visualization libraries such as Matplotlib, Seaborn, ggplot, Plotly, Folium, etc. will be introduced.

03 Regulations in the detailed course outline
51 Introduction to Machine Learning Prerequisites: CS201V, MATH201V

This course will provide an overview of the fundamentals of machine learning. Students will learn about the types of problems that can be solved, the basic components, and how to build models in machine learning. Several key algorithms will be explored. Upon completion of the course, students will have a working knowledge of several supervised and unsupervised learning algorithms, along with an understanding of important concepts such as underfitting and overfitting, regularization, and cross-validation. Students will be able to identify the type of problem they are trying to solve, select an appropriate algorithm, tune parameters, and evaluate models.

03 Regulations in the detailed course outline
52 Big Data Prerequisite: CS311V

The Big Data course provides a foundational understanding of big data and cloud computing: its properties, characteristics, data sources, applications, and value. The course will cover distributed programming models (i.e., MapReduce) and big data management systems (both SQL and NoSQL) for big data applications. The course focuses more on hands-on experience with storage systems (Hadoop), big data processing on Spark, and orchestration with Airflow and Redis Queue. The course also introduces public cloud services such as AWS, Cloudera, and deployment solutions for big data applications on the cloud.

03 Regulations in the detailed course outline
53 Introduction to Artificial Intelligence Prerequisite: CS111V, CS202V or MATH110V or STA206V

This course introduces the fundamental concepts of Artificial Intelligence (AI). It focuses on the fundamental aspects of AI as the study of agents that are capable of perception and action. Students will learn about classical problem-solving strategies such as search and planning, as well as more modern topics such as knowledge representation and machine learning. Programming exercises will be given to illustrate the theoretical material. Upon completion of this course, students will have a solid foundation in the fundamental topics of Artificial Intelligence.

03 Regulations in the detailed course outline
54 Advanced machine learning Prerequisite: CS332V

The "Advanced Machine Learning" course continues to equip students with in-depth knowledge of modern machine learning methods, complementing the knowledge learned in the basic courses. The course focuses on three main topics: probability-based learning methods (with an emphasis on Bayesian theory and Bayesian networks), ensemble learning methods (including bagging and boosting techniques), and time series data processing. This course plays an important role in preparing students to research and apply advanced machine learning techniques to solve complex real-world problems.

03 Regulations in the detailed course outline
55 Deep Learning Prerequisite: CS332V

This course introduces deep learning. Deep learning has attracted significant attention in the industry due to its state-of-the-art results in computer vision and natural language processing. Students will learn the fundamentals and advanced techniques of deep learning, as well as modern techniques for building advanced models such as CNN, RNN, LSTM, Autoencoder, VAE, GAN, U-Net, Transformer, etc. Students will use TensorFlow/PyTorch and Keras API to build deep learning models.

03 Regulations in the detailed course outline
56 Bayesian statistics Prerequisite: STA206V

This course introduces Bayesian analysis and statistical decision theory, the theory of decision making under uncertainty. It covers topics such as the formulation of decision problems and the quantification of their components, optimal decisions, Bayesian models, simulation-based approaches to Bayesian inference (including MCMC algorithms), and hierarchical models.

03 Regulations in the detailed course outline
57 Software Design and Implementation Prerequisite: CS201V

Techniques for designing and building reliable, maintainable, and useful software systems. Programming models and tools for medium to large scale projects: version control, support tools, performance analysis, UML, design patterns, software architecture, GUI and usability, software engineering, testing, and documentation.

03 Regulations in the detailed course outline
58 Distributed Systems Prerequisite: CS205V

The course provides fundamental and in-depth knowledge of the design, development and management of systems whose components reside on multiple computers connected via a network. The course focuses on core concepts such as inter-process communication, synchronization, error handling, data consistency and security in a distributed environment. Students will practice building simple to complex distributed applications, familiarizing themselves with modern distributed programming technologies and models. This course plays an important role in equipping students with the ability to build large-scale, fault-tolerant and high-performance applications. The course requires students to practice building and deploying distributed projects (available on the internet).

03 Regulations in the detailed course outline
59 Computer Network Prerequisite: CS205V

The Computer Networks course provides students with fundamental and in-depth knowledge of the structure, operation, and protocols of modern computer networks, with a particular focus on the Internet and the TCP/IP model. This course plays an important role in building foundational knowledge for more in-depth courses on network security, network administration, and network applications. Knowledge from this course also helps students have a foundation to approach international certificates such as CCNA. The course content includes layered network architecture, protocols from the physical layer to the application layer, routing, switching, and socket programming.

03 Regulations in the detailed course outline
60 Probability & Stochastic Processes Prerequisite: MATH201V

This course equips students with basic knowledge and skills on the ideas of probability theory; conditional probability and conditional expectation; Markov chains in discrete time; Poisson processes; Markov processes in continuous time and introduction to Brownian motion.

03 Regulations in the detailed course outline
61 Web Application Development Prerequisite: CS301V

The course provides basic and advanced knowledge for students to build, deploy, and maintain modern web applications, from frontend using HTML, CSS, JavaScript to backend processing with Node.js, Express and database integration such as MongoDB. This is a core course in the information technology program, helping students become familiar with popular tools such as React, master the process of deploying and securing web applications, and closely connect with courses such as Programming Fundamentals and Databases, providing good support for studying Advanced Software Development or System Administration.

03 Regulations in the detailed course outline
62 Mobile Application Development

(Mobile Application Development)

Prerequisite: CS301V

Mobile Application Development is a specialized course that helps students master the knowledge and skills of developing applications on mobile platforms (Android and iOS). The course equips students with knowledge of application architecture, interfaces, and user interface (UI/UX), data processing, API integration, and application deployment to Google Play or the App Store. This is a foundational course that connects to courses such as Programming Fundamentals, Fundamentals data, and prepare for advanced courses.

03 Regulations in the detailed course outline
63 IoT Application Development

(IoT Application Development)

Prerequisite: CS301V

IoT Application Development is an elective course that provides students with the knowledge and skills needed to design and deploy IoT applications in areas such as smart homes, smart agriculture, and industry 4.0. This course equips students with an understanding of IoT system architecture, microcontroller programming (Arduino, ESP32), communication protocols (MQTT, HTTP), sensor and actuator integration, and data analysis on cloud platforms. This is a foundational course for students to apply knowledge from Computer Networks and Embedded Programming.

03 Regulations in the detailed course outline
64 Cloud computing

(Cloud computing)

Prerequisite: CS440V

The Cloud Computing course plays an important role in equipping students with basic knowledge and practical skills in cloud technology, a rapidly growing field with wide applications in the information technology industry. Students will learn about basic concepts, service models (IaaS, PaaS, SaaS), advanced technologies such as containers, serverless, and DevOps on the cloud. At the same time, the course provides skills in deployment, management, resource optimization, and security on popular cloud platforms such as AWS, Azure, and Google Cloud. The course has a close relationship with other subjects in the program such as Operating Systems, Computer Networks, Databases, and Software Development, creating a premise for students to apply cloud technology to different areas in the information technology industry.

03 Regulations in the detailed course outline
65 Introduction to Computer Vision

(Introduction to Computer Vision)

Prerequisites: CS111V, MATH110V

This course introduces basic concepts and techniques in the field of Computer Vision, an important branch of Artificial Intelligence. The course equips students with knowledge of image processing, image analysis, feature extraction and object recognition in images and videos. This course is the foundation for more in-depth courses in advanced image processing, machine learning and practical applications of computer vision. The course content includes basic image processing techniques, edge detection, image segmentation, feature extraction and an introduction to object recognition.

03 Regulations in the detailed course outline
66 Introduction to Natural Language Processing Prerequisites: CS111V, CS202V, MATH110V

The course introduces basic concepts, techniques and algorithms in the field of human language processing by computers. NLP plays a key role in many modern applications such as information retrieval, sentiment analysis, emotions, machine translation, chatbots and more. This course equips students with a solid foundation in text processing, parsing, semantics and practical applications of NLP. closely related to the subjects of Artificial Intelligence, Machine Learning, Data Mining and Statistics. Content includes text preprocessing, word representation, language modeling, syntactic analysis and other popular NLP applications.

03 Regulations in the detailed course outline
67 Software Project Prerequisite: Consultation with Academic Advisor required.

The course plays an important role in equipping students with practical skills and basic knowledge to manage and implement software projects from planning, requirement analysis to implementation and maintenance. This is a specialized course closely linked to subjects such as Programming, Software Engineering, and IT Project Management. The content includes project management techniques, software development methods, teamwork, documentation and practice of the entire software project life cycle through practical exercises and final projects.

04 Regulations in the detailed course outline
68 Information Retrieval and Web Search Prerequisite: CS311V

The course equips students with the knowledge and skills needed to build information retrieval systems from text and the web, which play an important role in fields such as Computer Science, Data Science, and Natural Language Processing. The course content includes the basic principles of information retrieval, models such as Boolean, vector space, and machine learning; text indexing techniques, system evaluation, clustering, document classification, and result ranking. In particular, the course delves into practical applications in web search such as data collection, PageRank algorithm, and metadata analysis. This course is closely linked to subjects such as Natural Language Processing, Machine Learning, and Databases, creating a foundation for the development of intelligent information systems.

03 Regulations in the detailed course outline
69 Data Preprocessing/Cleansing Prerequisite: CS311V, CS332V

The course provides an important foundation in data science, helping students master the techniques of data normalization, cleaning and transformation to ensure quality before analysis. This course is closely related to subjects such as Data Mining, Machine Learning and Artificial Intelligence, creating a foundation for effective data processing and exploitation. The content includes methods for handling missing data, errors, noise, duplicates, restructuring and optimizing data, combined with intensive practice and final projects.

03 Regulations in the detailed course outline
70 Data science project & deployment Prerequisite: Consultation with Academic Advisor required.

The course equips students with comprehensive knowledge and practical skills to complete a complete Data Science project, from collection, pre-processing, analysis, model building and evaluation, to deployment, connecting learned theory with practice, helping students master the workflow of a Data Scientist. This is an intensive practical course for the final stage of the Data Science program, after students have a foundation in Mathematics, Statistics, Programming, Data Mining and Machine Learning. The content includes: project management, project development process, processing and analyzing real data, building and evaluating Machine Learning models, model deployment, teamwork and presentation. The course is closely related to Advanced Mathematics, Applied Statistics (mathematical and statistical foundations), Python/R Programming (programming tools), Databases (data management and querying) and Data Mining, Machine Learning (algorithms and data analysis models). The main content revolves around: problem selection, data collection and preprocessing, data analysis and exploration, model building and evaluation, model deployment and project reporting.

04 Regulations in the detailed course outline
71 Practical Deep learning in Natural Language Processing Prerequisite: CS434V

The course equips students with in-depth knowledge and practical skills in applying deep learning to the field of natural language processing (NLP). This course plays an important role in connecting deep learning theory with practical NLP problems, enabling students to build and deploy advanced NLP systems. The course is closely related to the courses on Natural Language Processing, Machine Learning and Deep Learning. The course content includes text preprocessing techniques, popular deep neural network models in NLP (RNN, LSTM, GRU, Transformer), and their applications in tasks such as sentiment analysis, machine translation, question answering and text summarization.

03 Regulations in the detailed course outline
72 Practical Deep learning in Computer Vision Prerequisite: CS333V, CS434V

The course provides students with in-depth knowledge and practical skills in applying deep learning in the field of computer vision. This course focuses on building, training and deploying deep learning models for common computer vision problems such as image classification, object detection, image segmentation and image generation. This course is a logical continuation of the subjects on image processing, computer vision and machine learning, equipping students with a solid foundation for researching and developing advanced computer vision applications. The course content includes neural network architectures Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) and advanced training techniques.

03 Regulations in the detailed course outline
73 Pattern Recognition Prerequisites: MATH110V, STA206V, CS331V

The course provides fundamental and in-depth knowledge of pattern recognition methods, from data preprocessing techniques, feature extraction to classification algorithms and performance evaluation. The course equips students with the ability to analyze, design and implement pattern recognition systems in many practical applications, including image, audio, natural language and tabular data processing. This course is closely related to subjects such as Linear Algebra, Calculus, Statistics, Machine Learning and Data Mining. The course content includes types of data patterns, feature extraction, classification models (Naive Bayes, SVM, LDA, PCA, ANN), and applications on audio, image, NLP and tabular data.

03 Regulations in the detailed course outline
74 Cryptography and Secure Application Prerequisite: MATH201V

The course provides a solid foundation in modern cryptographic principles and techniques, as well as their applications in building secure systems. The course equips students with knowledge of symmetric and asymmetric encryption algorithms, hash functions, digital signatures, public key infrastructure (PKI), and security protocols. This course is an important foundation for more in-depth courses in information security and network security, and is closely related to computer networking and programming subjects. The course content includes basic concepts of cryptography, encryption algorithms, attack and defense methods, as well as practical applications of cryptography in system and data security.

03 Regulations in the detailed course outline
75 Data Science Topics Prerequisite: Consultation with Academic Advisor required.

The course aims to equip students with in-depth and practical knowledge of techniques and applications in data science. The course is organized in the form of seminars, with the participation of guest experts from businesses and universities. Students are encouraged to conduct independent research, practice in groups and write final reports. The course provides a solid foundation for students when participating in the following courses or applying in their careers.

03 Regulations in the detailed course outline
76 Reinforcement Learning Prerequisite: CS434V

This course introduces students to an important area of artificial intelligence that focuses on training agents to make optimal decisions in an environment. This course equips students with knowledge of popular reinforcement learning algorithms, from basic to advanced, along with the ability to apply them to solve real-world problems. The course is closely related to courses such as Machine Learning, Artificial Intelligence, and Advanced Mathematics. The course content includes basic concepts of reinforcement learning, algorithms such as Q-learning, SARSA, Deep Q-Networks, and their applications in various fields.

03 Regulations in the detailed course outline
77 Calculus 3 Prerequisite: MATH201V

This course covers two fundamental topics in functions of several variables: multiple integrals and vector field calculus. These topics pave the way for future developments in advanced mathematics or applications in engineering and probability. The first part of the course is on double and triple integrals of functions of two or three variables. Students will learn how to use polar, cylindrical, and spherical coordinates in multiple integrals. The course introduces Fubini's Theorem and changes of variables. The second part of the course focuses on vector field calculus. The main topics are line and surface integrals, which are connected to the double and triple integrals in the first part of the course by higher-dimensional versions of the Fundamental Theorems of Calculus that students encountered in Math 101: Green's Theorem, Stokes' Theorem, and the Divergence Theorem. The primary goal is to teach Chapters 15 and 16 of Stewart.

03 Regulations in the detailed course outline
78 Graduation project Prerequisite: Consult with Academic Advisor or instructor.

The Graduation Project is the final and comprehensive course in the curriculum program, playing a key role in assessing the ability to apply learned knowledge to solve practical problems in the field of Information Technology. This course equips students with the skills to research, analyze, design, implement and evaluate a complete software system, data science or machine learning problem. The graduation project demonstrates the student's ability to self-study, think creatively and work independently. The course is closely related to all previously studied specialized courses, especially courses on system analysis and design, programming, databases, data science and machine learning. The result of the course is a working demo program and a detailed report (or presentation slide). The project content is guided by the lecturer and chosen by the student and registered with the faculty.

08 Regulations in the detailed course outline
79 Graduation thesis Prerequisites: Consultation with Academic Advisor, instructor, and GPA of 3.0 or greater at time of registration.

The Graduation Thesis course is the final, comprehensive and in-depth course in the curriculum program. This course equips students with the knowledge and skills to conduct an independent scientific research project under the guidance of a lecturer. Students will be guided on research methods, from reading and synthesizing documents, analyzing and evaluating previous research works, developing new ideas, conducting experimental research (if any), writing scientific reports and presenting research results to the council. This course has a close relationship with all specialized courses that have been studied, applying and synthesizing the knowledge that has been equipped to solve a specific problem in the specialized field. The result of the course is a complete thesis and a demo program (if any), demonstrating the student's ability to conduct independent and in-depth research.

08 Regulations in the detailed course outline
80 Graduation essay Prerequisite: Consultation with Academic Advisor required.

The Graduation Essay course is the final and important stage in the curriculum program, allowing students to apply the knowledge and skills they have learned to conduct an in-depth study on a specific topic in the field of Computer Science, Data Science, Machine Learning or Software Systems. This course equips students with the skills of independent research, analysis, synthesis of information, writing scientific reports and presentations. It has a close relationship with previous specialized courses, providing a basis for students to continue studying at higher levels or participate in research and development activities. The course content includes choosing a topic, building an outline, collecting and processing data, analyzing results and writing reports.

04 Regulations in the detailed course outline
81 Internship 1 Prerequisite: Consultation with Academic Advisor required.

Internship 1 is an important step in the training process, helping students apply the theoretical knowledge they have learned to the actual working environment at the enterprise. This course provides students with the opportunity to experience a professional working environment, be exposed to new technology and practice the necessary skills for their future career. The internship content is built on the coordination between lecturers and enterprises, ensuring practicality and conformity with the curriculum program. This course has a close relationship with specialized courses. In this course, students will do an internship at the enterprise. It is required to have a supervisor, connect with the enterprise to jointly evaluate knowledge, skills, attitudes and the level of completion of work according to the time schedule. The work content is discussed and agreed upon by the lecturer and the enterprise. The minimum internship period is from 6 to 8 weeks.

04 Regulations in the detailed course outline
82 Internship 2 Prerequisite: Consultation with Academic Advisor required.

Internship 2 is an important stage that helps students apply the knowledge they have learned to the actual working environment at the enterprise. This course creates opportunities for students to continue to experience practical work, get acquainted with corporate culture, develop professional skills and soft skills. Students will be assigned specific tasks under the guidance of lecturers and instructors at the enterprise, and at the same time be assessed on their knowledge, skills, attitudes and level of work completion. The course has a close relationship with specialized courses, helping students better understand the application of theoretical knowledge in practice. In this course, students will do an internship at the enterprise. It is required to have a supervisor, connect with the enterprise to jointly evaluate knowledge, skills, attitudes and level of work completion according to the time schedule. The content of the work is discussed and agreed upon by the lecturer and the enterprise. The minimum internship period is from 10 to 12 weeks.

06 Regulations in the detailed course outline

 

PART II. PROGRAM USER GUIDE

Required by the Ministry of Education and Training in coordination with other ministries/sectors to develop and issue for implementation.

 

  • 01 credit is equivalent to 15 credit hours.
  • 01 credit hour in class is equal to 01 class period and 02 self-study periods.
  • 01 credit hour in practice is equal to 02 practice periods in class and 01 self-study period.
  • 01 credit hour in compulsory self-study is equal to 03 compulsory self-study periods but must be tested and evaluated.
  • It is specifically shown in the annual enrollment plan and the detailed outline of each subject approved by the Principal at the beginning of each course.

According to the training regulations set by TTU.

 

4.1. Conditions for consideration and recognition of graduation

Implemented according to the University Training Regulations of Tan Tao University (Issued under Decision No. 31/QD-DHTT.21, Long An, dated June 30, 2021 of the President of Tan Tao University).

  1. Accumulate enough credits (minimum 130 credits) and complete other required content as required by the training program;
  2. Cumulative GPA of the entire course must be at least 2.00;
  3. Meet the foreign language output standards as prescribed by the School: TOEFL iBT 61 or IELTS 5.0 or equivalent;
  4. Complete the Physical Education (PE) and National Defense and Security Education (NDE) modules;
  5. Have a Soft Skills certificate provided by the school;
  6. Meet the required number of hours of participation in community service activities as prescribed;
  7. At the time of graduation, not being prosecuted for criminal liability or not being subject to disciplinary action at the level of suspension from school;
  8. Fulfill obligations to the school;
  9. Register for graduation according to regulations at the Training Management Department.

4.2. Graduation recognition

The principal shall issue a graduation certificate based on the graduation recognition results according to school regulations.

 

5.1. Graduation internship implementation plan

- Graduation internship: At technology companies, research institutes,.... introduced by the Faculty or found by the student himself but must be presented to the Faculty and receive approval from the Faculty and the School.

 


Details of the 2022 Curriculum Program, please see below::