Master of Science in Data Science

Program Overview

This Data Science program equips students with advanced technical skills and a scientific understanding of the field. It focuses on industry-relevant skills, research competency, and ethical considerations. Graduates are prepared for careers as data scientists, machine learning engineers, and other data science-related roles in various industries. The program emphasizes hands-on experience, collaboration with industry partners, and access to state-of-the-art technologies.

Program Outline

Degree Overview

Profile of the Programme

This program equips students with advanced technical skills and a scientific understanding of Data Science. It aims to:

• Provide students with technical and analytical skills for acquiring, managing, analyzing, and extracting knowledge from heterogeneous data sources.
• Develop research competency so students can design their scientific methods and push the boundaries of this emerging field.
• Focus on industry-relevant skills through collaborations with industry instructors.
• Develop full-stack research data scientists who can collect requirements, innovate, design, implement, and critically evaluate data science solutions.
• Enable students to assess and provide solutions for privacy and ethical issues arising from applying data science methods.
• Provide opportunities for students to work on real-world problems with real data and collaborate with industrial partners.
• Gain hands-on experience with state-of-the-art data science technologies like Deep and Reinforcement learning.
• Prepare students for pursuing a PhD in data science or other data science-related fields (e.g., bioinformatics, computational social science, etc.)
• Instill a strong sense of social commitment, global vision, and independent self-learning ability.

Learning Outcomes

Upon completing the program, students will be able to:

• Critically collect requirements, design, implement, and assess the performance of a data science solution.
• Conduct research and develop novel methods for Data Science or other interdisciplinary fields requiring Data Science skills.
• Identify and communicate data privacy and ethics issues as they arise from specific real-world applications and synthesize solutions to alleviate these issues.
• Communicate and collaborate with teams on interdisciplinary problems.
• Design solutions for real-world challenges of data mining (big data, streaming data, heterogeneous data, noisy data, etc.).
• Synthesize reports and presentations for communicating analysis results and debating data-driven decisions.
• Define, compare, and combine recent research developments in data science, machine learning, and artificial intelligence and invent novel potential applications with social and business value.

Outline

Program Structure:

• Section A: Major Requirements (50 ECTS): Covers foundational topics in data science, including research seminars and methodology, mathematics for data science, data programming, managing and visualizing data, machine learning, and a data science project.
• Section B: Electives (40 ECTS): Allows students to specialize in specific areas of interest, with choices like Deep and Reinforcement Learning, Social and Web Data Mining, Big Data Management and Processing, Artificial Intelligence, Business Intelligence, Data Privacy and Ethics, Data Science in Bioinformatics and Medicine, and Thesis.

Semester Breakdown:

• Semester 1: Data Programming, Mathematics for Data Science, Data Privacy and Ethics.
• Semester 2: Machine Learning, Managing and Visualizing Data, Research Seminars and Methodology, Project in Data Science.
• Semester 3 (Non-Thesis Option): Deep and Reinforcement Learning, Big Data Management and Processing, Artificial Intelligence.
• Semester 3 (Thesis Option): Thesis (30 ECTS).

Course Descriptions:

• COMP-542DL Data Programming: Introduces fundamental programming skills for data science.
• COMP-540DL Mathematics for Data Science: Covers mathematical concepts crucial for data science, such as linear algebra, probability, and statistics.
• COMP-543DL Managing and Visualizing Data: Explores data management techniques and visualization tools for data exploration and analysis.
• COMP-544DL Machine Learning: Provides a comprehensive understanding of machine learning algorithms and techniques.
• COMP-592DL Project in Data Science: Allows students to apply their acquired skills to a real-world data science project.
• COMP-546DL Deep and Reinforcement Learning: Delves into advanced topics like deep learning and reinforcement learning techniques.
• COMP-547DL Social and Web Data Mining: Explores data mining techniques applicable to social and web data.
• COMP-548DL Big Data Management and Processing: Focuses on managing and processing large-scale datasets efficiently.
• COMP-549DL Artificial Intelligence: Examines the principles and approaches of artificial intelligence.
• COMP-551DL Business Intelligence: Applies data science techniques to business intelligence applications.
• COMP-552DL Data Privacy and Ethics: Discusses critical data privacy and ethical issues in data science.
• COMP-553DL Data Science in Bioinformatics and Medicine: Explores applications of data science in bioinformatics and medicine.
• COMP-593DL Thesis: Provides opportunities for students to pursue original research in their chosen area of data science.

Assessment

Course assessment typically combines a final exam and continuous assessment through interactive activities, projects, forum participation, and assignments. The final grade for each course is calculated based on the weight of these components and the actual numerical marks obtained. Students need to maintain a minimum cumulative grade point average (CPA) of 2.0 to graduate and achieve a minimum grade of 'C' to meet individual course requirements.

Teaching

The program features diverse teaching methods, including:

• Interactive lectures: Engaging classroom sessions with faculty actively guiding students through concepts and practical applications.
• Hands-on workshops: Practical sessions for students to apply acquired knowledge and gain valuable practical experience.
• Project-based learning: Collaborative projects challenging students to apply their data science skills to real-world problems.
• Guest lectures: Industry experts providing insights into current data science trends and practical applications.
The program boasts an experienced faculty with expertise in data science, machine learning, and related fields. The faculty includes both resident professors and adjunct faculty from renowned institutions and organizations.

Careers

This program prepares graduates for various career paths in the rapidly growing field of data science, such as:

• Data Scientist
• Machine Learning Engineer
• Artificial Intelligence Specialist
• Business Intelligence Analyst
• Data Analyst
• Big Data Engineer
• Data Architect
• Research Scientist
• Research Engineer
Graduates can work in diverse industries, including:
• Information and Communication Technologies
• Healthcare
• Finance
• Business Intelligence
• Marketing
• Education
• Research and Development
• E-commerce
• Social Media

Other

• The program offers a unique opportunity for students to work directly with real-world data and collaborate with industrial partners.
• It provides access to state-of-the-art data science technologies and software.
• The program emphasizes practical skills and problem-solving abilities, preparing graduates for immediate impact in the job market.