MSc in Data Science with Specialization in Analytics in Teaching and Learning

Collaborate with FED

All courses are compulsory:

EDUC7098 Project Report

6 credits

Project Report is designed to encourage students in this program to participate in various projects and practices at different levels relevant to the use of big data and cultivate their abilities in collecting, managing, and processing big data in related areas of education independently. The Project Report should include the purpose, process and outcomes of participation in the project.

Choose 4 required elective courses from the following:

EDUC7041 Assessment and Evaluation of Educational Big Data

3 credits

This course is designed to introduce graduate students to the application of big data in educational context, including the epistemological underpinnings of data science, in-depth knowledge of data science theories in education, and the methodological nuts-and-bolts in conducting educational evaluation.

EDUC7042 Data-driven Approach to Educational Administration

3 credits

This course is to introduce prospective data scientists to data management in educational administration. Emphasis is placed on the use and repurposing of data to enhance governance and efficiency of school education as well as to formulate or update relevant policies and administrative measures emerged in response to changing social development or enactment of laws or rules.

EDUC7043 Learning Enhancement with Big Data

3 credits

This course is designed to improve instruction using data. In the digital age, a wealth of data is available for teaching and learning purposes. This course aims to broaden students with the initiatives undertaken to make use of data-driven approaches that can improve the learning process. Students are expected to make use of tools to mine a wide range of learning patterns and behaviors so as to enhance the quality of instruction. They will study, experience and review the theory and practice of existing applications of big data in order to make informed judgment about their educational duties.

EDUC7044 Quantitative Social Science Research with Big Data

3 credits

This course is designed to be part of the emerging field of quantitative/computational social sciences. The goal is to equip students with data science approach to answer social science questions. This course will introduce principles and skills of quantitative social science research. Students will receive an update of the major tools and ideas used in the field and be guided toward their first data-driven research project throughout this course.

SSGC7201 Civic Data Acquisition and Analysis

3 credits

To promote studies using computational methods in social sciences, this course focues on machine learning techniques and their applications in social sociences. By introducing principal ideas in statistical learning, the course will help students to understand the conceptual underpinnings of methods in data mining, how classical concepts of research design in the social sciences can be implemented in new data sources, and how these new data sources might require social scientists to update their thinking on research design. The course focuses more on the usage of existing software packages (mainly in R) than developing the algorithms by the students. Students will be required to work on projects to practice applying the existing software.

Fundamental Courses:

CISC7201 Introduction to Data Science Programming

CISC7204 Data Science and Data Visualization

CISC7203 Database and Data Mining Technologies

CISC7202 Tools for Machine Learning