In the digital era, following advancements made in innovative technologies, data handling is growing at an unprecedented pace. The data-driven world opens tremendous possibilities and opportunities for companies and businesses for all industries as they can make use of the data information to create value for their business. As a disruptive consequence of the digital revolution, data science and analytics have become an emerging and cross-disciplinary field that requires knowledge and skills in many areas such as computer science, statistics, and mathematics. Riding on the research strengths on big data and data science of HKUST faculty across different Schools as well as the success of the MSc Big Data Technology (MSc BDT) program, the Data Science and Analytics Thrust under the Information Hub in HKUST (GZ) will offer the PhD program in Data Science and Analytics in collaboration with schools/departments to advance HKUST’s international visibility in data science and analytics research.
The PhD program in Data Science and Analytics aims to facilitate close integration of statistical analytics, logical reasoning, and computational intelligence in the study of data processing and analytics. The program will provide rigorous research training that prepares students to become knowledgeable researchers who are conversant in applying logic, mathematics, algorithms and computing power in the process of examining and analyzing data in academia or industry so as to derive valuable insights for making better decisions.
The Doctor of Philosophy (PhD) program aims to develop the skills needed for students to identify theoretical research issues related to a practical applications, formulate and undertake research that addresses issues identified, and independently find a data science and analytics related solution. A candidate for a PhD degree is expected to demonstrate mastery of knowledge in the discipline and to synthesize and create new knowledge, making original and substantial scientific contribution to the discipline.
CROSS-DISCIPLINARY FOCUS AREAS
- Data-driven AI & Machine Learning
- Statistical Learning and Modeling
- Industrial and Business Analytics (Operations-Related Data Analytics, Business Intelligence, and Strategy, etc.)
- Sector-Specific Data Analytics (Healthcare, Finance, Insurance, Marketing, Manufacturing, Transportation, etc.)
- Data visualization and Infographics
- AI-driven Data Analytics
- High-Performance Systems for Data Analytics