Required Courses

  • Introduction to Data Science and Analytics
  • Introduction to Artificial Intelligence
  • Machine Learning
  • Introduction to Data Mining
  • Deep Learning
  • Database Management Systems
  • Design and Analysis of Algorithms
  • Data Science Project
  • Discrete Mathematics
  • Mathematics for Data Science
  • English Communication I for Information Hub Programs
  • English Communication II for Information Hub Programs
  • Final Year Capstone Project

Sample Elective Courses

  • Introduction to Data Science and Analytics
  • Advanced Probability and Statistics
  • Machine Learning
  • Computer Architecture and Systems
  • Advanced Programming Languages
  • Discrete Mathematics
  • Mathematics for Data Science
  • Introduction to Artificial Intelligence
  • Cloud Computing and Big Data Systems
  • Introduction to High-Performance and Parallel Computing
  • Advanced Algorithms
  • Introduction to Natural Language Processing and Knowledge
  • Data Science for Computer Vision and Multimedia
  • Introduction for Reinforcement Learning
  • Data Visualization
  • Data Privacy and Security
  • Data Science Ethnics
  • Bayesian Models and Applications
  • Theories in Computing
  • Advance Theories in Computing
  • Theories in Data Science
  • Deep Learning for Science
  • Introduction to Optimization
  • Statistical Inference
  • Introduction to Data Mining
  • Advance Machine Learning and Deep Learning
  • Machine Learning Systems
  • Data Science for Cross-disciplinary Applications
  • Special Topics in Data Science
  • Data Management for Data Science
  • Complex Data Management
  • Data Science for Battery Technologies

Program Learning Outcomes

  • 01
    Identify scientific and engineering correlations, significances, and insights in new data science and analytics models, algorithms, tools, principles, frameworks, solutions, and techniques.
  • 02
    Demonstrate critical thinking and analytical skills from the perspective of data science and analytics.
  • 03
    Apply a range of qualitative and quantitative research methods for data science and analytics.
  • 04
    Translate and transform fundamental research insights effectively into data science practice in academic fields and industries.
  • 05
    Exercise independent thinking and demonstrate effective communication skills in presenting and publishing scientific findings.
  • 06
    Conduct original research independently and competently showing in-depth knowledge in the field of data science and analytics.

General Information

  • Credit structure:

    PhD: 21 credits

  • Normative Program Duration:


    Full-time: 3 years (those with a relevant research master’s degree), 4 years (those without a relevant research master’s degree)