PHD IN DATA SCIENCE AND ANALYTICS (DSA)
Required Courses
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Data Mining and Knowledge Discovery in Data Science
Sample Elective Courses
- Automatic Machine Learning
- Deep Learning in Data Science
- Advanced Database Management for Data Science
- Advanced Machine Learning
- Parallel Programming for Data Science and Analytics
- Foundation of Data Science and Analytics
- Data Science Computing
- Data Analysis and Privacy Protection in Blockchain
- Data Exploration and Visualization
- Spatio-Temporal Data Analysis
- Introduction to Graph Learning
- Special Topics
- Independent Study
- Computer Vision and Its Applications
- Convex and Nonconvex Optimization I
Program Learning Outcomes
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01Identify scientific and engineering correlations, significances, and insights in new data science and analytics models, algorithms, tools, principles, frameworks, solutions, and techniques.
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02Demonstrate critical thinking and analytical skills from the perspective of data science and analytics.
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03Apply a range of qualitative and quantitative research methods for data science and analytics.
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04Translate and transform fundamental research insights effectively into data science practice in academic fields and industries.
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05Exercise independent thinking and demonstrate effective communication skills in presenting and publishing scientific findings.
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06Conduct original research independently and competently showing in-depth knowledge in the field of data science and analytics.
PHD
General Information
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Credit structure:
PhD: 21 credits
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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)
