Master of Science in Data Science (Analytics in Teaching and Learning)

Master’s Degree & Postgraduate Certificate/Diploma Programmes – UM GRS

https://grs.um.edu.mo/index.php/prospective-students/master-postgraduate-certificate-diploma-programmes/

Applicants must meet the requirements:

1. Hold a recognised bachelor’s degree or equivalent.
2. GPA 2.8 (75 out of 100) or above in the Bachelor’s degree studies.
3. Preference is given to those who majored in education and possess a quantitative background (statistics, STEM, etc). Students with prior research experience, including research publication, assistantship, scholarship and grants, will be evaluated favorably.
4. English proficiency should be in line with that stipulated in the UM Admission Guidelines Governing Master’s Degree and Postgraduate Certificate/Diploma Programmes.

Please refer to the website from the Institute of Collaborative Innovation (ICI) for details: https://cds.ici.um.edu.mo/programme/study-plan-2020/

Graduation Requirement: Students must complete 30 credits including the passing of the project report.

CISC7201 INTRODUCTION TO DATA SCIENCE PROGRAMMING

This course is designed for students who are new to the world of data science. After the introduction of some basic arithmetic, variables, and data structures in Python, students will start to learn how to collect and extract data from real datasets. Some data analytical skills using the control flows and Python packages (e.g., NumPy, SciPy, Pandas, etc.) will be introduced. To address the needs of big data processing, some distributed computing frameworks (e.g., Spark) and visualization tools with Python will be discussed. Students may apply some basic learning algorithms with Python packages (e.g., scikit-learn) to extract knowledge from data.

Pre-requisite: None

CISC7202 TOOLS FOR MACHINE LEARNING

The course will start from the very beginning of the ML basis. First, the basic concepts such as liner algebra; probability and information theory, and numerical methods will be introduced. Next machine learning overview, inductive learning, and representation learning will be introduced. Basic deep learning processes are designed as artificial neural network; Bayesian Networks and learning; Deep learning and deep neural networks; convolution neural network. Throughout the course, practical methodology of using tools such as Tensorflow or Karas etc. will be be emphasized.

Pre-requisite: Introduction to Data Science Programming

CISC7203 DATABASE AND DATA MINING TECHNOLOGIES

This course is designed to enable students to learn the database and data mining concepts and techniques for big data analytics and development in different domains. The course concentrates on the practical issues of database and data mining for solving big data problems. The content includes data modeling in database and data warehouse, SQL, Python programming for database, Python programming and R programming for data mining applications. Students will learn the skills of database modeling, querying, and programming, as well as the programming techniques for data mining.

Pre-requisite: None

CISC7204 DATA SCIENCE AND DATA VISUALIZATION

This course is designed to enable students to learn the significance of data visualization in data science and big data analytics, and develop knowledge and skills to present quantitative data using data visualization tools. This course emphasizes on the practical aspects of data science with a focus on using R or Python programming language to process data, produce visualizations, and interpret these visualizations. Students will learn the practice of data cleaning, reshaping of data, basic tabulations, aggregations and visual representation in order to increase the understanding of complex data and models.

Pre-requisite: None

EDUC7041 ASSESSMENT AND EVALUATION OF EDUCATIONAL BIG DATA

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.

Pre-requisite: None

EDUC7042 DATA-DRIVEN APPROACH TO EDUCATIONAL ADMINISTRATION

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.

Pre-requisite: None

EDUC7043 LEARNING ENHANCEMENT WITH BIG DATA

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.

Pre-requisite: None

EDUC7044 QUANTITATIVE SOCIAL SCIENCE RESEARCH WITH BIG DATA

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.

Pre-requisite: None

SSGC7201 CIVIC DATA ACQUISITION AND ANALYSIS

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.

Pre-requisite: None

EDUC7098 PROJECT REPORT

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.

Pre-requisite: None

Chinese and English

Tuition Fee Scheme of Postgraduate Programmes

https://grs.um.edu.mo/index.php/current-students/tuition-fee/