Εξάμηνο: 7th semester

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.

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 presents baselines on digital economy. With the completion of the course, the student will be in position:

• to understand and become familiar with the key concepts and principles of applications of e-business.
• to know the main characteristics of the e-business applications both in terms of development and in terms of provisioning of such applications.
• to be able to implement e-business applications, by applying the knowledge obtained from laboratory exercises in different application contexts / domains.

Course Contents

• e-Business introduction.
• e-Commerce presentation.
• Baselines on e-business micro-economy theory.
• Business requirements analysis for the design of e-commerce.
• Methodology for the design of successful web pages. Blogs.
• e-Stores, methodology for the design of e-stores.
• Design evaluation and faults detection through the use of web statistics applications.
• eGovernment, ebanking; ehealth; business-to-business applications.

Moreover, the EVDOXOS system is utilized to provide additional useful information to the students as well as exercises that respond to the corresponding thematic topics / sessions covered by the course.

Recommended Readings

• McGarvey R. & Campanelli M. (2005): Start Your own E-Business, Entrepreneur Press.
• Chaffey D. (2008): E-Business and E-Commerce Management, 3rd Edition, Prentice Hall.

This course offered by the Department of Business Administration of University of Piraeus.

Learning Outcomes

Upon successful completion of the course, the student will be able:

• to describe in a methodical way a course lesson and how to orchestrate activities
• to know how to use graphical educational design tools to create educational scenarios
• to know the basic principles of science, technology, engineering, and mathematics (STEM)
• to create STEM worksheets
• to understand and successfully implement the quality assessment principles of training scenarios
• to design STEM educational activities with Educational Robotics and Internet of Things
• to develop Robotics and IoT applications using Scratch & Lego Mindstorms
• to implement applications using platforms like Lego EV3 (Robotics), BBC Microbit and Raspberry Pi (IOT).

 

Course Contents

• Methodology for creating learning scenarios by orchestrating educational activities
• Design worksheets through examples
• Introduction to the Science, Technology, Engineering and Mathematics Teaching Approaches – STEM (science, technology, engineering, and mathematics)
• Presentation of pedagogical principles through which STEM training activities are planned
• Analysis of educational robotics activities with the tools MIT Scratch, Lego Mindstorms, Makecode
• Explaining how to develop educational activities with ARDUINO & RASSBERY Pi
• Development of STEM educational activity as lab praxticals

Recommended Readings

• Alimisis D., Moro M., Menegatti E. (eds) (2017) Educational Robotics in the Makers Era. Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 560). Springer, Cham
• Julie Dirksen (2015). Design for How People Learn (2nd Edition) (Voices That Matter), New Riders.

Associated scientific Journals

• Nicolai Pöhner and Martin Hennecke (2018). The Teacher’s Role in Educational Robotics Competitions. In Proceedings of the 18th Koli Calling International Conference on Computing Education Research (Koli Calling ’18). ACM, New York, NY, USA, Article 34, 2 pages. DOI: https://doi.org/10.1145/3279720.3279753
• Mayerove, K. and Veselovska, M. (2017): “How to Teach with LEGO WeDo at Primary School”. In: Merdan, M. et al. (eds.): Proceedings of the 7th International Conference on Robotics in Education (RiE 2016, Vienna). Vienna: Springer International Publishing. pp. 55 — 62.
• Sullivan, F. and Heffernan, J. (2016): “Robotics Construction Kits as Computational Manipulatives for the Learning in STEM Disciplines”. In: Schrum, L. (ed.): Journal of Reserach on Technology in Education. Volume 48. Issue 2. London: Routledge. pp. 105 — 128.

Learning Outcomes

The main objective of the course is to introduce the fundamental concepts of digital systems project management and to study best practices in the area of project management such as the Project Management Body of Knowledge (PMBOK) of Project Management Institute (PMI), and to use such practices in project management of digital systems. The course will incorporate a laboratory session with project management software tools that allow students to practice some of the principles addressed.

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

• Recognize the need for IT project management
• Recognize the key issues during the IT project management procedures
• Describe the best practices in IT project management processes and follow an IT project management methodology –from project inception to project closure
• Create work break down structures (WBS)
• Create project plans
• Create business cases
• Describe PMI project management process groups
• Use various methods and techniques for schedule and budget estimation
• Use various methods and techniques for project monitoring
• Use various methods and techniques for resource loading and leveling
• Assign tasks and resources using project management software tools
• Create a Gantt/PERT schedule using project management software tools
• Monitor project progress using project management software tools

Course Content

1. Introduction to project management (e.g. project definition, projects typology, triple constraint concept, a systems approach to project management, organizational influences).
2. Projects, information systems and services life cycles. IT project management methodologies (e.g. phases, deliverables, PMI project management procedures).
3. IT projects business cases (e.g. Measurable Organizational Value, feasibility study, risk analysis, cost-benefit analysis, financial and scoring models).
4. IT project management portfolios (e.g. project selection using Balanced Scorecard).
5. Project charters and project plans. PMI project management processes (PMBOK areas).
6. Project Time and Recourse Management (e.g. Work Breakdown Structure, Project organization structure and responsibilities, Gantt charts, the critical path, network diagramming, PDM networks, CPM/PERT, Scheduling with resource constraints).
7. Project estimation (e.g. Delphi technique, Time boxing). Software engineering metrics and approaches (e.g. Lines of Codes, Function point analysis, COCOMO).
8. Project control. Cost control (e.g. variance analysis, earned value). Performance analysis (e.g. Performances indices SPI and CPI). Forecasting (e.g. Forecasted cost to complete project, forecasted cost at completion).

Suggested Bibliography

• J. Marchewka (2016): Information Technology Project Management: Providing Measurable Organizational Value, Wiley.
• Schwalbe K (2013): Information Technology Project Management, Cengage Learning.
• Phillips J (2010): IT Project Management: On Track From Start to Finish, McGraw-Hill Education.
• B. Maizlish and R. Handler (2010): IT Portfolio Management Step-by-Step: Unlocking the Business Value of Technology, Wiley.
• Project Management Institute (2004): A Guide to the Project Management Body of Knowledge, Third Edition (PMBOK Guides), Project Management Institute.
• Nicholas J (2004): Project Management for Business and Engineering: Principles and Practice, Butterworth-Heinemann.
• International Journal of Information Technology Project Management (IJITPM), IGI Global.
• International Journal of Project Management, Elsevier.

This course offered by Department of Business Administration of University of Piraeus.

Learning Outcomes

This course is designed to promote a fundamental understanding of the theoretical and applied knowledge related to instructional theories and models (principles, methods, strategies) for the design, development, implementation and evaluation of Technology Enhanced Learning Environments (TELE). The purpose of this course is to introduce students to the orchestrated elements of instruction.

At completion of the course, the students will be able:

• to understand the fundamental principles of instructional theories and models
• to analyze, design, develop and evaluate different types of instructional methods & strategies for an effective teaching.
• to demonstrate a knowledge of the ‘what’, ‘how’, and ‘when’, in the instructional design process in different disciplines.
• to integrate ICT educational technologies into activities for K-16 and business settings.
• to create an educational plan/scenarios for reflective thinking into educational practice.
• to create the appropriate orchestrated instructional methods and strategies for an effective teaching.
• to articulate a personal set of values, motivation, attitudes and a vision for the future classroom.

Course Contents

• Introduction to the concepts: Education – Learning – Instruction – Training.
• Principles of the learning processing in different Teaching Models (behavioral, cognitive and social constructivist principles & theories in specific instructional methods).
• Personal and psychological factors in learning and instruction (individual differences, self-efficacy beliefs, motivations, needs, attitudes, learning styles, cognitive learning styles on TELE).
• Models of teaching and instructional design (Gagne’s Nine Events of Instruction, Bruner, Pr/jBL, Kirkpatrick model).
• Taxonomies and learning objectives (Bloom’s Taxonomy).
• Formative, summative and authentic assessment.
• ICT in educational settings (synchronous and asynchronous learning, web 2.0 environments, authoring tools, AHLE, LMS, CMS, smart education, e-portfolios, educational games, gamification).
• ICT applications (lesson plans, educational scenarios, macro/micro scenarios).
• School and professional environment (interpersonal relationships, communication, ethics).

Recommended Readings

Joyce, B. R.; Weil, M.; Calhoun, E. (2015). Models of Teaching, 9th Edition, Pearson

Dell’Olio, J., & Donk, T. (2007). Models of teaching: Connecting student learning with standards. Thousand Oaks, CA: Sage Publications.

Joyce, B. R.; Weil, M.; Calhoun, E. (2015). Models of Teaching, 9th Edition, Pearson

Elliott S, Kratochwill T, Littlefield-Cook J, Travers J. (2008).Educational Psychology: Effective Teaching, Effective Learning,Brown & Benchmark Pub.

Reggie, K., Fox, R., Chan F. T. , Tsang P. (2008). Enhancing Learning Through Technology: Research on Emerging Technologies and Pedagogies. World Scientific

Roblyer M.D. (2009). Integrating Educational Technology into Teaching, 5^thed., Allyn & Bacon.

Sawyer, R. K. (2006). The Cambridge Handbook of the Learning Sciences, Cambridge University Press.

Slavin R., E. (2008). Educational Psychology: Theory and Practice, 9^thed.,Allyn & Bacon.

Snowman, J. Biehler, R. (2008). Psychology Applied to Teaching, 12^th Edition, Hougton & Mifflin.

Woolfolk, A. (2010). Educational Psychology,11^thed., Allyn & Bacon.