Master of Science in Data Science

Data science emerged as a result of the proliferation of vast data sets in various fields, leading to the need for automated methods to facilitate efficient and effective data analysis by humans. The Master of Science in Data Science (MSDS) program equips individuals with the necessary expertise to collect, manage, analyze, and visualize data, enabling them to make informed and ethical decisions driven by data. It draws upon the convergence of computer science, statistics, and domain-specific knowledge to address real-world challenges and find practical solutions.

Graduates of the program can explore different roles in the field of data science, such as:

    • Data Engineer
    • Data Scientist
    • Machine Learning Engineer
    • Business Intelligence Developer
    • Data Steward
    • Data Storyteller

Program of Study

The Data Science graduate program has six (6) core courses and students are able to customize their learning experience through four (4) electives. The courses are scheduled across three (3) terms for full-time students and five (5) terms for part-time students. In addition to the academic units, the program also requires passing the comprehensive examination and publication of the research output.

The core courses include: 

  • Principles of Data Science
  • Data Visualization
  • Data Governance, Ethics and Privacy
  • Machine Learning for Data Science
  • Big Data and Scalable Computing
  • Research Methods

The program allows students to choose from different tracks, namely: 

  • Big Data Analysis track
  • Applied Machine Learning track
  • Business Analytics and Business Intelligence track

Admission Requirements

To be eligible for consideration to the MSDS program, one must hold a Bachelor’s degree from an accredited institution, which includes a minimum of four years of full-time study. Admission to the program that is different from the individual’s undergraduate program of study may require completion of prerequisite courses before commencing graduate-level coursework. Due to the technical nature of the program, students are expected to have a strong technical background, typically with an undergraduate degree in STEM.

Degree Requirements

The MSDS degree is awarded upon fulfillment of the following requirements:

  • completion of all academic courses
  • pass the oral comprehensive examination
  • submission of a master’s thesis based on an independent, original research
  • successful defense of the master’s thesis
  • publication in a reputable refereed international scientific journal or from an ISI/Scopus-indexed CS conference
  • fulfillment of residency and other University requirements

Course Details

  • Principles of Data Science (DATA100). This is an introductory course designed to provide students with the basic concepts of data analysis and statistical computing to explore interesting issues and problems. The course is designed for entry-level students from any major, specifically for students who have not previously taken any statistics or computer science courses.
  • Data Visualization (DATA101). This course explores the design and creation of data visualizations based on available data and tasks to be achieved. This process includes basic data modeling, processing, mapping data attributes to graphical attributes, and strategic visual encoding based on known properties of visual perception as well as the tasks(s) at hand. Students will also learn to evaluate the effectiveness of visualization designs, and think critically about each design decision, such as choice of color and visual encoding.
  • Data Mining and Statistics (DATA102). This course studies algorithms and computational paradigms that allow computers to find patterns and regularities in databases, and generally improve their performance through interaction with data. This course includes data selection, cleaning, and using different statistical techniques. The course will cover all these issues and will illustrate the whole process with examples. Data mining mostly handles tabular data because of its roots in knowledge discovery in databases, but is not limited to it.
  • Introduction to Machine Learning (DATA103). Machine learning is the automatic induction of new information from large amounts of data to make predictions or decisions without human intervention. This course introduces the students to a broad cross-section of models and algorithms for machine learning, and equips them with skills to discover new information from volumes of data. Data mining and machine learning have overlapping algorithms and methods, but they focus on different things: data mining focuses on finding patterns while machine learning focuses on predictive models.