Overview

This program integrates a variety of topics to allow students to learn the fundamental facets of big data and how it is used in the real world. Topics include mathematical foundations (convex/non-convex optimization and computational methods), data analytics (from data collection, integration, cleansing, mining, machine learning, to business intelligence), and data processing infrastructures (MapReduce, Hadoop, Apache Spark, SQL).

The courses in this program are offered by renowned faculty members from the Computer Science and Engineering Department and the Mathematics Department at HKUST. HKUST ranks at the 30th in Computer Science and Information Systems and 36th in Mathematics according to 2021 QS World University Rankings by Subject.

Syllabus

Courses under this program:
Course 1: Foundations of Data Analytics

Learn the fundamental techniques for data analytics and to be prepared for learning and applying more advanced big data technologies.

Course 2: Data Mining and Knowledge Discovery

Learn how to discover knowledge in data via data mining.

Course 3: Big Data Computing with Spark

Learn the theory and gain hands-on experience of big data systems, using Spark as the exemplary platform.

Course 4: Mathematical Methods for Data Analysis

Learn mathematical methods for data analysis including mathematical formulations and computational methods. Some well-known machine learning algorithms such as k-means are introduced in the examples.

Course 5: Big Data Technology Capstone Project

The Big Data Technology Capstone Project will allow you to apply the techniques and theory you have gained from the four courses in this MicroMasters program to a medium-scale project.

Courses

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4 weeks, 4-8 hours a week, 4-8 hours a week
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In this capstone course, you will get an opportunity to apply the knowledge and skills that you have gained throughout this MicroMasters program. You can choose to complete any one project from a number of choices, covering topics ranging from data integration, data mining, Spark programming, to data analysis. After finishing the project, you will need to submit a report together with the code, to be reviewed by our TAs.

By completing this capstone project, you will create a showcase project and demonstrate to employers that you are job ready and a worthy candidate in the field of big data.
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8 weeks, 6-10 hours a week, 6-10 hours a week
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Big data systems such as Hadoop and Spark emerge as enabling technologies in managing massive amounts of data across hundreds or even thousands of computing nodes. Meanwhile, cloud computing platforms have made these technologies easily accessible to individuals as well as large enterprises. This course is an online adaptation of the signature course MSBD 5003 Big Data Computing offered to our popular MSc Program in Big Data Technology. In addition to 20+ hours of lecture videos, the course contains 100+ multiple-choice questions and 20 coding questions, aimed at equipping learners with both the theory and practical skills of big data systems, using Spark as the exemplary platform.
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8 weeks, 6-10 hours a week, 6-10 hours a week
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Data mining has recently emerged as a major field of research and applications. Aimed at extracting useful and interesting knowledge from large data repositories such as databases and the Web, data mining integrates techniques from the fields of database, statistics and AI.
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8 weeks, 6-10 hours a week, 6-10 hours a week
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Foundations of Data Analytics: This course will provide fundamental techniques for data analytics, including data collection, data extraction, data integration, data cleansing, and basic machine learning techniques. The learners will learn how to manage and optimize the analytics value chain, including collecting and extracting the suitable values, selecting the right data processing, integrating the data from various resources, with programming tools such as Python. This course will also introduce data security and privacy.
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8 weeks, 6-10 hours a week, 6-10 hours a week
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Mathematics has been playing an important role in data analysis from the very beginning; for example, Fourier analysis is one of the main tools in the analysis of image and signal data. This course is to introduce some mathematical methods for data analysis. It will cover mathematical formulations and computational methods to exploit specific structures contained in the data. Some special machine learning algorithms are introduced in case studies.