B.Sc. in Computational Data Science (CDASN)

Nowadays, people live in a digital society. Human and automated activities continuously generate data, storing them in digital format. These data come from everywhere including social media, corporate information systems, wearable equipment, etc. 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 for data analysts in both local and global employment markets. Besides, how to design proper data-driven solutions for analyzing and reasoning about massive information remains a non-trivial challenge, since it requires in-depth knowledge of both computing and statistical methodologies for problem solving, data collection, data modeling and analysis, and scientific experimental design.

The Computational Data Science (CDASN) programme (co-organized by the Department of Statistics) is designed to manufacture mathematical, technical and analytical skills to create solutions to lead data-driven decision making. Data scientists build intelligent systems to understand, interpret, manage and derive key knowledge from big data sets. It aims to equip students with the latest in large-scale data processing, computational statistics, machine learning, data mining, and data visualisation, while also developing the skills to effectively communicate data insights to key stakeholders, etc. Such capabilities enable students to develop cutting-edge massive data analytics and management solutions that are of practical interest to academics, industry, and society.

The CDASN curriculum emphasizes on equipping students with the following capabilities:

• Effectively collect, clean, manage and analyse large or complex data sets;
• High-performance parallel and distributed computing for massive data management;
• Data-intensive sciences, such as astronomy, biology and physics;
• Data-driven theories and methodologies for mining patterns;
• Making predictions from large and complex datasets, backed by rigorous foundations of data structures and algorithms, statistical modeling and analysis;
• Parallel and distributed computing system programming

Four optional specialized streams are offered for students:

• Computational Data Science
• Computational Physics
• Computational Medicine
• Computational Social Science

CDAS Programme Introduction and FAQs