Program Description
Data Science encompasses the generation of insights and value from raw data and is the
core of digital businesses across all sectors. It’s a field that requires a diverse mix of
capabilities and skills—and never gets boring. Data informs key decisions, leads to
optimisation of existing processes, and is the enabler of entirely new business models via
data insights and automation. When you take a Master’s degree in data science you join the
data revolution that is leading major changes in businesses, economies, and societies today.
Our Master of Science in Data Science Engineering aims to provide a programme of study
that combines data science, machine learning, statistics and mathematics. The programme
uses a rigorous approach, has a mathematical focus and involves applying data science to
the social sciences.
Program Objectives
Apply quantitative modeling and data analysis techniques to the solution of real world business problems, communicate findings, and effectively present results using data visualization techniques.
Recognize and analyze ethical issues in business related to intellectual property, data security, integrity, and privacy.
Apply ethical practices in everyday business activities and make well-reasoned ethical business and data management decisions.
Demonstrate knowledge of statistical data analysis techniques utilized in business decision making.
Apply principles of Data Science to the analysis of business problems.
Use data mining software to solve real-world problems.
Program Highlights
Degree Awarded
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Master of Science in Data Science
Commencement of Session
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January 2023 (then 4 times a year; Oct, Jan, Apr or Jul)
Study Model
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Online Studies / E-learning
Approval
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To be approved by the Ministry of Education and Research, Estonia.
Duration
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24 Months
Credits
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120 ECTS/EAP ( European Credit Transfer and Accumulation System)
Program Learning Outcomes
Design and execute research methodologies and communicate strategies and
outcomes to broader audiences of both technical and non-technical nature.
Conduct complex ICT projects in their area of expertise in accordance with ethical and
professional standards, prepare and present professional posters, lectures, and
publications of the research findings in conference and journal settings.
Demonstrate comprehensive knowledge of advanced data science and ICT
technology concepts by successfully completing advanced courses and seminars.
Identify authoritative sources and conduct relevant search and discovery of topical
literature sources and tools to be used in the design and development of solutions to
complex problems in their domain of discourse.
Identify and apply appropriate methodologies in successful execution of complex
software projects and implement analytical, statistical and/or numerical solutions of
data-driven or theoretical question related to IT-related problems.
Program Curriculum
Provides basic knowledge and skills required for understanding
and managing IT and/or Data Science specific subjects
Contains courses focused on IT essentials and important components
of IT – networks, programming, Python Language, databases, web applications, etc.
Contains courses focused on Data Science, IT systems
administration and development, Data Quality and Data Wrangling, Model
Engineering, Time Series Analysis, Explorative Data Analysis and Visualization, Time
Series Analysis, Neural Nets and Deep Learning and Big Data Technologies and also
a corresponding internship.
Students can freely choose any courses offered by the institute without any restrictions.
classical thesis (formulation of a practical problem with
corresponding analysis and solution) related to Data Science or to an area, where
Data Science plays an important role.
Mandatory Core Courses
PMDS0501 - Fundamentals of Data Science.
PMDS0502 - Probability & Statistics For Data Science
PMDS0507 - Database Management Systems
PMDS0551 - Process & Project Management
PMDS0591 - Research Methods.
PMDS0504 - Data Mining & Decision Support.
PMDS0545 - Big Data Analytics.
PMDS0504 - Research Webinar.
PMDS0693 - Thesis Proposal
Thesis project
PMDS0552 - Data Driven Innovation.
PMDS0694 - Thesis Presentation & Defence.
Elective Courses (Any 5)
PMDS0540 - Statistical Learning (Recommended Elective) PMDS0504 - Innovation and Entrepreneurship (Recommended Elective)
PMDS0512 - Information Theory
PMDS0515 - Modeling and Simulation for Computer Science
PMDS0547 - Algorithmic Trading
PMDS0581 - Acquisition and Analysis of Biomedical Data
PMDS0594 - Deep Learning
PMDS0615 - Role of Python in Data Science
PMDS0616 - Cloud Computing
Program Structure
| Semester 1 | ||||
|---|---|---|---|---|
| S. N. | Course | Course Name | EAP | Hours/Week |
| 1 | PMDS0501 | Fundamentals of Data Science | 6 | 6 |
| 2 | PMDS0502 | Probability & Statistics For Data Science | 6 | 6 |
| 3 | PMDS0507 | Database Management Systems | 6 | 6 |
| 4 | PMDS0551 | Process & Project Management | 6 | 6 |
| 5 | PMDS0591 | Research Methods | 6 | 6 |
| Total For The Semester | 30 | 30 | ||
| Semester 2 | ||||
|---|---|---|---|---|
| S. N. | Course | Course Name | EAP | Hours/Week |
| 1 | PMDS0504 | Data Mining & Decision Support | 6 | 6 |
| 2 | PMDS0545 | Big Data Analytics | 6 | 6 |
| 3 | PMDS0592 | Research Webinars | 6 | 6 |
| 4 | PMDSXXXX | Elective 1 Innovation & Entrepreneurship (Recommended) | 6 | 6 |
| 5 | PMDSXXXX | Elective 2 | 6 | 6 |
| Total For The Semester | 30 | 30 | ||
| Semester 3 | ||||
|---|---|---|---|---|
| S. N. | Course | Course Name | EAP | Hours/Week |
| 1 | PMDS0693 | Thesis Proposal | 6 | 6 |
| 2 | PMDS0552 | Data Driven Innovation | 6 | 6 |
| 3 | PMDSXXXX | Elective 1 | 6 | 6 |
| 4 | PMDSXXXX | Elective 2 | 6 | 6 |
| 5 | PMDSXXXX | Elective 3 | 6 | 6 |
| Total For The Semester | 30 | 30 | ||
| Semester 4 | ||||
|---|---|---|---|---|
| S. N. | Course | Course Name | EAP | Hours/Week |
| 1 | PMDS0694 | Thesis Presentation & Defence | 30 | 30 |
| Total For The Semester | 30 | 30 | ||