1. Program Overview

The Data Science program is designed to educate highly qualified professionals with a strong foundation in mathematics, statistics, information technology, and data science. The program equips students with the knowledge and skills required to collect, process, analyze, and extract insights from data, as well as to apply advanced data science techniques to solve real-world problems.

Students develop competencies in critical thinking, problem-solving, teamwork, communication, foreign languages, and professional ethics, enabling them to adapt to rapidly evolving technological environments. The program follows a two-stage educational model, allowing students to earn a Bachelor's degree upon completion of the undergraduate stage or continue to the advanced stage to obtain an Engineer degree in Data Science.

2. Curriculum Structure

The curriculum consists of a total of 151 credits, distributed among four major knowledge blocks:

• General Education: 30 credits (19.9%)
• Fundamental Discipline Knowledge: 36 credits (23.9%)
• Specialized Discipline Knowledge: 55 credits (36.4%)
• Advanced Specialized Knowledge: 30 credits (19.9%)

The program is organized into two stages:

• Stage 1 (Undergraduate Level): Students complete general education, fundamental discipline, and specialized discipline courses to earn a Bachelor's degree.
• Stage 2 (Advanced Professional Level): Students undertake advanced specialized courses and complete an engineering thesis/project to earn an Engineer degree in Data Science.

3. Course List

3.1 General Education Courses

• Philosophy of Marxism–Leninism
• Political Economy of Marxism–Leninism
• Scientific Socialism
• Ho Chi Minh Thought
• History of the Communist Party of Vietnam
• English 1
• English 2
• English 3
• Basic Informatics
• Probability and Statistics for Data Science
• Calculus
• Linear Algebra
• Physical Education 1, 2, 3
• National Defense and Security Education 1, 2, 3, 4

Elective Courses

• Logic
• Innovation and Entrepreneurship
• Scientific Research Methodology

3.2 Fundamental Discipline Courses

• Digital Law
• Numerical Analysis
• Discrete Structures
• Introduction to Programming
• Programming Practice
• Data Structures and Algorithms
• Data Structures and Algorithms Laboratory
• Object-Oriented Programming
• Object-Oriented Programming Laboratory
• Database Systems
• Database Laboratory
• Computer Organization and Architecture
• Computer Networks
• Computer Networks Laboratory
• Data Warehousing
• Data Warehousing Laboratory

Elective Courses

• Dynamic Programming
• Linear Programming
• Algorithm Analysis

3.3 Specialized Discipline Courses

Compulsory Courses

• Introductory Statistical Analysis
• Business Statistics
• Data Mining
• Data Mining Laboratory
• Data Analytics Techniques
• Data Analytics Laboratory
• Artificial Intelligence
• Artificial Intelligence Laboratory
• Big Data
• Big Data Laboratory
• Machine Learning
• Machine Learning Laboratory
• Deep Learning
• Deep Learning Laboratory
• Data Visualization
• Digital Image Processing
• Digital Image Processing Laboratory
• Major Project
• Professional Skills in Industry
• Internship
• Graduation Thesis

Elective Courses

Group A

• Regression Analysis and Applications
• Regression Analysis and Applications Laboratory
• Optimization
• Time Series Analysis
• Time Series Analysis Laboratory
• Game Theory

Group B1

• Natural Language Processing
• Natural Language Processing Laboratory
• Data Encryption and Security
• Data Encryption and Security Laboratory
• Remote Sensing
• Remote Sensing Laboratory

Group B2

• Data Graphics
• .NET Technology
• Java Technology

3.4 Advanced Specialized Courses (Engineer Program)

Compulsory Courses

• Computer Vision and Pattern Recognition
• Ethical Issues in Artificial Intelligence
• Engineer Thesis
• Engineer Capstone Project

Elective Courses

• Advanced Topics in Data Analytics
• Advanced Machine Learning
• Decision Support Systems
• Social Network Analysis
• Blockchain Technology
• IoT Application Development

 4. Admission Information

Applicants must have completed upper secondary education (or an equivalent qualification) and satisfy the admission requirements stipulated by the University. The program also provides credit transfer and course exemption mechanisms for learners who already possess relevant college or university qualifications, in accordance with current regulations.

5. Program Duration and Credit Requirements

The standard duration of the program is four years, comprising a total of 151 credits. The curriculum is structured into two stages, leading to either a Bachelor of Data Science degree or an Engineer in Data Science degree upon successful completion of the corresponding requirements.

6. Career Opportunities

Graduates of the Data Science program can pursue careers in a wide range of positions, including:

• Data Scientist
• Data Analyst
• Data Specialist
• Business Analyst
• Data Engineer
• Database Administrator
• Statistician

Graduates are also well prepared to pursue postgraduate studies and research in Data Science and related fields.

7. Program Learning Outcomes (PLOs)

Learining OutComes:

·  PLO1 (C3): Apply fundamental knowledge of social sciences, natural sciences, law, and information technology to activities in the field of Data Science.

·  PLO2 (C4 – Bachelor; C5 – Engineer):

• Bachelor Level (C4): Analyze and organize disciplinary and specialized knowledge to solve problems in Data Science.
• Engineer Level (C5): Integrate advanced disciplinary knowledge to propose solutions for complex problems in Data Science.

·  PLO3 (P3 – Bachelor; P4 – Engineer):

• Bachelor Level (P3): Demonstrate professional skills and critical thinking abilities to solve problems in Data Science.
• Engineer Level (P4): Demonstrate advanced professional competencies, including analysis, synthesis, evaluation, and innovation for solving Data Science problems.

·  PLO4 (P3): Demonstrate proficiency in learning, research, and knowledge discovery within the field of Data Science.

·  PLO5 (A3): Exhibit professional ethics, social responsibility, political awareness, and engineering consciousness.

·  PLO6 (P3): Demonstrate communication, organizational, and teamwork skills in academic, research, and professional environments.

·  PLO7 (P4): Communicate effectively and exchange information proficiently, including the use of foreign languages in professional contexts.

·  PLO8 (R4): Analyze societal and business contexts, continuously update professional knowledge, and engage in entrepreneurship and lifelong learning to meet industry and societal demands.

·  PLO9 (P3 – Bachelor; P4 – Engineer):

• Bachelor Level (P3): Operate, monitor, evaluate, and improve activities within the field of Data Science.
• Engineer Level (P4): Manage, organize, lead, and continuously improve professional activities in Data Science.

Competency Scale Legend

• C (Cognitive Domain): Knowledge competency based on Bloom’s Taxonomy.
• P (Psychomotor Domain): Professional and practical skills competency.
• A (Affective Domain): Attitudes, ethics, and professional values.
• R (Crawley Proficiency Rating Scale): Professional autonomy, responsibility, and occupational competence.