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 to become a qualified expert in the principles and practices of data science.


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 the data science “The Sexiest Job of the 21st Century.”


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 of IELTS academic writing band 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


Semester Course Title Type Credits
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
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