Κατεύθυνση: Secondary Major in Information Systems or Data Management: Optional Courses

Learning Outcomes

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

• to know and understand the key concepts of digital teaching and learning
• to analyse, assess, select and justify pedagogically appropriate e-learning methods and tools for digital teaching and learning innovations.
• to design and create pedagogically grounded online courses.

Course Contents

• Online Teaching and Learning: Theoretical Underpinnings
• Educational Design for Online Teaching and Learning
• An hierarchical Open Access to Online Education framework: Elements (Open Educational Resources, Learning Activities and Lesson Plans, Online Courses, Digital Learning Spaces). Tools and Key Roles (Online Education Instructional Designers, e-Tutors, e-Learning Systems Administrators, Managers)
• Open Educational Resources: Learning Objects, Educational Metadata, Repositories of Learning Objects. Case Studies: the National Repositories of Learning Objects
• Learning Activities and Lesson Plans: Authoring Tools for Learning Activities and Lesson Plans, Repositories of Learning Activities and Lesson Plans. Case Studies: the National Repositories of Learning Activities and Lesson Plans
• Design, Development and Delivery of Online Courses: Methodology for Designing Online Courses. Authoring Tools for Developing Online Courses. Course Management Systems. Case Study: Open edX, MOODLE
• Digital Learning Spaces: 3D Virtual Classrooms and Laboratories

Recommended Readings

• Textbook in Greek (provided for free)
• Additional Open Access Educational Resources available through the course management system

Learning Outcomes

This course introduces students in consulting procedures for the personal and professional development in an IT context. It addresses the needs of students as future workers on ‘how to be involved in a IT workforce community’ by enhancing them to provide emerging professional development opportunities and practices.

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

• • understand the theoretical background of the consulting (in physical and IT context).
  • select and design the appropriate components for their academic and career path (KPIs).
  • critically evaluate a set of skills for professional development.
  • design and build products/services via appropriate components to an institutional IT context and demands (needs, motivations, attitudes, ethics).
  • compose a personal/professional career plan for further development in the society (KPIs).

Course Contents

• Basic consulting theories and practices necessary for the development of effective performance on an academic and professional environment in IT business community (Kirkpatrick model, SRL, SDL).
• Continuing Professional Development programs (CPD).
• Skills and Competencies.
• Communication and Collaboration (active listening, verbal, non-verbal, communication).
• Μentoring and coaching.
• Personal and affective factors in performing (needs, attitudes, motivation, self-esteem etc.
• Organizational factors (ethics, leadership).
• Problem solving, innovation, creativity.
• Evaluation (KPIs).

Recommended Readings

• Robinson D. & Robinson J. (2008): Performance Consulting: A practical Guide for HR and Learning Professionals, Berrett-Koehler Publishers.
• Rosenberg M. (2001): E-Learning Strategies for Delivering Knowledge in the Digital Age, McGraw-Hill.

Learning Outcomes

This course focuses on wide area wireless networks and addresses advanced topics in physical layer design, multi-carrier systems and wireless standards evolution.

At the end of this course, students will have acquired advanced/in depth knowledge in the field of Wireless Communications, with particular emphasis on wireless channel modelling, Multiple Input Multiple Output systems design, and performance evaluation in terms of capacity.

The students will be capable of performing numerical calculations of various wireless parameters, stochastic modelling of wireless transceivers and performance assessment by means of analytical evaluations and simulations, with main focus on baseband processing and radio resources management.

Course Contents

• Advanced physical layer design topics: modulation and coding
• Multiplexing in time, space, frequency, code
• Multiple Input Multiple Output Systems
• Multi-carrier systems: OFDM/OFDMA.
• Radio resource allocation: multi-user communications and scheduling, cross-layer optimization.
• Wireless standards: 3G evolution, IEEE 802.x, 4G and 5G

Recommended Readings

• Behrouz A. Forouzan, “Data Communications and Networking”, Fourth edition, McGraw-Hill, 2007
• W Stallings, Wireless Communciations and Networks, Pearson, 2004.
• D. Tse, P. Viswanath, Fundamentals of Wireless Communciations, 2005.
• T. S. Rappaport, Wireless communications – Principles and practices, Pearson, 2002.
• Harri Holma, Antti Toskala, WCDMA for UMTS: HSPA Evolution and LTE, Wiley, 2010.
• Andrea Goldsmith, Wireless Communications, Cambridge University Press, 2005.

Learning Outcomes

he course presents simulation techniques with particular emphasis on discrete event simulation and applications in computer computational systems and communication networks.

Upon successful completion of the course, the students will be in position to:

• design system models with the required details level that serves better the problem at hand
• develop simulation programs in general purpose programming language (e.g C++) to simulate and evaluate the behavior of simpler systems
• use more sophisticated simulation software for the study and performance evaluation of more complex communication networks and computational systems (e.g network simulator ns3 and CloudSim for cloud computing systems)
• design experiments, collect measurements and interpret and evaluate simulation results

Course Contents

• Introduction to dynamic discrete event systems.
• Development of discrete system models, event-advance design, time-advance design, activity-based design.
• Pseudorandom number generation, random variables generation.
• Overview of simulation languages and platforms.
• Development of simulation programs using general purpose programming languages.
• Measurement techniques, traffic load and experiment design.
• Statistical analysis of simulation experiments, transient and steady state, data collection, confidence intervals, variation reduction techniques.
• Simulation exercises and examples of data networks and cloud computing systems. Theoretical results verification.

Recommended Readings

1. Roumeliotis and Souravlas, “Simulation Techniques”, Epikentro Publications.
2. Kouikoglou and Konstantas, “Simulation of Discrete Event Systems”, Disigma Publications, 2016.
3. Harry Perros, “Computer Simulation Techniques – The Definitive Introduction”, free download from https://people.engr.ncsu.edu/hp/files/simulation.pdf
4. Averill M. Law and W. David Kelton, “Simulation Modeling and Analysis”, McGraw-Hill, Inc.
5. NS manual and tutorials, https://www.nsnam.org/documentation/

Learning Outcomes

The students after the successful completion of the course will be able:

• to model and analyze data with appropriate analysis techniques, assess the quality of input
• to choose the appropriate exploratory and/or inferential method for analyzing data, and interpret the results contextually.
• to use supervised and unsupervised learning techniques for solving many analysis problems such as prediction, classification, segmentation.
• to apply methods for the evaluation of the data analysis results.

Course Contents

• Collection, preparation and representation of data for analysis
• Linear, logistic regression
• Classification Techniques (probabilistic classification, decision trees, support vector machines)
• Predictive analytics and neural networks
• Recommender systems
• Graph analysis (applications on social networks)
• Text mining – sentiment analysis
• Evaluation of data analysis results

Recommended Readings

• Mohammed J. Zaki, Wagner Meira Jr. (2018): Data Mining and Analysis Fundamental Concepts and Algorithms, Cambridge University Press.
• Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman (2014): Mining massive datasets, Cambridge University Press.
• Top of Form.
• Bottom of Form.

Students can choose it only once during undergraduate studies (either the 7th or the 8th semester).

Learning Outcomes

The objective of this course is to present fundamentals concepts regarding Healthcare Information Systems (HIS). HIS are described at both conceptual and technical level and types of HIS are studied thoroughly. In addition, best practices regarding HIS architectural design, development methodologies and interoperability are analysed. Challenges and perspectives of HIS are presented with reference to modern digital technologies of data analytics and artificial intelligence. The course will incorporate a significant laboratory component with various digital tools (mainly open source) that allow student to implement HIS.

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

• Understand the connection between healthcare systems and healthcare information systems
• Define the users of information and decision support based on existing data
• Describe the general functions, objectives and advantages of HIS
• Describe contemporary architectural trends and HIS, in the form of services provided, for supporting important healthcare processes
• Compare various HIS characteristics and choose the most appropriate systems for specific needs and operational frameworks
• Develop HIS, by using open source tools, inventing innovative practices in the fields of medical data architecture and management for their multiple exploitation.

Course Content

1. Healthcare Information Systems: General characteristics. HIS evolution.
2. HIS analysis, design and implementation.
3. Patient-oriented HIS development.
4. Process-oriented healthcare organizations. Healthcare process and data management.
5. Specialized HIS. Contribution to provided healthcare services.
6. HIS architectures, integration and interoperability.
7. HIS security. Standards and security policies.
8. Presentation of well-known commercial HIS of the global market regarding electronic health records.
9. HIS challenges and perspectives. HIS in Greece.
10. HIS development (analysis, design, implementation, testing, operation, maintenance).

Suggested Bibliography

• Karen A. Wager, Frances W. Lee and John P. Glaser (2009): Health Care Information Systems: A Practical Approach for Health Care Management, Jossey-Bass.
• Joseph Tan (2010): Developments in Healthcare Information Systems and Healthcare Informatics: Improving Efficiency and Productivity, IGI Global.
• Charlotte A. Weaver, Marion J. Ball, George R. Kim, Joan M. Kiel, (2015): Healthcare Information Management Systems: Cases, Strategies, and Solutions, Springer.
• Sean P. Murphy, (2015), Healthcare Information Security and Privacy, McGraw-Hill Education.
• Pamela K Oachs, Amy Watters, (2016), Health Information Management: Concepts, Principles, and Practice, American Health Information Management Association.
• International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global
• International Journal of Healthcare Technology and Management, Inderscience
• International Journal of Medical Informatics, Elsevier.

Learning Outcomes

The aim of this course is to support the students in learning the principles, concepts and applications of cryptography.
Upon successful completion of the course the student will be able:

• to handle the basic elements of numerical theory and modular arithmetic
• to manage cryptographic algorithms and their properties
• basic cryptographic functions, such as pseudo-random sequences, one-way hash functions, shift and displacement networks and feistel networks.
• the main features for symmetric and asymmetric cryptography are familiar
• to handle key management systems and digital signatures

Course Contents

• Basic definitions and concepts; information security.
• Symmetric cryptography.
• Digital signatures.
• Authentication.
• Public key cryptography.
• Hash functions.
• Integrity checking.
• Key management and random number generators.

Recommended Readings

• Schneier B. (1996): Applied Cryptography, 2nd Edition, John Wiley & Sons.
• Stallings W. (2006): Cryptography and Network Security, 4th Edition, Prentice Hall.

Learning Outcomes

This course is the basic introductory course in the field of computational analysis and synthesis of social networks.

The course material seeks to introduce the students to the basic concepts and algorithms for the study of social networks. The course focuses on answering questions related to the creation of social networks, their information properties and the interaction between their structure and the emergence of social processes related to information diffusion, strategic interaction and collective behavior. All theoretical results are presented in relation to their application in real problems in social computational environments such as Facebook of Google search.

The successful completion of the course will make students capable of:

• understanding the basic and important features of social networks in both an algorithmic and interaction level.
• knowing the major features of the tools and development methods for the creation of digital social networks and applications

Course Contents

• Conceptual features of social networks
• Elements of Graph Theory
• Social links
• Topics in Social Environments (Homophily, Group participation, Separation)
• Social Balancing
• Information Diffusion
• Elements of Game Theory
• Group Decision-Making
• Sharing frameworks

Recommended Readings

• Martin J. Osborne, An Introduction to Game Theory, Oxford, 2010
• Instructor Notes