1. DECISION ON THE ESTABLISHMENT OF THE ENGLISH-MEDIUM UNDERGRADUATE PROGRAM IN ARTIFICIAL INTELLIGENCE
2. UNDERGRADUATE TRAINING PROGRAM
UNDERGRADUATE ACADEMIC PROGRAM IN: ARTIFICIAL INTELLIGENCE
(Issued together with Decision No. 30/QD-DHTT.26 dated January 24, 2026 by Provost of Tan Tao University)
1. General information about the academic program
– Program Title (in Vietnamese): Trí tuệ Nhân tạo
– Program Title (in English): Artificial Intelligence
– Level: Undergraduate
– Program Code: 7480107
– Program Duration: 4 years – 8 semesters
– Mode of study: Full-time
– Total Credits: 130 credits
– Degree Awarded: Bachelor of Artificial Intelligence
– Medium of Instruction: English
2. Admission Requirements
Applicants must meet the admission criteria specified in the official admission guidelines of Tan Tao University.
3. Graduation Requirements
According to the Training Regulations of Tan Tao University.
- Complete all required courses/credits (a minimum of 130 credits) and other compulsory requirements of the curriculum;
- Achieve a minimum cumulative GPA of 2.00 for the entire program;
- Meet the University’s foreign language exit requirement: IELTS 7.0 or equivalent;
- Obtain a Soft Skills certificate issued by TTU;
- At the time of graduation consideration, not be subject to criminal prosecution and not be under disciplinary action at the level of suspension;
- Fulfill all financial and administrative obligations to the University;
- Submit an application for graduation consideration in accordance with the regulations of the Registrar’s Office.
4. Program Objectives
4.1. General Objectives
The objectives of the curriculum are designed to align with the Vision, Mission, and Educational Philosophy of Tan Tao University, and to be compatible with the Vision and Mission of the School of Information Technology, with the aim of nurturing individuals and promoting applied scientific research to meet societal needs.
The Bachelor’s curriculum in Artificial Intelligence (AI) aims to educate graduates who possess a solid and interdisciplinary foundation in science and technology; who are professionally developed and capable of innovation in the field of AI; who are committed to lifelong learning and adaptability to change; who can communicate effectively and collaborate across global interdisciplinary contexts; and who demonstrate social responsibility and professional ethics.
4.2. Specific Program Objectives (POs)
| POs | Description |
| 4.2.1. Knowledge | |
| PO1 | Interdisciplinary Knowledge:Possessing a solid interdisciplinary foundation in mathematics, natural sciences, life sciences, and social sciences, which fosters comprehensive and flexible thinking. |
| PO2 | Core Artificial Intelligence Knowledge:Possess core knowledge in Artificial Intelligence and Mathematics, thereby establishing a solid foundation for acquiring and developing AI-driven solutions in real-world contexts. |
| PO3 | Advanced Technologies:Possess in-depth knowledge in specialized fields such as Machine Learning, Deep Learning, Artificial Intelligence, Natural Language Processing, Computer Vision, Data Mining, and High-Performance Computing (HPC) platforms, thereby establishing the capability to architect and deploy intelligent systems adaptable to real-world environments. |
| 4.2.2. Skills: | |
| PO4 | Critical Thinking and Complex Problem Solving:Demonstrate critical thinking, a systematic approach, and the application of scientific methods to analyze and solve complex, multidimensional problems in technology and society. |
| PO5 | Creativity and Innovation Skills:Exhibit creative thinking, design, and development of breakthrough solutions, with an entrepreneurial mindset and the ability to identify innovation opportunities in technology. |
| PO6 | Lifelong Learning Ability:Demonstrate self-learning, self-research, and continuous adaptability to technological developments, including the ability to comprehend specialized literature in English. |
| PO7 | Communication and Interdisciplinary Collaboration Skills:Demonstrate the ability to articulate complex ideas, exchange professional knowledge, work effectively in multicultural environments, and collaborate with experts from diverse fields. |
| 4.2.3. Autonomy and Responsibility: | |
| PO8 | Social Awareness and Technology Ethics:Possess a deep understanding of the societal impacts of technology, including issues related to privacy, security, AI fairness, and the social responsibility of technology practitioners. |
| PO9 | Leadership and Management Ability:Demonstrate leadership skills, effective project management, data-driven decision-making, and the ability to coordinate cross-functional teams in a global work environment. |
| PO10 | Personal Development and Global Citizenship Awareness:Possess an awareness of comprehensive personal development, including humanistic values, global citizenship responsibility, and a commitment to contributing to addressing global challenges through technology. |
5. Program Learning Outcomes (PLOs)
| PLOs | Description |
| 5.1 Knowledge | |
| PLO1 | Foundation in Natural, Human, and Environmental Sciences:Basic understanding of natural sciences, human studies, and environmental sciences, as well as their significance, applications, and impacts across various professions in society. |
| PLO2 | Foundation in Politics, Law, Economics, and Society:
Basic understanding of economics and management, political theory, culture, society, law. In addition, knowledge of the culture and society of global civilizations. |
| PLO3 | Foundation in Artificial Intelligence:Possess a foundational understanding of algorithms, data structures, databases, operating systems, computer networks, and computer architecture, alongside the principles of Artificial Intelligence, Machine Learning, Deep Learning, and model optimization methods. |
| PLO4 | Foundation in Mathematics:Understanding of mathematical models, including probability and statistics, linear algebra, graph theory, and optimization, for designing, evaluating, and optimizing algorithms. |
| PLO5 | Applied Specializations:Depending on the chosen specialization orientation, knowledge in each track includes: |
| PLO5a – Computer Vision: Understanding of computer vision techniques such as image processing, object classification, detection, and segmentation. Mastery of methods for designing, training, and deploying deep learning models for visual data across diverse computing platforms. | |
| PLO5b – Natural language processing: Understanding of natural language processing techniques such as syntax parsing, text classification, entity extraction, and language synthesis. Mastery of methods for constructing, fine-tuning, and evaluating modern language models for real-world linguistic challenges. | |
| 5.2 Skills | |
| 5.2.1 Hard skills | |
| PLO6 | Model construction and development:Apply Artificial Intelligence knowledge to analyze, construct, and deploy Artificial Intelligence models for real-world intelligent solutions. Proficient in at least one high-level programming language. |
| PLO7 | Selection and Application of Technological Solutions: Identify, evaluate, and select appropriate technological solutions for developing practical Artificial Intelligence applications. |
| 5.2.2 Soft skills | |
| PLO8 | Communication, Critical Thinking, and Foreign Language Proficiency: Demonstrate effective communication skills in Vietnamese and English (IELTS 7.0 or equivalent), the ability to critically evaluate, present, negotiate, and manage situations in academic and professional environments. Utilize professional literature effectively. |
| PLO9 | Leadership, Teamwork, and Entrepreneurship: Demonstrate effective teamwork skills, including planning, task allocation, supervision, and performance evaluation; possess an entrepreneurial mindset, the ability to create employment opportunities, and proficiency in tools supporting collaborative work. |
| 5.3 Autonomy and Responsibility | |
| PLO10 | Professional Awareness, Ethics, and Social Responsibility: Possess a clear understanding of ethical and social responsibility in the development and application of Artificial Intelligence; ensure transparency, fairness, and data privacy; comply with legal regulations and actively contribute to the community. |
| PLO11 | Self-Learning and Sustainable Personal Development: Demonstrate lifelong learning ability and adaptability to change; work with discipline, responsibility, collaboration, and autonomy to develop long-term professional competencies. |
3. COURSE SYLLABUS
| No. | Course name | Course objectives | Cr | Assessment |
| 1 | World Civilization History | Prerequisites: NoneThis course provides students with fundamental and systematic knowledge about the history of formation, development, and some outstanding achievements in culture, science, and technology of prominent ancient and medieval civilizations in the East such as Egypt, India, and China, and in the West such as Greece, Rome, and Western European countries. This helps students gain a foundational understanding of the history of human development and progress. | 03 | As specified in the course syllabus |
| 2 | Modern times | Prerequisites: NoneThe course provides knowledge covering world history from the discovery of the New World and the American Revolution to the end of the 20th century. Significant changes throughout history have resulted from trade, militarial power, and democracy. These events include the Industrial Revolution, European imperialism, trade and globalization, the world wars, the rise of superpowers, and more. | 03 | As specified in the course syllabus |
| 3 | Introduction to Cultural Studies | Prerequisites: NoneThis course provides students with fundamental theories of cultural studies, including: basic conceptual systems of culture, methods of identifying culture, some specific cultural issues (yin-yang philosophy, symbolic culture, maritime culture, water culture, etc.), some general aspects of Vietnamese and world culture, applied culture, etc. | 03 | As specified in the course syllabus |
| 4 | Contemporary Art | Prerequisites: NoneThis course provides students with a fundamental understanding of art from its beginnings to the present day. Contemporary art is situated in a world of global influence, cultural diversity, and technological advancement. The dynamic combination of materials, methods, concepts, and themes continues to challenge boundaries that were well-established in the 20th century. Contemporary art is part of a cultural dialogue that relates to larger contextual frameworks such as personal and cultural identity, family, community, and nationality. | 03 | As specified in the course syllabus |
| 5 | Vietnamese and Other World Classic Cultures | Prerequisites: NoneThis course provides students with a basic understanding of Vietnamese culture (identity, value system, regional culture, culinary culture, etc.) and some representative world cultures (Korea, Japan, China, etc.), helping learners gain a fundamental understanding of Vietnamese culture and some representative world cultures. | 03 | As specified in the course syllabus |
| 6 | Culture and Literature | Prerequisites: NoneThis course provides students with a fundamental understanding of culture and literature, including: general theories of culture and literature; the role of culture and literature; basic knowledge of Vietnamese culture and some representative world cultures; and some classic literary works of Vietnam and the world. | 03 | As specified in the course syllabus |
| 7 | Writing and Ideas | Prerequisites: NoneThis module aims to help students develop their thinking skills, enhance their reasoning abilities, and effectively evaluate and respond to information being presented. It is not limited to written presentation and oral communication but focuses on the structure of arguments and avoiding logical pitfalls. Information will be analyzed from news, public records, films, slides, transcripts, and any other media sources, and then incorporated into a well-structured essay. | 03 | As specified in the course syllabus |
| 8 | Leadership and Communication | Prerequisites: NoneThis course provides students with fundamental and systematic knowledge of historical, theoretical, and practical perspectives on leadership (characteristics, skills, styles, situations, contingencies, pathways, transformational leadership, and leadership teams) and communication (communication elements, the communicative position of a leader; using social and communicative position to communicate effectively in a leadership role). The course will also guide learners towards applying these theories to real-world problems. | 03 | As specified in the course syllabus |
| 9 | Language and Vietnamese | Prerequisites: NoneThis course provides students with a basic understanding of language in general (origin, nature, function, etc.) and Vietnamese with its fundamental characteristics: phonetics, vocabulary, semantics, grammar, and pragmatics. | 03 | As specified in the course syllabus |
| 10 | Human and Environmental Interactions | Prerequisites: NoneThis course provides fundamental knowledge to develop a correct attitude in understanding the organic relationship between the development needs of human society and the exploitation and use of natural resources. The course aims to educate people on the importance of protecting the environment and combating pollution. It provides students with an understanding of global environmental issues and solutions. Furthermore, practical activities are integrated into the lectures, making them more engaging and relevant. | 03 | As specified in the course syllabus |
| 11 | Climate Change | Prerequisites: NoneThis course aims to provide students with fundamental knowledge about the Earth’s climate patterns, the causes of climate change, the challenges and opportunities of climate change, the impacts of climate change on resources and the environment, and how humans respond to climate change. The course also provides knowledge about the processes by which global, national, and regional organizations develop climate change response plans. Furthermore, the course describes how countries educate students about climate change. | 03 | As specified in the course syllabus |
| 12 | Calculus 1 | Prerequisites: NoneThis module covers differential and integral calculus of a variable, emphasizing applications in various contexts. It forms the foundation for subsequent courses in mathematics, engineering, and social sciences. The core content covers Chapters 1–8 of James Stewart’s textbook. Key topics include: functions, limits of functions, continuity, derivatives, differentials, applications of differentials and integrals, and applications of integrals in various fields (physics, engineering, economics, and biology). | 03 | As specified in the course syllabus |
| 13 | Introduction to Data Science | Prerequisites: NoneThe Introduction to Data Science course provides learners with fundamental knowledge and skills in Data Science, from Python programming and data processing to basic machine learning techniques. The course content includes an overview of Data Science, steps in the data mining process, working with tabular data, data visualization and preprocessing, machine learning methods (classification, regression, clustering), data dimensionality reduction, and time series data processing. In addition to theory, the course emphasizes practical exercises using Python and popular libraries such as Pandas, NumPy, Matplotlib, and Scikit-learn, helping learners become familiar with and solve real-world data problems. | 03 | As specified in the course syllabus |
| 14 | Engineering Design | Prerequisites: NoneThis course equips students with the fundamental knowledge, processes, and tools necessary to effectively implement engineering design projects. The content focuses on the key stages of the design process, from problem identification and analysis, idea generation, to development, evaluation, and solution selection. Students will be guided in using important design tools such as modeling, value analysis, and project management techniques. Furthermore, the course emphasizes developing teamwork skills, professional communication, and a heightened sense of professional responsibility, particularly in the context of sustainable and socially responsible design. Through practical projects, students will have the opportunity to apply theoretical knowledge to solve real-world engineering problems, develop creative thinking, and enhance problem-solving skills. | 03 | As specified in the course syllabus |
| 15 | Principle of Economics | Prerequisites: None This module provides an introduction to the fundamental principles of both microeconomics and macroeconomics. Students will learn how individuals, businesses, and governments make decisions in the face of scarcity, and how these decisions interact in markets and throughout the economy. Topics include supply and demand, consumer behavior, producer behavior, market structure, externalities, public goods, national income, unemployment, inflation, monetary policy, fiscal policy, and international trade. The module emphasizes the application of economic principles to real-world business situations and policy issues. | 03 | As specified in the course syllabus |
| 16 | Administrative Office Management | Prerequisites: NoneOffice Management is a course designed to equip students with the knowledge and skills to effectively organize, manage, and control administrative activities in a modern business environment. The course focuses on task management, information processing, record keeping, meeting organization, and the application of technology tools such as Google Workspace and Microsoft Office, helping students master the digital office environment and enhance their professional work performance. | 03 | As specified in the course syllabus |
| 17 | Personal Finance Management | Prerequisites: NoneThis course provides learners with the knowledge and tools to develop financial planning skills; build financial plans; analyze and make important financial decisions related to spending, saving, investing, and risk management. It helps learners proactively make financial decisions and develop careers to become professional financial advisors at financial institutions. | 03 | As specified in the course syllabus |
| 18 | Marxist-Leninist Philosophy | Prerequisites: NoneMarxist-Leninist philosophy is one of the three constituent parts of Marxism-Leninism. The course content consists of three 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. It equips students with a correct worldview, a positive philosophy of life, and a dialectical and scientific methodology to effectively solve problems arising in practice. The course also serves as a foundation for students to better understand Political Theory and other scientific subjects. | 03 | As specified in the course syllabus |
| 19 | Marxist-Leninist Political Economy | Prerequisite: Marxist-Leninist philosophyBased 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 regarding commodities, markets; surplus value in the commodity economy, industrialization, modernization, and integration of Vietnam. | 02 | As specified in the course syllabus |
| 20 | Scientific Socialism | Prerequisite: Marxist-Leninist philosophyBased on the course objectives, the curriculum for the Scientific Socialism course is structured into 7 chapters. It provides students with the scientific theoretical foundations to understand and have revolutionary faith in the path of national construction and development during the current transitional period to socialism in Vietnam. | 02 | As specified in the course syllabus |
| 21 | Ho Chi Minh Thought | Prerequisites: Marxist-Leninist philosophy, Marxist-Leninist political economy, scientific socialism.Ho Chi Minh Thought course is structured into 6 chapters, discussing the concept of Ho Chi Minh Thought, its origins, stages of development, research subjects and tasks, and the basic ideological content of Ho Chi Minh. The Ho Chi Minh Thought course is closely related to the course on the Revolutionary Line of the Communist Party of Vietnam and the Basic Principles of Marxism-Leninism. This is because the Party’s line is the creative application and development of Marxism-Leninism and Ho Chi Minh Thought to the practical realities of the Vietnamese revolution. | 02 | As specified in the course syllabus |
| 22 | History of the Communist Party of Vietnam | Prerequisites: Ho Chi Minh Thought, Marxist-Leninist Philosophy, Marxist-Leninist Political Economy, Scientific SocialismThis course studies the formation and development of the Party and the content of its guidelines in leading the Vietnamese revolution from 1930 to the present. Therefore, the main content of the course is to provide students with a systematic understanding of the Party’s viewpoints, policies, and strategies, especially during the period of reform. The course on the History of the Communist Party of Vietnam is closely related to the course on the Basic Principles of Marxism-Leninism and the course on Ho Chi Minh Thought. This is because the Party’s guidelines are the creative application and development of Marxism-Leninism and Ho Chi Minh Thought to the practical realities of the Vietnamese revolution. | 02 | As specified in the course syllabus |
| 23 | Fundamentals of Law | Prerequisites: NoneThis course provides fundamental and systematic knowledge of law and some basic branches of law within the Vietnamese legal system, aiming to enhance legal awareness and foster voluntary compliance with the law among learners. | 02 | As specified in the course syllabus |
| 24 | Digital Literacy | Prerequisites: NoneDigital Competencies module equips students with the necessary skills and knowledge to effectively use digital technology in their studies, work, and daily lives. Students will learn how to operate devices and software, extract information and data, communicate and collaborate in a digital environment, protect personal information, create digital content, and develop digital skills for their careers. | 02 | As specified in the course syllabus |
| 25 | English 1 | Prerequisites: NoneThis module aims to improve general English skills to meet the requirements of English learning and communication. Lessons are skill-oriented (Listening, Speaking, Reading, Writing) and are presented through practical topics with images, stories, and video clips. | 04 | As specified in the course syllabus |
| 26 | English 2 | Prerequisites: General English 1, Advanced English 1This module follows on from English 1, aiming to improve overall English skills to meet the requirements of English learning and communication. Lessons are skill-based (Listening, Speaking, Reading, Writing) and are presented through practical topics with images, stories, and video clips. | 04 | As specified in the course syllabus |
| 27 | English 3 | Prerequisites: General English 2, Advanced English 2This module follows on from General English 2, aiming to enhance general English skills to meet the requirements of English learning and communication. Lessons are skill-oriented (Listening, Speaking, Reading, Writing) and are presented through practical topics with images, stories, and video clips. | 04 | As specified in the course syllabus |
| 28 | Applied Mathematics for DS | Prerequisites: General Mathematics 1, Discrete Mathematics, Probability and Statistics.Learn Applied Mathematics for Data Science This course provides essential mathematical knowledge for Computer Science and Artificial Intelligence. Content includes advanced linear algebra, multivariable calculus, optimization theory, and information theory. The course equips students with a solid mathematical foundation for application in machine learning, deep learning, and artificial intelligence. | 03 | As specified in the course syllabus |
| 29 | Linear Algebra | Prerequisites: Are notCourse module Linear algebra provides knowledge and applications of vectors, vector spaces, systems of linear equations, matrices, determinants, linear transformations, interior products, eigenvalues, eigenvectors, matrix diagonalization, etc. | 03 | As specified in the course syllabus |
| 30 | General Physics | Prerequisites: NoneCourse module This course provides students with a foundational understanding of key physical principles, focusing on core content directly related to Computer Science, Data Science, and Artificial Intelligence. The course content covers topics from Mechanics, Electricity, Magnetism, and Electromagnetic Waves, with a focus on practical applications in information technology and data science. | 03 | As specified in the course syllabus |
| 31 | Data Structure and Algorithms | Prerequisites: Introduction to programming or equivalent.This course analyzes , uses, and designs data structures and algorithms using object-oriented programming languages such as Java to solve computational problems. It emphasizes abstraction, including interfaces and abstract data types for arrays/lists, trees, sets, tables/maps, and graphs, as well as their algorithms. | 03 | As specified in the course syllabus |
| 32 | Discrete Mathematics | Prerequisites: Introduction to programming or equivalent.Course module This section introduces the theory and practical principles of discrete mathematics—a science specializing in discrete objects. Discrete mathematics is crucial for recognizing the mathematical structure of objects and understanding their properties. This ability is particularly important for computer scientists, software engineers, data scientists, security analysts, and financial analysts, etc. Fundamental topics in discrete mathematics include Mathematical Logic, Sets, Relations, Number Theory, Induction and Recursion, Counting, Boolean Algebra, and Computational Modeling. This knowledge is a prerequisite for all other courses in Computer Science . | 03 | As specified in the course syllabus |
| 33 | Introduction to Programming | Prerequisites: NoneThe Introduction to Programming course provides foundational knowledge of programming thinking and how to build computer programs using high-level programming languages. Through Python – a modern, popular, and accessible language – learners will become familiar with basic data structures, execution flow control, program organization using functions, file handling, and standard libraries. In addition, the course introduces several practical libraries such as NumPy and Pandas for data processing, expanding application capabilities in modern information technology fields such as artificial intelligence, data analysis, and automation.Through exercises and small projects, learners practice skills in writing structured code, testing and debugging programs, and presenting results according to technical standards. | 03 | As specified in the course syllabus |
| 34 | Probability and Statistics | Prerequisites: NoneCourse module Probability and statistics involve data analysis and statistical methods used fundamentally in business and economics. Key topics include: an introduction to probability: distributions, expectation, variance, portfolios, the central limit theorem; statistical inference of univariate data: confidence intervals, hypothesis testing; statistical inference for two-variable data: inference for simple linear regression models; and an introduction to statistical calculators. | 03 | As specified in the course syllabus |
| 35 | Introduction to Database | Prerequisites: Data structures and algorithms, and familiarity with JavaScript and/or Python.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. Besides relational and semi-structured databases (e.g., JSON), this course also introduces several other topics related to data management, distributed storage, and parallel processing. | 03 | As specified in the course syllabus |
| 36 | Introduction to Machine Learning | Prerequisites: Data structures and algorithms, Applied mathematics for data science.Learn This section will provide an overview of the fundamental principles of machine learning. Learners will explore 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 this section, learners will have practical knowledge of several supervised and unsupervised learning algorithms, along with an understanding of key concepts such as underfitting and overfitting, regularization, and cross-validation. Learners will be able to identify the type of problem they are trying to solve, select appropriate algorithms, fine-tune parameters, and evaluate models. | 03 | As specified in the course syllabus |
| 37 | Data Processing | Prerequisites: Introduction to Programming, Databases, Probability and StatisticsThis module equips learners with the knowledge and skills necessary to collect, clean, transform, and prepare data for data science projects. Learners will learn how to process data from various sources, address data quality issues, and prepare data for analysis and modeling. Through practical exercises and projects, learners will apply data processing techniques to real-world problems. | 03 | As specified in the course syllabus |
| 38 | Data Visualization | Prerequisite: Introduction to ProgrammingData visualization is the presentation of data graphically, playing a crucial role in representing data at both small and large scales. The main goal of this course is to provide skills for data mining, thereby revealing valuable information by extracting insights, gaining a better understanding of data, and making effective decisions. The course will introduce various visualization libraries such as Matplotlib, Seaborn, ggplot, Plotly, Folium, etc. | 03 | As specified in the course syllabus |
| 39 | Data Mining | Prerequisites: Data structures and algorithmsData mining is the process of gaining knowledge about descriptive, understandable, and predictive models from large-scale datasets. Key components of this module include data mining analysis, frequent pattern mining and association rules, clustering, and classification. The module provides these fundamental foundations while also addressing advanced topics such as kernel methods, multidimensional data analysis, and complex graphs and networks. The module integrates concepts from related fields such as machine learning and statistics, and is well-suited for a data analysis course. | 03 | As specified in the course syllabus |
| 40 | Econometrics | Prerequisite: Probability and StatisticsEconometrics course provides fundamental knowledge of linear regression models and estimation and hypothesis testing methods in quantitative analysis. Students will become familiar with how to construct, estimate, and evaluate econometric models using real-world data. The course equips students with the necessary foundation to apply data analysis techniques in socio-economic research and support data-driven decision-making. | 03 | As specified in the course syllabus |
| 41 | Principles of Marketing | Prerequisites: NoneThis course provides foundational knowledge of marketing concepts, processes, and strategies in the modern business environment. Students are introduced to consumer behavior, market research, customer segmentation, product positioning, and marketing tools such as product, price, distribution, and promotion. The course helps Data Science students understand the context of data application in marketing and support decision-making based on customer and market data analysis. | 03 | As specified in the course syllabus |
| 42 | Financial Management | Prerequisites: NoneThis course provides students with a foundational understanding of financial management in businesses, including principles of financial decision-making, cash flow management, financial analysis, asset valuation, and financing options. Students will develop analytical skills, the ability to use basic financial tools, and the ability to make financial decisions relevant to real-world business contexts. The course also contributes to the development of financial thinking and enhances ethical awareness in corporate financial management. | 03 | As specified in the course syllabus |
| 43 | Supply Chain Management | Prerequisites: NoneThis course provides fundamental knowledge about the supply chain management process from supplier to end customer, including activities such as planning, procurement, production, transportation, distribution, and inventory management. Students are introduced to concepts, models, and analytical tools to optimize supply chain efficiency. The course also emphasizes the role of data and analytics in supporting decision-making and improving supply chain performance in the modern business environment. | 03 | As specified in the course syllabus |
| 44 | Marketing and Customer Analytics | Prerequisite: Marketing principlesThis course provides students with the knowledge and skills necessary to understand and analyze customer behavior, as well as build data-driven marketing strategies. The course content includes fundamental marketing concepts, market segmentation, product positioning, consumer behavior, and customer analytics tools such as RFM analysis, CLV (Customer Lifetime Value) models, regression analysis, machine learning, and data visualization. Students will become familiar with popular analytics software and languages such as Excel, Python, or R. Through theoretical lectures combined with case studies and real-world projects, students will develop data-driven decision-making abilities in the field of marketing. | 03 | As specified in the course syllabus |
| 45 | Enterprise Data Analytics | Prerequisites: Econometrics; Supply chain managementBusiness Data Analytics course provides students with the knowledge and skills necessary to collect, process, analyze, and interpret data within a business context. Through this course, students will understand the role of data in supporting decision-making, optimizing operations, and developing business strategies.The course content includes: an overview of data science in businesses, data mining techniques, descriptive analytics, predictive analytics and diagnostic analytics, as well as data visualization. Students will also practice with popular tools such as Excel, SQL, Python, or R to apply them to real-world business scenarios. | 03 | As specified in the course syllabus |
| 46 | General Chemistry & Organic chemistry | Prerequisites: NoneThis course provides a foundational understanding of chemistry. Students will learn about the structure of matter and chemical composition, enabling them to name substances chemically and explain their physical and chemical properties. The course also equips students with basic knowledge in specialized areas such as analytical chemistry, inorganic chemistry, natural compounds, heterocyclic compounds, 3D structures, and the evaluation of structure-physical-chemical relationships. Based on this knowledge, students can apply it to assess the potential influence of organic compounds on biological activity. Students will acquire fundamental knowledge and skills in chemical and biological research. | 04 | As specified in the course syllabus |
| 47 | Cellular and Molecular Biology | Prerequisites: NoneMolecular and Cellular Biology course provides students with knowledge of biological mechanisms at the molecular level, including the structure, function, and regulation of macromolecules such as DNA, RNA, and proteins, as well as processes such as DNA replication, transcription, translation, gene mutation, and cellular signal transduction. The course covers chapters ranging from the history of molecular biology, gene structure, and genomes, to modern techniques such as PCR, DNA electrophoresis, and the application of genetic modification in research. In addition to theory, students gain practical experience in the laboratory to hone their skills and master fundamental techniques in this field. | 03 | As specified in the course syllabus |
| 48 | Genetics | Prerequisites: NoneThis course provides a foundational understanding of chemistry. Students will learn about the structure of matter and how to explain its physical and chemical properties. The course also equips students with basic knowledge in analytical chemistry, inorganic chemistry, and the structure of matter. Based on this knowledge, students can apply it to analyze chemical substances. Students will acquire fundamental knowledge and skills in chemical and biological research. | 02 | As specified in the course syllabus |
| 49 | Biochemistry | Prerequisites: NoneThis course teaches the fundamental principles of biochemistry, linking basic biochemistry with physiology, pathology, pharmacology, clinical diagnosis, and nutrition. Initially, students will learn about the essential structure and function of nucleic acids, proteins, lipids, and carbohydrates. Additionally, it will discuss the biosynthesis, transport, and degradation of macromolecules, as well as their relationship to disease. | 03 | As specified in the course syllabus |
| 50 | Introduction to Bioinformatics | Prerequisites: Nonemodule is designed to provide students with a foundational understanding of integrating Information Technology into Biology, primarily focusing on areas of Biotechnology as the quickest way to enter the field of Bioinformatics. Throughout this course, students will work with DNA and protein sequencing at the gene and genome levels using various Bioinformatics software to clarify numerous issues related to diseases or disorders caused by polymorphisms in gene/nucleotide sequences. In addition, the module will emphasize cutting-edge topics such as Epigenetics, Cancer Genetics, and Cancer Genomics, which are receiving significant attention from laboratories worldwide. Students who complete this module will gain a practical understanding of “What is Bioinformatics?” and its applications in various biological fields, particularly medicine and agriculture. | 03 | As specified in the course syllabus |
| 51 | Applied Data Science in Biology | Prerequisites: NoneThis course provides students with the foundational knowledge and skills to apply Python and data science techniques to biological data analysis. The course content includes biological data processing, sequence analysis (genome, protein), data visualization, and building simple machine learning models for biological research. Students will practice with popular Python libraries such as Pandas, NumPy, Biopython, Matplotlib, and Scikit-learn. Additionally, the course will provide a brief introduction to the R programming language. | 03 | As specified in the course syllabus |
| 52 | Graduation Thesis | Prerequisites: Consultation with an academic advisor and supervising lecturer is required, and a GPA of 3.0 or higher is necessary at the time of application. Students must have completed at least three years of study and accumulated the required number of credits by the time of consideration.Students will be guided on research methodology, from reading and synthesizing literature, analyzing and evaluating previous research, developing new ideas, conducting experimental research (if applicable), writing a scientific report, and presenting research results to a committee. This module is closely related to all previously completed specialized modules, applying and synthesizing acquired knowledge to solve a specific problem in the field of specialization. The outcome of this module is a complete thesis and a demo program (if applicable), demonstrating the student’s independent and in-depth research capabilities. | 10 | As specified in the course syllabus |
| 53 | Capstone Project | Prerequisites: Consultation with an academic advisor and supervising lecturer is required, and a GPA of 3.0 or higher is necessary at the time of application. Students must have completed at least three years of study and accumulated the required number of credits by the time of consideration.Students will be guided on research methodology, from reading and synthesizing literature, analyzing and evaluating previous research, developing new ideas, conducting experimental research (if applicable), writing a scientific report, and presenting research results to a committee. This module is closely related to all previously completed specialized modules, applying and synthesizing acquired knowledge to solve a specific problem in the field of specialization. The outcome of this module is a complete thesis and a demo program (if applicable), demonstrating the student’s independent and in-depth research capabilities. | 06 | As specified in the course syllabus |
| 54 | Graduation Internship | Prerequisite: Consultation with your academic advisor and supervising lecturer is required.This module is a crucial stage that helps learners apply their acquired knowledge to a real-world work environment within a business. It provides opportunities for learners to gain practical work experience, familiarize themselves with corporate culture, and develop both professional and soft skills. Learners will be assigned specific tasks under the guidance of instructors and mentors at the company, and will be evaluated on their knowledge, skills, attitude, and work completion level. The module is closely related to specialized modules, helping learners better understand the application of theoretical knowledge in practice. The work content is discussed and agreed upon between the instructor and the company. The minimum internship duration is 8 to 10 weeks, equivalent to 375 hours. | 10 | As specified in the course syllabus |
| 55 | Career Orientation Internship | Prerequisite: Consultation with your academic advisor and supervising lecturer is required.This module is a crucial stage that helps learners apply their acquired knowledge to a real-world work environment within a business. It provides opportunities for learners to gain practical work experience, familiarize themselves with corporate culture, and develop both professional and soft skills. Learners will be assigned specific tasks under the guidance of instructors and mentors at the company, and will be evaluated on their knowledge, skills, attitude, and work completion level. The module is closely related to specialized modules, helping learners better understand the application of theoretical knowledge in practice. The work content is discussed and agreed upon between the instructor and the company. The minimum internship duration is 5 to 7 weeks, equivalent to 225 hours. | 04 | As specified in the course syllabus |
| 56 | Graduation Essay | Prerequisite: Consultation with your academic advisor and supervising lecturer is required.This module is the final and crucial stage in the training program, allowing learners to apply their acquired knowledge and skills to conduct in-depth research on a specific topic in the fields of Computer Science, Data Science, Machine Learning, or Software Systems. This module equips learners with the skills for independent research, analysis, information synthesis, scientific report writing, and presentation. It is closely related to previous specialized modules, forming the foundation for further studies at higher levels or participation in research and development activities. The module content includes topic selection, outline development, data collection and processing, results analysis, and report writing. | 04 | As specified in the course syllabus |
| 57 | Big Data | Prerequisite: Introduction to DatabasesThis course This course provides foundational knowledge about big data and cloud computing: its attributes, characteristics, data sources, applications, and value. It will cover distributed programming models (i.e., MapReduce) and big data management systems (both SQL and NoSQL) for big data applications. The module focuses more on hands-on practice 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 and Cloudera, and solutions for deploying big data applications in the cloud. | 03 | As specified in the course syllabus |
| 58 | Introduction to AI | Prerequisites: Introduction to Programming, Discrete Mathematics, Linear Algebra, or Probability and Statistics.Course module This module introduces the fundamental concepts of Artificial Intelligence (AI). It focuses on foundational aspects of AI, such as the field of study of agents capable of perception and action. Learners will explore classic problem-solving strategies like search and planning, as well as more modern topics such as knowledge representation and machine learning. Programming exercises will be assigned to illustrate the theoretical material. Upon completion of this module, learners will have a solid foundation in the basic topics of Artificial Intelligence. | 03 | As specified in the course syllabus |
| 59 | Introduction to Deep Learning | Prerequisite: Introduction to Machine LearningThis module introduces deep learning. Deep learning has garnered significant attention in the industry due to its advanced results in computer vision and natural language processing. Learners will study fundamental and advanced concepts of deep learning, as well as modern techniques for building sophisticated models such as CNNs, RNNs, LSTMs, Autoencoders, VAEs, GANs, U-Nets, Transformers, etc. Learners will utilize TensorFlow/PyTorch and the Keras API to build deep learning models. | 03 | As specified in the course syllabus |
| 60 | Reinforcement Learning | Prerequisite: In-depth introductory courseThis course introduces students to a crucial area of artificial intelligence, focusing on training agents to make optimal decisions in a given environment. It equips students with knowledge of common reinforcement learning algorithms, from basic to advanced levels, 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 fundamental concepts of reinforcement learning, algorithms such as Q-learning, SARSA, Deep Q-Networks, and their applications in various fields. | 03 | As specified in the course syllabus |
| 61 | Federated Learning | Prerequisite: In-depth introductory courseThis course This course equips students with knowledge and skills in distributed machine learning for privacy protection. The content includes concepts, principles, algorithms (FedAvg, horizontal/vertical/transferred FL), challenges (non-IID, privacy, legal); methods for implementing FL models using Python (TensorFlow Federated, PySyft), applications in healthcare, IoT, vision, and language; awareness of privacy, legal issues (GDPR), and practical applications. The course helps learners understand and apply FL, develop programming and analytical skills, and cultivate ethical awareness, while encouraging self-learning and a final project. | 03 | As specified in the course syllabus |
| 62 | Operation Research | Prerequisites: Are notVận trù học là học phần nhập môn về nghiên cứu hoạt động, tập trung vào các khái niệm và kỹ thuật cơ bản để giải quyết các bài toán tối ưu hóa. Nội dung bao gồm lập trình tuyến tính, phương pháp Simplex, phân tích nhạy cảm, đối ngẫu, bài toán vận chuyển, mô hình mạng, và lập trình nguyên. Người học sẽ học cách xây dựng mô hình, phân tích và giải quyết các bài toán thực tế trong quản lý và vận hành, sử dụng các công cụ cơ bản như Excel Solver. Học phần giúp phát triển kỹ năng phân tích, giải quyết vấn đề, và làm việc nhóm, phù hợp với người học mới bắt đầu tìm hiểu về nghiên cứu hoạt động. | 03 | As specified in the course syllabus |
| 63 | Bayesian Statistics | Prerequisite: Probability and StatisticsBayesian Statistics course introduces Bayesian analysis and statistical decision theory, the theory of decision-making in uncertainty. The course will cover topics such as constructing decision problems and quantifying their components, optimal decisions, Bayesian models, simulation-based approaches to obtaining Bayesian inference (including MCMC algorithms), and hierarchical models. | 03 | As specified in the course syllabus |
| 64 | Probability & Stochastic Processes | Prerequisite: Applied MathematicsThe course on Probability and Random Processes equips students with fundamental knowledge and skills regarding the ideas of probability theory; conditional probability and conditional expectation; Markov chains in discrete time; Poisson processes; Markov processes in continuous time; and an introduction to Brownian motion. | 03 | As specified in the course syllabus |
| 65 | Data science project & Deployment | Prerequisites: Basic knowledge of programming and software engineering (Refer to your Academic Advisor)Data Science Project and Implementation module equips learners with comprehensive knowledge and practical skills to complete a full Data Science project, from data collection, preprocessing, analysis, model building and evaluation, to implementation, connecting learned theory with practice, helping learners master the workflow of a Data Scientist. This is an intensive practical module for the final stage of the Data Science program, after learners have a foundation in Mathematics, Statistics, Programming, Data Mining, and Machine Learning. Content includes: project management, project development process, processing and analyzing real-world data, building and evaluating Machine Learning models, model implementation, teamwork, and presentations. This course is closely related to Advanced Mathematics, Applied Statistics (mathematical and statistical foundations), Python/R Programming (programming tools), Databases (data management and querying), Data Mining, and 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 implementation, and project reporting. | 03 | As specified in the course syllabus |
| 66 | Recommendation Systems | Prerequisites: Introduction to Machine Learning, Linear Algebra, Probability and Statistics, Introduction to ProgrammingHọc phần này cung cấp kiến thức nền tảng và nâng cao về hệ thống khuyến nghị, bao gồm các phương pháp tiếp cận chính như lọc cộng tác (Collaborative Filtering), lọc dựa trên nội dung (Content-Based Filtering), mô hình lai (Hybrid Models) và các kỹ thuật nâng cao sử dụng Machine Learning, Deep Learning. Người học sẽ tìm hiểu cách xây dựng, đánh giá và tối ưu hóa hệ thống khuyến nghị trong thực tế. | 03 | As specified in the course syllabus |
| 67 | Cryptography and Secure Application | Prerequisite: Applied Mathematics for Data ScienceApplied Cryptography and Security course provides a solid foundation in modern cryptographic principles and techniques, as well as their application 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 a crucial foundation for more advanced courses in information security and network security, and is closely related to courses in computer networking and programming. The course content includes basic cryptographic concepts, encryption algorithms, attack and defense methods, and practical applications of cryptography in system and data security. | 03 | As specified in the course syllabus |
| 68 | Advanced Machine Learning | Prerequisites: Introduction to Machine LearningAdvanced Machine Learning course continues to equip students with in-depth knowledge of modern machine learning methods, supplementing the knowledge acquired in the foundational courses. The course focuses on three main topics: probabilistic learning methods (with an emphasis on Bayesian theory and Bayesian networks), synthetic learning methods (including bagging and boosting techniques), and time series data processing. This course plays a crucial role in preparing students to research and apply advanced machine learning techniques to solve complex real-world problems. | 03 | As specified in the course syllabus |
| 69 | Introduction to Computer Network | Prerequisite: Introduction to Operating SystemsIntroduction to Computer Networks course provides students with foundational 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 a crucial role in building a foundation for more advanced courses in network security, network administration, and network applications. Knowledge gained from this course also provides students with a basis for pursuing international certifications such as CCNA. Course content includes layered network architecture, protocols from the physical layer to the application layer, routing, switching, and socket programming. | 03 | As specified in the course syllabus |
| 70 | Distributed Systems | Prerequisite: Introduction to Operating SystemsThe increasing development of information technology such as Wireless Sensor Networks and the Internet of Things has led to the collection of vast amounts of data and information from the environment, as well as interactions between humans and the environment, every day. This enormous amount of data needs to be processed and results delivered within a limited timeframe. Software and applications that process data sequentially become a barrier and cannot meet the ever-increasing demands of users. Distributed systems provide a method of connecting and leveraging computing and storage resources from computers distributed in many different geographical locations to perform computational and data analysis tasks. The Distributed Systems course introduces the basic concepts of distributed systems, and methods for designing and implementing fault-tolerant and scalable systems. | 03 | As specified in the course syllabus |
| 71 | Advanced Business Statistics (Time Series) | Prerequisite: Probability and StatisticsAdvanced Business Statistics (Timeline) course equips students with in-depth statistical analysis knowledge and skills, including time series, regression, probability, hypothesis testing, quality control, decision theory, linear programming, and business forecasting. Students will practice applying these methods to real-world business and management situations. | 03 | As specified in the course syllabus |
| 72 | Social Network Analysis | Prerequisites: NoneSocial Network Analytics course provides foundational knowledge and tools for analyzing the structure and dynamics of social networks. Students will be equipped with the theory and practice of techniques for measuring, modeling, and interpreting social network data, serving applications in social science, management, communication, and big data analytics. | 03 | As specified in the course syllabus |
| 73 | Physical Education 1 | Prerequisites: Nonemodule provides learners with fundamental knowledge of Physical Education, as well as knowledge of formations and drills, and general developmental exercises. Through this, learners will learn how to organize and manage groups and develop the ability to create general developmental exercise routines. | 01* | As specified in the course syllabus |
| 74 | Physical Education 2 | Prerequisite: Physical Education 1course equips students with fundamental knowledge of the history and development of table tennis, as well as basic technical principles. This knowledge enables students to independently organize training sessions on table tennis techniques and develop their general and specialized physical fitness. | 01* | As specified in the course syllabus |
| 75 | Physical Education 3 | Prerequisite: Physical Education 2This course equips students with fundamental knowledge of the history and development of table tennis, as well as basic technical principles. This knowledge enables students to independently organize training sessions on table tennis techniques and develop their general and specialized physical fitness. | 01* | As specified in the course syllabus |
| 76 | National Defense and Security Education | Prerequisites: NoneContent issued together with Circular No. 03/2017/TT-BGDĐT dated January 13, 2017, of the Minister of Education and Training on the promulgation of the national defense and security education program in teacher training secondary schools, teacher training colleges, and higher education institutions. | 08* | As specified in the course syllabus |
| 77 | Intensive English 1 | Prerequisites: NoneThis module aims to improve general English skills to meet the requirements of English learning and communication. Lessons are skill-oriented (Listening, Speaking, Reading, Writing) and are presented through practical topics with images, stories, and video clips. | 02* | As specified in the course syllabus |
| 78 | Intensive English 2 | Prerequisites: General English 1, Advanced English 1This module follows on from English 1, aiming to improve overall English skills to meet the requirements of English learning and communication. Lessons are skill-based (Listening, Speaking, Reading, Writing) and are presented through practical topics with images, stories, and video clips. | 02* | As specified in the course syllabus |
| 79 | Intensive English 3 | Prerequisites: General English 2, Advanced English 2Students will be equipped with the necessary knowledge and skills (Listening and Reading) for the TOEIC exam. Upon completion of this module, students will achieve a TOEIC score of 350-400. | 02* | As specified in the course syllabus |
| 80 | Soft Skills | Prerequisites: NoneThis course equips students with fundamental knowledge and the ability to apply essential life skills such as communication, negotiation, creative thinking, problem-solving, teamwork, planning, and organization. It fosters critical thinking and interaction with society, the community, and groups; enhances job acumen; and maximizes the professional knowledge and skills acquired through training.This module covers the fundamental knowledge of essential skills such as presentation skills, time management and planning skills, teamwork skills, CV design skills, interview skills, impressing recruiters, leadership skills, and negotiation skills to gain an advantage. | 02* | As specified in the course syllabus |
| 81 | Introduction to Entrepreneurship and Innovation | Prerequisites: NoneThis course provides foundational knowledge about entrepreneurship and innovation in the context of the digital economy and globalization. Students will learn about the process of generating startup ideas, developing business models, raising investment capital, building brands, and managing startup businesses. The course also emphasizes creative thinking, problem-solving skills, teamwork, and adaptability in a volatile business environment. In addition, students will have the opportunity to access practical startup models through case studies, group discussions, and networking with entrepreneurs and experts in the startup field. | 02* | As specified in the course syllabus |
| 82 | Ethics and Law in Digital Age | Prerequisites: NoneThis module equips learners with foundational knowledge and practical skills in professional ethics and law within the context of rapid digital transformation and technological development. Learners will gain a thorough understanding of issues related to privacy, data security, AI ethics, digital copyright, cybersecurity law, and the social responsibility of individuals and organizations in the digital environment. Through this, learners will develop critical thinking, a sense of responsibility, and the ability to make decisions that align with ethical and legal standards. | 02* | As specified in the course syllabus |
(*): Not included in the cumulative GPA.
