Data Science, MSc draft

Program Overview

The Data Science, MSc program equips professionals with comprehensive data science skills, including data analysis, modeling, and application development. Through hands-on practice and collaboration with industry partners, graduates gain proficiency in solving real-world problems and pursuing advanced studies or careers in data science, data analytics, and related fields.

Program Outline

Degree Overview:

• Program Overview: The Data Science, MSc program equips professionals to comprehend data properties, uncover data relationships, transform raw data, analyze data, draw conclusions from data, and model real-world processes.
Graduates gain proficiency in developing and managing data-oriented applications, executing and coordinating R&D tasks, and pursuing PhD studies.
• Program Objectives:
• Comprehend the characteristics of various data types and the structure of complex data sets.
• Unfold the inherent relationships within data.
• Apply essential transformations to raw data for analysis preparation.
• Analyze data and derive meaningful insights.
• Model real-world processes.
• Develop and manage data-oriented applications.
• Conduct and coordinate R&D tasks.
• Pursue further studies in PhD programs.

Outline:

• Content and Structure: The program emphasizes hands-on practice (60%) alongside traditional lectures and seminars (40%).
Course content is designed to provide a comprehensive foundation in data science, covering topics such as:
• Machine learning fundamentals
• Statistical foundations of data science
• Optimization in data science
• Data visualization methods
• Data-oriented programming
• Cloud computing
• Information security
• Data ethics
• Modern deep learning frameworks
• Advanced machine learning
• Advanced reinforcement learning
• Big data technologies
• Advanced robotics
• Self-driving cars
• AI security
• Financial models
• Genetics and big data
• Course Schedule: The program spans four semesters, with each semester focusing on specific topics and skills.
The indicative course schedule is as follows: | Year | Main Subjects | |---|---| | 1 | Machine Learning Fundamentals, Statistical Foundations of Data Science, Optimization in Data Science, Data Visualization Methods, Data-oriented Programming, Cloud Computing, Information Security, Data Ethics | | 2 | Modern Deep Learning Frameworks, Advanced Machine Learning, Advanced Reinforcement Learning, Big Data Technologies, Advanced Robotics, Self-driving Cars, AI Security, Financial Models, Genetics and Big Data |

Assessment:

• Assessment Methods: Student progress is evaluated through a combination of methods, including:
• Exams
• Quizzes
• Projects
• Presentations
• Research papers
• Assessment Criteria: Specific assessment criteria vary depending on the assessment method, but overall, students are evaluated on their:
• Knowledge and understanding of data science concepts and techniques
• Ability to apply data science methods to real-world problems
• Communication and presentation skills
• Critical thinking and problem-solving skills

Teaching:

• Teaching Methods: The program utilizes a range of teaching methods to cater to diverse learning styles, including:
• Lectures
• Seminars
• Workshops
• Guest lectures
• Online learning resources
• Faculty: The program is taught by highly qualified faculty with expertise in data science and related fields.
• Unique Approaches: The program incorporates unique approaches to enhance student learning, such as:
• Hands-on practice through projects and internships
• Collaboration with industry partners to provide real-world experience
• Integration of ethical considerations into data science practices

Careers:

• Potential Career Paths: Graduates of the Data Science, MSc program have a wide range of career opportunities in various industries, including:
• Data scientist
• Data analyst
• Data engineer
• Business analyst
• Machine learning software developer
• Opportunities and Outcomes: Graduates are equipped with the skills and knowledge to pursue careers in a variety of settings, such as:
• Technology companies
• Financial institutions
• Healthcare organizations
• Government agencies
• Research institutions

Tuition fee: 7,500 USD/year Other fees: application fee 150 USD, entrance procedure fee 350 USD