PHD IN DATA SCIENCE AND ANALYTICS (DSA)
Required Courses
- Data Mining and Knowledge Discovery in Data Science
Sample Elective Courses
- Advanced Database Management for Data Science
- Deep Learning in Data Science
- Foundation of Data Science and Analytics
- Data Science Computing
- Advanced Machine Learning
- Data Analysis and Privacy Protection in Blockchain
- Industrial Analytics
- Data Exploration and Visualization
- Experimental Design and Causal Inference
- Functional Data Analysis
- Spatio-Temporal Data Analysis
- Combination Optimization with Machine Learning
Program Learning Outcomes
-
01Identify scientific and engineering correlations, significances, and insights in new data science and analytics models, algorithms, tools, principles, frameworks, solutions, and techniques.
-
02Demonstrate critical thinking and analytical skills from the perspective of data science and analytics.
-
03Apply a range of qualitative and quantitative research methods for data science and analytics.
-
04Translate and transform fundamental research insights effectively into data science practice in academic fields and industries.
-
05Exercise independent thinking and demonstrate effective communication skills in presenting and publishing scientific findings.
-
06Conduct original research independently and competently showing in-depth knowledge in the field of data science and analytics.
PHD
General Information
-
Credit structure:
PhD: 21 credits
-
Normative Program Duration:
PhD:
Full-time: 3 years (those with a relevant research master’s degree), 4 years (those without a relevant research master’s degree)
