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B.Sc. in Computational Data Science (CDASN)

The data-driven era creates strong interests and needs of analyzing, storing, distributing, and sharing massive amounts of data using sophisticated data analytics and machine learning algorithms and methodologies, with applications in multiple disciplines including science, social science, finance, public health, medicine, engineering, and telecommunications. We have already witnessed a huge job demand of data analysts in both local and global employment markets. However, how to design proper data-driven solutions for analyzing and reasoning about massive information remains a non-trivial challenge, since it requires the in-depth knowledge of both computing and statistical methodologies for problem solving, data collection, data modeling and analysis, and scientific experimental design.

Our programme, through the synergy of the strengths of the Department of Computer Science and Engineering and the Department of Statistics, focuses on the in-depth academic trainings in the domain of computational data science. It aims to equip students with the capabilities of applying both (1) high-performance parallel and distributed computing for massive data management and (2) data-driven theories and methodologies for mining patterns and making predictions from large and complex datasets, backed by rigorous foundations of data structures and algorithms, statistical modeling and analysis, as well as parallel and distributed computing system programming. Such capabilities enable students to develop cutting-edge massive data analytics and management solutions that are of practical interest to academics, industry, and society.

In particular, our programme features a solid inter-disciplinary curriculum that includes cohesive efforts across the departments in the Faculty of Engineering and the Faculty of Science; it can be viewed as a “Computer Science/Statistics + X” programme. Its core curriculum blends the fundamentals of computer science and statistics from the courses offered by the Department of Computer Science and Engineering and the Department of Statistics. In addition, it offers several specializations (i.e., the X component) that apply the core knowledge of computational data science to different science, engineering, and medicine disciplines, including (a) Computational Data Science, (b) Computational Physics, (c) Computational Medicine, and (d) Computational Social Science.

This programme is offered by the Department of Computer Science and Engineering and Department of Statistics. 

More programme information could be found here.

The programme’s mission is to equip students with the following capabilities:

  • reasoning about and inferring knowledge of massive information;
  • mastering hardware/software primitives, including data structures and algorithms, statistical modeling and analysis, as well as parallel and distributed computing system programming, for building data analytics and management applications;
  • designing and implementing data analytics and management applications that can analyze, store, distribute, and share massive data at scale;
  • applying computational data science methodologies in various disciplines such as science, social science, finance, public health, medicine, engineering, and telecommunications;
  • developing research skillsets for making cutting-edge innovations in computational data science;
  • developing effective technical communication and project management skillsets in collaborative projects related to computational data science;
  • considering reliability, safety, privacy, and security issues of data analytics and management applications; and
  • understanding the ethical and societal impacts of data analytics and management in human life.

Our graduates will have acquired the ability to

  1. apply knowledge of computer science and statistics appropriate to the degree discipline in computational data science; (K/S)
  2. design and conduct scientific experiments, as well as to analyze and interpret massive data; (K/S)
  3. design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health, safety, manufacturability, and sustainability; (K/S/V)
  4. function on multi-disciplinary teams; (S/V)
  5. identify, formulate, and solve computational data science problems; (K/S)
  6. understand professional and ethical responsibility; (K/V)
  7. communicate effectively; (S/V)
  8. understand the impact of data analytics and management solutions in a global and societal context, especially the importance of health, safety, and environmental considerations to both workers and the general public; (K/V)
  9. recognize the need for, and engage in, life-long learning; (V)
  10. use the techniques, skills, and modern computing and statistical tools necessary for engineering and science practice appropriate to the computational data science discipline; and (K/S)
  11. use the computing and statistical tools relevant to the computational data science discipline along with an understanding of their processes and limitations. (K/S)

K = Knowledge outcomes 
S = Skills outcomes 
V = Values and attitude outcomes

Every student is assigned an academic advisor who meets with the students at least once a year for purposes of general supervision such as course selection, guided study, adaptation to University learning modes and disciplinary fundamentals, etc.  Students with academic problems or on academic probation / extended probation are required to have a monthly meeting with the academic advisor.