Professional Master in Data Science (PMDS)
The program is designed for working professionals with a background in ICT who would like to extend their skill set to encompass data science and become qualified experts in the principles and practices of data science.
Background and Rationale
Data science is concerned with the extraction of useful knowledge from data sets. It is closely related to the fields of computer science, mathematics, and statistics. It is a relatively new term for a broad set of skills spanning the more established fields of machine learning, data mining, databases, and visualization, along with their applications in various fields. In 2012, Harvard Business Review called data science “The Sexiest Job of the 21st Century.”
Eligibility
The common core is designed so that any graduate of a 4-year Bachelor of Science or Bachelor of Engineering program can succeed in the program.
The mathematical background for the common core is undergraduate linear algebra and multivariate calculus (matrices, vectors, and partial derivatives) and basic probability theory (probability mass functions, probability density functions, sampling, and common distributions). The Mathematical Foundations of Data Science course provides a review of this background material and reinforcement of the particular mathematical techniques used in data science.
The assumed IT background is basic IT literacy and the ability to write basic programs in any high-level programming language. Applicants with no or very little programming experience should take an online course in Python programming, for example on Coursera, before beginning the PMDS.
Applicants with related work experience are preferable. In addition, applicants should be employed with a company or other organization that can utilize the skills obtained from the program. We expect students to apply the skills learned in PMDS to a problem faced by the company or organization in their industrial project.
Applicants should have a good command of English with an English proficiency score in the IELTS academic writing band of at least 5.0.
| Semester | Period | Course |
| First semester | August-Mid December 2021 | Core and elective courses |
| Second semester | January-Mid May 2022 | Core and elective course |
| Third semester | June-July 2022 | Individual project with industry |
Course
| 1st Semester (August-November) | Computer Programming for Data Science | Required | 3 |
| 1st Semester (August-November) | Data Modeling and Data Management | Required | 3 |
| 1st Semester (August-November) | Fundamentals of Machine Learning | Required | 3 |
| 1st Semester (August-November) | Mathematical for Data Science | Required | 3 |
| 2nd Semester (January-Mid May) | Data Analytics for Business Intelligence | Required | 3 |
| 2nd Semester (January-Mid May) | Data Driven Computer Vision | Required | 3 |
| 2nd Semester (January-Mid May) | Deep Learning | Required | 3 |
| 2nd Semester (January-Mid May) | Human-Computer Interaction | Required | 3 |
| 3rd Semester (June-July) | Industrial Project | Required | 6 |
