Full-Time Program

    The full-time program consists of one year of coursework, followed by a semester-long thesis or capstone project.

    Fall Quarter
    COURSE ECTS
    Information Systems and Business Process Management

    Upon completion of the course the students will be able to:

    - Understand and apply concepts of Information Systems Analysis Design and Management in the context of an Enterprise (Enterprise Architecture)

    - Understand how business processes connect human resources, information systems and technologies and enterprise strategy

    - Apply techniques of business process analysis and modelling (Enterprise Architecture modelling) to extract requirements and to formulate specifications for business support through digital technologies

    - Understand and apply techniques for the definition of Key Performance Indicators (KPIs) in the context of Business Process Management

    - Understand and apply Business Analytics technologies for the management of KPIs

    - Understand and apply the Archimate modelling language to define business and technology enterprise architecture.

    		 5 ECTS
    Large Scale Optimization

    - Understand the relation between Prescriptive Analytics and Combinatorial Optimization

    - Differentiate between solution shape and solution objective

    - Familiarize with three main types of Combinatorial Optimization problems

    - Understand the insufficiency of using mathematical programming methods for dealing with large-scale combinatorial optimization problems

    - Use a modern programming language to develop algorithms for dealing with optimization problems

    - Describe and apply local search-based optimization methodologies

    - Incorporate efficient guiding mechanisms into local search optimization frameworks

    		 5 ECTS
    Fundamentals in Data Management

    Upon completion of the course, students will be able to:

    • Model the data of a business or organization using entity-relationship diagrams or the relational model
    • Write simple or complex SQL queries to manage the schema of a relational database, insert/update data in a relational database, or retrieve data in various ways
    • Connect to a relational database using a programming language and retrieve data
    • Design the relational schema of a data warehouse using the star or snowflake methodology
    • Understand modern data management systems (NoSQL) and the modeling approaches they offer, specifically key-value stores, document stores, and graph databases
    • Use a data stream management system (stream analytics)
    		5 ECTS
    Statistics for Business Analytics I

    Primary aim of this course is the understanding and the application of statistical methods in real life business problems. Emphasis is given in the implementation of all methods using R and in problem solving. Interesting real-life datasets and problems are analyzed during this course with aim to provoke their attention and motivate them. Finally, the students are introduced to the basic principles of scientific report writing and storytelling by writing an assignment accompanied with a written scientific report.

    		5 ECTS

    Winter Quarter
    COURSE ECTS
    Business Intelligence & Data Engineering

    Upon completion of the course, students will be able to:

    • Understand the value of implementing a data warehouse in business decision-making
    • Comprehend the fundamental principles of data integration and the challenges it presents
    • Design the relational schema of a data warehouse using the star or snowflake methodology
    • Create data cubes based on a star/snowflake schema
    • Use a commercial or open-source relational database system to implement all of the above

    Gain in-depth understanding of each phase of the ETL (Extract, Transform, Load) process and become familiar with the tools, systems, and programming languages used to implement each phase.

    		 5 ECTS
    Data Visualization

    After completion of the course, the student will be able to:

    • Understand how data visualization works, in terms of human visual perception and cognition
    • Understand good and bad practices when plotting data
    • Learn about practical data visualization, including methods to plot various types of data, interaction techniques, and the grammar of graphics concept
    • Create data visualizations using R
    • Build a Tableau application
    		 2.5ECTS
    Statistics for Business Analytics II

    After completion of the course, the student will be able to:

    • Fit and understand regression models and their extensions
    • Understand the classification problem and apply a wide range of methods, comparing them and determining suitability
    • Understand the clustering problem and apply several methods, with diagnostics to evaluate their success
    • Use R for the models taught
    		 5 ECTS
    Requirements Engineering for Analytics

    This course equips students with the principles, methods, and tools needed to translate business needs into clear and applicable requirements for analytics projects.

    Upon completion of the course, students will be able to:

    1. Understand the multidimensional nature of requirements engineering (business/technical, functional/non-functional, data/models and algorithms, extraction/modeling/documentation/confirmation)
    2. Manage the project of defining requirements for business analytics applications, including roles, activities, and deliverables
    3. Apply methods and techniques for the collection, modelling, documentation, and communication of requirements in analytics projects
    4. Use software tools that support requirements engineering tasks
    		 2.5ECTS
    Python for Analytics & Artificial Intelligence

    The aim of the course is to provide a broad coverage of Machine Learning and Artificial Intelligence through an applied approach using Python. Students will engage with all stages related to Business Analytics, Machine Learning, and AI, from data cleaning and processing to advanced neural network architectures and issues of ethics, bias, discrimination, impartiality, and fairness. The course also provides a comprehensive understanding of basic Machine Learning techniques, enabling students to select the most appropriate tool for each task.

    		 5 ECTS

    Spring Quarter
    COURSE ECTS
    AI for Business Analytics

    The course aims to equip students with the appropriate skills and the necessary knowledge and abilities to apply AI techniques, practices and tools in a range of applications. Specifically, upon successful completion of the course, the student will:

    • Have a solid understanding of the capabilities and challenges of AI and be prepared to participate and get involved in the development of cutting-edge AI technologies,
    • Be able to design, develop, fine-tune and adapt AI tools in various fields and applications,
    • Has gained programming experience in cutting-edge technologies such as TensorFlow/Pytorch,
    • Has mastered good practices in big data integration into AI applications,
    • Be capable of designing and synthesizing solutions that integrate AI Agentic workflows,
    • Has gained hands-on experience by collaborating and co-working with fellow students to solve a real-world problem and present an integrated solution.
    		5 ECTS
    Advanced Topics in Data Analysis

    Upon completion of the course, students will be able to:

    • Model different types of entities and relationships as nodes and edges and represent this information as relational data.
    • Design and perform analysis calculations on time series, networks, and other complex structures
    • Use advanced network analysis software to create visualizations and perform empirical studies on network data.
    • Use Neo4j to store and process network data. Know and use data mining techniques on big data.
    • Understand the advantages and disadvantages of different data representations (e.g., as points, vectors, sets, graphs) in data modeling and analysis.
    • Select appropriate data mining techniques for emerging big data applications.
    • Understand the basic concepts of time series. Work with time series data or network data and apply them on a wide range of problems.
    		 2,5 ECTS
    Data Governance and Privacy

    Upon completion of this course, students will be able to:

    • identify the key regulatory, legal and ethical issues related to fundamental rights and freedoms with focus on privacy and (personal) data protection and understand the adequacy and relevance of the existing law and the regulatory frameworks in privacy and data protection in the digital environments and especially with regard to Artificial Intelligence
    • identify security threats and risks for personal data, get familiar with security requirements and technical measures and achieve a relative technical background that may support activities/ jobs like this of DPO
    • understand and measure the intersection of different domains and use this approach while designing their technology and/or business project
    • understand and integrate their studies and professional background into a general social, economic and institutional context.
    		 2,5 ECTS
    Innovation and Entrepreneurship

    a. Understand the skills, mindset, and drive necessary to be a successful entrepreneur.

    b. Identify personal strengths and weaknesses in terms of entrepreneurial competences.

    c. Understand the lean startup methodology and entrepreneurial process through a hands-on approach focusing on the business analytics and technology space.

    d. Develop initial concept, sales pitch, business model and mock-up of an innovative business venture to be used for business validation

    g. Identify the drivers and barriers behind a successful business venture and the power of the team.

    		 2,5 ECTS
    Business Analytics Use Cases

    Upon completion of the course, students will be able to:

    - Understand the end-to-end process in analytics applications (business goals, data collection, data integration, analysis, interpretation, delivery),

    - Understand the requirements and types of analysis in analytics applications in different domains (e.g. healthcare, banking, finance, energy, insurance, etc.)

    - Design architectures for analytics applications

    		 5 ECTS
    Cloud Infrastructure for Analytics

    By the end of the course, students will be able to:

    • Understand and compare the data analytics services offered by Azure, AWS, and GCP
    • Design and implement data pipelines using Azure Data Factory and Databricks
    • Use Azure Synapse and Databricks for large-scale data processing and analytics
    • Perform basic analytics using AWS (Redshift, Glue, Athena) and GCP (BigQuery, Dataflow, Dataproc)
    • Evaluate trade-offs between platforms in terms of scalability, integration, and pricing
    • Navigate security, governance, and compliance aspects in cloud-based analytics environments
    		2,5 ECTS

    Fall Quarter (2nd year)
    COURSE ΠΙΣΤΩΤΙΚΕΣ ΜΟΝΑΔΕΣ
    Thesis or Field Study Project or Internship

    Upon successful completion of the thesis, the student will have studied in depth a specific topic from the scientific areas of the master, will have utilized the relevant knowledge acquired during his studies at the master, will have developed the synthetic and analytical ability, will have learned to look for the appropriate scientific information from the relevant scientific literature, will have acquired skill in writing a scientific text and in presenting the topic of the work.

    The dissertation or field study project or internship is compulsory and applies to full and part-time students upon completion of the course, i.e. in the semester from 1 August to 31 January of the following year. The students of the program may choose (a) a field study project instead of a dissertation, with few hours of weekly meetings of the student in the company, or (b) an internship of at least 3 months and up to 40 working hours per week in a company-provider, to solve real-life problems related to the subject of the dissertation. The above options shall be equally important and equal Credits as for the dissertation shall be awarded, as specified in the studies regulation.

    During the Internship/Field Study Project, students:

    - Combine theoretical training with professional experience.

    - Develop and highlight practical skills.

    - Acquire familiarity with the work environment and its requirements, and knowledge of the rules of work ethics and behavior.

    - Are facilitated in making decisions about their professional orientation.

    - Can use the knowledge they acquired during their internship in the context of their thesis.

    - Acquire a form of work experience that they can refer to in the future.

    		 30 ECTS