Part-Time Program

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.

The aim of the course is to provide a broad coverage of the field of Machine Learning and Artificial Intelligence, through an applied approach using the Python programming language. Students will engage with all stages related to Business Analytics, Machine Learning, and Artificial Intelligence, from data cleaning and processing to advanced neural network architectures and issues of ethics, bias, discrimination, impartiality, and fairness. At the same time, the broad coverage aims to give students a comprehensive understanding of the subject, including basic Machine Learning techniques, so that they can select the most appropriate tool for each task, depending on the specific requirements.

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 being able to understand whether it is suitable for the problem or not.

- Understand the clustering problem and apply several methods, together with diagnostics to understand the success of them

- Use R for the models taught.

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.

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

• understand and integrate their studies and professional  background into a general social, economic and institutional context.  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.

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.

- 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

After completion of the course the student will be able

•       understand how data visualization works, in terms of human visual perception and cognition

•       to understand about good and bad practices when plotting data

•       learn about practical data visualization, including methods to plot various types of data, interaction techniques, the grammar of graphics concept etc.

•       create data visualizations using R 

•       To build a  Tableau Application

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
• 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
• 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

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

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.