Distribution of Curriculum
| Courses | Credits | Percent |
|---|---|---|
| General Courses | 53 | 37.3% |
| Core Courses | 30 | 21.2% |
| Major Courses | 59 | 41.5% |
Program Summary
Learning Objective: Develop a strong foundation in managing, processing, and analyzing big data, encompassing both theoretical and applied aspects of data science within information systems. Enhance problem-solving skills for analyzing, optimizing, and designing information systems through extensive data analysis. Acquire the ability to design, develop, and integrate information systems for technical applications focused on comprehensive data analysis, with a capability to address interdisciplinary issues in engineering, social sciences, politics, and economics. Gain expertise in data science and information systems to thrive in a data-driven world.
Career Opportunities: A Bachelor of Data Science offers many career options, including roles as an analyst, designer, and developer of intelligent data mining software. Undergraduates can find opportunities in businesses, government agencies, research institutes, and leading software development companies like DXC, FPT, IBM, TMA, TPS, and Microsoft. Additionally, they can explore diverse fields related to data analysis, mining, and processing.
Module handbook: link
Curriculum
| Subject | Credits | Subject | Credits |
|---|---|---|---|
| Semester 1 | 23 | Semester 5 | 16 |
| Calculus 1 | 4 | Statistical Methods | 3 |
| Physics 1 | 2 | Critical Thinking | 3 |
| Philosophy Marx -Lenin | 3 | Scalable and Distributed Computing | 4 |
| Introduction to Data Science | 3 | Fundamental Concepts of Data Security | 4 |
| Writing AE1 | 2 | History of Vietnamese Communist Party | 2 | Listening AE1 | 2 |
| Physical Training 1 | 3 | ||
| Fundamentals of Programming | 4 | ||
| Semester 2 | 20 | Semester 6 | 18 |
| Calculus 2 | 4 | Data Mining | 4 |
| Physics 2 | 2 | Analytics for Observational Data | 4 |
| Physical Training 2 | 3 | Artificial Intelligence | 4 |
| Linear Algebra | 3 | Ho Chi Minh’s Thoughts | 2 |
| Writing AE2 | 2 | Elective | 4 |
| Object-Oriented Programming | 4 | Internship | 3 |
| Speaking AE2 | 2 | ||
| Semester 3 | 20 | Semester 7 | 14 |
| Regression Analysis | 4 | Elective | 3 |
| Environmental Science | 3 | Special Study of the Field | 3 |
| Chemistry for Engineers | 3 | Big Data Technology | 4 |
| Data Analysis | 4 | Elective | 4 |
| Marxist – Leninist Political Economy | 2 | ||
| Probability, Statistic & Random Process | 3 | Chemistry Laboratory | 1 |
| Semester 4 | 14 | Semester 8 | 10 |
| Data Science and Data Visualization | 4 | Thesis | 10 |
| Principle of Database Management | 4 | ||
| Scientific Socialism | 2 | ||
| Algorithms and Data Structure | 4 | ||
Elective Courses
The Elective course (3, 1) for all levels of English proficiency:
| Course | Credits |
|---|---|
| Business Process Analysis | 4 |
| Decision Support Systems | 4 |
| Theory of Networks | 4 |
| Experimental Design | 4 |
| Blockchain | 4 |
| Data mining for IoT | 4 |
| IT Project Management | 4 |
| Information System Management | 4 |
| Mobile Cloud Computing | 4 |
| Entrepreneurship | 3 |
| Machine Learning Platforms | 4 |
| Deep Learning | 4 |
| Optimization and applications | 4 |