Ngành Tuyển sinh

Data Science

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1. General Objectives

The undergraduate program in Data Science at the University of Economics – Technology for Industries is designed with an application-oriented approach to equip students with political integrity, ethical qualities, and good health; a strong foundational and in-depth knowledge of mathematics, statistics, and information technology; and the ability to analyze and process data to solve real-world problems related to economics, finance, engineering, and other data-driven fields. The program aims to train and supply high-quality data science human resources that meet the demands of the domestic and international labor markets. Graduates will have clear career orientation as well as the ability for self-learning, innovation, communication skills, entrepreneurial thinking, and adherence to professional responsibility and ethics.

2. Specific Objectives

PO1: Apply foundational knowledge of mathematics, statistics, computer science, social sciences, politics, law, and disciplinary reasoning to solve problems in the field of data science in enterprises, organizations, and institutions.

PO2: Demonstrate personal skills and qualities, clear career orientation, systems thinking, innovative and entrepreneurial thinking to adapt to the Industrial Revolution 4.0 and international integration.

PO3: Demonstrate teamwork skills, independent working ability; effective communication; self-learning capability; participation in lifelong learning opportunities; and continuous updating of emerging trends and technologies in the field of data science.

PO4: Develop the ability to analyze and solve practical problems using data science through the application of statistical models and artificial intelligence.

3. Career Opportunities for Graduates

Graduates of the Data Science program have many career opportunities. Some specific positions include:

1. Business Data Analyst:

  • Analyze sales, marketing, financial data, etc. to develop effective business strategies;

  • Build models to forecast revenue, profit, costs, etc.;

  • Report data analysis results to management.

2. Data Scientist:

  • Apply machine learning and artificial intelligence algorithms to solve business problems;

  • Develop machine learning models to predict customer behavior, optimize processes, etc.;

  • Research and apply new technologies in data science.

3. Data Science Consultant:

  • Advise enterprises on the application of data science;

  • Develop data science strategies and solutions for organizations;

  • Train and coach staff in data science.

4. Data Science Research Specialist:

  • Research new methods and algorithms in data science;

  • Develop new data science tools and applications;

  • Participate in scientific research projects in data science.

5. Data Science Lecturer:

  • Teach data science courses at universities and colleges;

  • Develop data science curricula and training programs;

  • Conduct scientific research in data science.

6. Healthcare Data Analyst:

  • Analyze healthcare data to diagnose diseases, predict health risks, etc.;

  • Develop machine learning models to support doctors in diagnosis and treatment;

  • Conduct scientific research in healthcare data science.

7. Financial Data Analyst:

  • Analyze financial data to forecast stock markets, assess risks, etc.;

  • Develop machine learning models to support investment decision-making;

  • Conduct scientific research in financial data science.

8. Marketing Data Analyst:

  • Analyze marketing data to understand customer behavior and campaign effectiveness;

  • Develop machine learning models to optimize marketing campaigns;

  • Conduct scientific research in marketing data science.

9. Cybersecurity Data Analyst:

  • Analyze cybersecurity data to detect and prevent cyberattacks;

  • Develop machine learning models to predict cybersecurity threats;

  • Conduct scientific research in cybersecurity data science.

4. Opportunities for Further Study and Professional Development

Graduates of the Data Science program have many opportunities for further study and professional development after graduation. Some common pathways include:

1. Participating in specialized courses: Many training centers and universities offer advanced courses in data science, helping graduates enhance knowledge and skills in specific areas such as machine learning, artificial intelligence, and big data analytics.

2. Attending professional seminars and conferences: Opportunities to learn from industry experts, stay updated on the latest data science trends, and expand professional networks.

3. Self-learning: With the development of the internet, graduates can easily access online learning resources, including free and paid courses, blogs, e-books, and instructional videos on data science.

4. Participating in real-world projects: Applying learned knowledge and skills through personal projects, group projects, or enterprise-based projects.

5. Working in practical environments: Graduates may work in various sectors such as banking, securities, e-commerce, healthcare, telecommunications, manufacturing, energy, logistics, tourism, education, etc. Practical work environments help enhance problem-solving, teamwork, and communication skills.

6. In addition, graduates may further enhance their qualifications by: Publishing scientific papers; participating in scientific competitions; teaching; and conducting scientific research.