7th Semester

This course introduces students to the notion of entrepreneurship while providing them with skills and knowledge regarding the whole cycle of the entrepreneurial process from opportunity identification and assessment to mobilising resources creating the enterprise, managing for growth and ending the new venture. An introduction to the notion of social entrepreneurship and the development of social enterprises is also made in the frame of the course. More specifically the course includes three parts, referring to the:
- Notion and importance of entrepreneurship and its environment
- Entrepreneurial process: Creativity and business idea, business model, business planning, securing resources and agreements, growth strategies and exit strategies.
- Sources of capital and financing in all stages of growth.

Course contents

The course material includes the following thematic areas:
- Introduction to the notion of entrepreneurship
- The eco-system of entrepreneurship
- Innovation and creativity
- Business idea – Business model
- Business plan (I): Development
- Business plan (II): Evaluation
- Software for the development of the financial statements of the business plan
- Foundation of the venture
- Managing and growing the venture
- Exit strategies
- Sources of capital and financing in all stages of growth.
- International entrepreneurship
- Social entrepreneurship

The course deals with the theory, algorithms and applications of discrete (also known as combinatorial) optimization with an emphasis on problems regarding flows, paths and matchings on graphs. More specifically, the course presents algorithms for the problems of shortest path, maximum flow, minimum-cost flow, maximum-cardinality and maximum-weight matchings (mostly regarding bipartite graphs) and, last, stable matchings and b-matchings on bipartite graphs.
Apart from solving such problems using specialized combinatorial algorithms, the students are also expected to formulate applications and real-life problems as flow, path or matching problems on graphs. In addition, this course introduces general methods for discrete optimization problems that can be modeled as Linear Integer Programs, i.e., Branch-and-Bound and Branch-and-Cut.
The purpose of this course is the understanding of algorithmic design specifically for discrete optimization algorithms defined on graphs and integer programming methods. Apart from understanding all related notions, the purpose is to investigate the application of such algorithms (i.e., algorithms for paths, flows and matchings) on real-life problems.

Course contents

The course material includes the following topics:
• Network Flows and Integer Programming
• Shortest-path algorithms: Dijkstra, Bellman-Ford, Floyd-Warshall
• Maximum-flow and minimum-cost flow algorithms
• Matching algorithms in bipartite graphs: maximum-cardinality matching, maximum-weight matching, stable matching and stable b-matching
• Applications modeled as flow problems: project management, job assignment to machines, distinct and restricted representatives, capital allocation, etc.v • Integer Programming: Branch-and-bound methods, Balas' additive algorithm, Branch-and-Cut methods
• Applications of Integer Programming
• Trees: properties, transversal algorithms, minimum-spanning tree algorithms, Steiner trees.

Financial Engineering provides the means of implementing financial innovation through the use of financial instruments like forwards, futures, swaps and options. Usual applications include the restructuring of corporate or investor cash flows in order to achieve tactical and strategic targets, with particular emphasis on risk management. Financial Engineering is at the forefront of innovation and development in financial markets, granting private investors, corporations and institutions almost complete flexibility in transforming existing cashflows into new cashflows with different quantitative and qualitative characteristics. This course aims to provide the tools, methodologies and skills necessary in order to understand, implement and innovate in this very active environment. Real case studies will be presented, demonstrating practical applications of the taught material.

Course contents

The course material includes the following thematic areas:The course material includes the following thematic areas:
- Financial Engineering Mathematics
- Forwards & Futures
- Options
- Swaps, Caps & Collars
- Risk Management

In this course, students learn to appreciate the opportunities and challenges from the use of ICTs, though in-class analysis and discussion of case studies from the international context, so that they can identify and manage similar situations efficiently when encountered in practice.

Students in this advanced course study how information systems in organizations can be managed so that information resources are efficiently used. Four main axes define the learning outcomes of the course:

• The strategic role of IT in contemporary business and strategic planning for information resources and systems
• The business role of IT as a tool for supporting and promoting business functions and management and the managerial skills associated with this role
• The functional structure (department/ services) of IT in contemporary business, its human resources and management
• Broader social aspects related to the use of IT in contemporary business

Course contents

The course material includes the following thematic areas:
1. Information sharing in organization
2. Change management in the development and implementation of information systems (IS) in organizations
3. Information resource management and IT department governance
4. Broader information resource management issues (e.g. privacy) and their societal implications
5. Strategic value and international growth trends for the IT sector

The usage of data in enterprise decision making has been identified as one of the most critical element for success in our data-driven society. The objective of the course is to present the theory and the techniques used in modern data analysis systems in a business context. This includes, architectures, algorithms, tools, applications and commercial systems.

Course contents

- Advanced modern database topics: query processing, transaction processing, main-memory databases, column-oriented databases.
- Business Intelligence: architecture, design and modelling of data warehouses, ETL, data cubes, OLAP, tools, systems.
- Data Mining: Architecture, the KDD process, classification, clustering, association rules, applications, systems.
- Large-scale data management: MapReduce, Hadoop and tools, NoSQL systems.
- Special Topics: Text analytics, data streams, data visualization, social media analytics.

The successful manager nowadays is not the one who possesses the technical or managerial knowledge but the one who has developed appropriately the necessary personal skills to use this knowledge effectively. The current course attempts to “assist” students in developing these competencies, through a program of self-assessment and evaluation with a strong emphasis on interaction with colleagues.
The objectives of the course are the following:
• Students’ self-assessment of their personal skills
• Applications of personal skills at work
• Using personal skills in job search activities and career management

Course contents

• Introduction
• Self-assessment
• Learning and learning styles
• Stress and stress management
• Group dynamics
• Conflict and negotiation
• Influence
• Leadership and Emotional Intelligence development
• Job search and career management

This course offers an overview of the most recent trends in learning and knowledge management in companies and organizations. Students will be introduced to strategies, methods and technologies of organizational learning and knowledge management helping them to develop analytical, development and judgmental skills. Students will be able to relate organizational and technological choices to performance improvements in organizations in the context of changing organizational environments. Practical skill in the implementation of e-learning programs and systems are also emphasized.
The course comprises two units; (1) E-Learning and (2) Knowledge Management. In the first students are introduced to concepts of organizational and workplace learning, training in the context of human resource development, and performance management. Methods and tools for digital instructional design are explained and then applied in practice by students in their course assignments. In the second unit, theoretical models as well as organizational practices concerning the creation, sharing and use of organizational knowledge and intellectual capital are explained. Cases from practice in various companies and organizations will be examined and the latest trends in knowledge management presented and analyzed.

Course contents

Unit 1 E-Learning
• Workplace learning, employee performance and the role of technology: concepts, methods and tools
• E-learning platforms, technologies, and instructional content development tools
• Methods for digital instructional design
• Issues of eLearning Implementation and Management
Unit 2 Knowledge Management
• New Challenges – New Organizational Forms.
• Knowledge Management: Definitions of notions, Measuring intellectual capital, Types and forms of knowledge, Knowledge objects, Knowledge and competitiveness, Overview of tools for knowledge management.
• Knowledge and Innovation.

Στόχος του μαθήματος είναι να εμβαθύνει στην κατανόηση επιλεγμένων κρίσιμων θεμάτων που άπτονται της τομής μεταξύ στρατηγικής και καινοτομίας. Ειδικότερα εστιάζεται σε θέματα που αφορούν στην υλοποίηση των στρατηγικών επιλογών και διαχείριση της στρατηγικής αλλαγής και στην διαχείριση και αξιοποίηση της καινοτομίας. Στοχεύει επίσης στην ενδυνάμωση των δεξιοτήτων των φοιτητών πάνω σε έννοιες, μεθοδολογίες και «εργαλεία» με εφαρμογή στα θέματα που εξετάζονται και σε διαφορετικού τύπου επιχειρηματικά περιβάλλοντα (π.χ. μη-κερδοσκοπικές επιχειρήσεις) αξιοποιώντας μελέτες περίπτωσης.

Σκοπός του μαθήματος είναι η κατανόηση θεμάτων που συσχετίζονται – άμεσα ή/και έμμεσα – με τα Συστήματα Διαχείρισης Επιχειρησιακών Πόρων (Enterprise Resource Planning Systems – ERP).

Το μάθημα έχει θεωρητικό αλλά και πρακτικό προσανατολισμό, στοχεύοντας στην εξοικείωση των φοιτητών με τις συσχετιζόμενες θεωρητικές έννοιες αφενός και αφετέρου με τη χρήση λύσεων λογισμικού (SAP & NAV ERPs) που είναι παγκόσμια διαδεδομένες και υιοθετούνται από εταιρείες και οργανισμούς σε όλους τους οικονομικούς τομείς (industries).

Τα Συστήματα Διαχείρισης Επιχειρησιακών Πόρων (Enterprise Resource Planning Systems – ERP) είναι ένα συμπαγές σύνολο εφαρμογών λογισμικού που υποστηρίζουν ευρύ φάσμα επιχειρησιακών δραστηριοτήτων και λειτουργιών κι ένα επιχειρησιακό εργαλείο ελέγχου, παρακολούθησης και συντονισμού των εργασιών στις κεντρικές και απομακρυσμένες εγκαταστάσεις μιας επιχείρησης. Επιτυγχάνουν τη συγκέντρωση των δεδομένων, την ενοποίηση και ολοκλήρωση όλων των εφαρμογών μίας επιχείρησης και τον επανασχεδιασμό των επιχειρησιακών διαδικασιών, επιδιώκοντας τη βελτιστοποίηση των λειτουργιών, την αύξηση της παραγωγικότητας, και την απόκτηση συγκριτικού πλεονεκτήματος μέσα από τη χρησιμοποίηση νέων τεχνολογιών πληροφορικής. Για τις σύγχρονες επιχειρήσεις στην Κοινωνία της Πληροφορίας, τα ERPs αποτελούν το βασικό πυλώνα της transactional πληροφοριακής υποδομής που επιτρέπει σε εταιρίες και οργανισμούς να ανταποκριθούν στις απαιτήσεις και προκλήσεις της οικονομικής δραστηριότητας στα πλαίσια της παγκοσμιοποίησης.

Τα προσδοκώμενα μαθησιακά αποτελέσματα, μετά την ολοκλήρωση του μαθήματος, είναι η απόκτηση γνώσεων στους παρακάτω βασικούς πυλώνες:

-        Το προϊόν «ERP» και οι υπηρεσίες που προσφέρει υποστηρίζοντας επιχειρησιακές διαδικασίες

-        Παράγοντες και παράμετροι έργων υλοποίησης και εφαρμογής συστημάτων ERP

-        Η εξέλιξη των συστημάτων ERP

-        ERPs, ηλεκτρονικό επιχειρείν, πλατφόρμες και κοινότητες λογισμικού ERP: Σύγχρονες Τάσεις μετάβασης και μετασχηματισμού.

Το περιεχόμενο του μαθήματος περιλαμβάνει τις παρακάτω βασικές θεματικές ενότητες:

-        Το προϊόν και οι υπηρεσίες που προσφέρει. Η εξέλιξη των Συστημάτων Αξιοποίησης Επιχειρησιακών Πόρων (ERP). Τεχνολογική επισκόπηση με έμφαση στις σύγχρονες προσεγγίσεις αρχιτεκτονικής συστημάτων. Απεικόνιση επιχειρησιακών γεγονότων (business events) στις δομές βάσεων δεδομένων. Η λειτουργικότητα που προσφέρεται από τα Συστήματα Αξιοποίησης Επιχειρησιακών Πόρων. Τα συστήματα ERP σαν ολοκληρωμένα πληροφοριακά συστήματα που υποστηρίζουν τις επιχειρησιακές διαδικασίες.

-        Το έργο υλοποίησης και εφαρμογής. Τα συστήματα ERP ως έτοιμο προς λειτουργία (turn-key) έργο. Επιλογή ERP λύσης. Μεθοδολογίες υλοποίησης. Κρίσιμοι παράγοντες επιτυχίας.

-        Η εξέλιξη ενός 'ζωντανού' συστήματος.

-        Η μετάβαση στο Ηλεκτρονικό Επιχειρείν, στις πλατφόρμες και στις κοινότητες λογισμικού ERP. Το ERP σαν νέο κανάλι για τις επιχειρησιακές επικοινωνίες. Επέκταση των συστημάτων ERP για συντονισμό των προμηθευτών και πελατών των επιχειρήσεων. Πλατφόρμες και κοινότητες λογισμικού ERP.

This course deals with the analysis and planning of transport and distribution systems. The course consists of two parts. The first part is focused on the major operational features of transport systems, the relevant institutional environment, and methods for forecasting transport demand. The second part provides operational and tactical planning problems arising in designing and managing freight transport and distribution systems. After completing the course the students will be able to:
- understand the structure, the operations and the broader Political, Economic, Ecological, Societal, and Technological environment of the transportation system,
- develop and apply transport demand forecasting models,
- understand the characteristics of major decision making problems for transportation and distribution systems,
- develop and solve mathematical models for optimizing transportation and distribution decisions,
- understand the role and capabilities of advanced technologies, and will be able to assess the impacts of advanced technologies on the management of freight transportation systems.

Course contents

The course includes the following sections:
• Transportation Systems Analysis: Transportation systems structure, operations, and the political, ecological, economical, social, and technological environment, characteristics of transport demand and supply, performance measures of transportation system and externalities (energy, environment, safety).
• Transport Demand Forecasting: categorization of transport demand forecasting methods, development and application of transport demand forecasting models, cases of transport demand forecasting applications.
• Transportation and Distribution Systems Planning: Introduction to Transport and Distribution Systems planning decisions, classification of mathematical models and algorithms for addressing transport and distribution planning and facility location decisions, case studies addressing real-world transportation, distribution and location problems.
• Telematics and Geographical Information Systems Applications in Freight Transportation problems.

This course examines on one hand the fundamental types of stochastic models employed within Management Science and, on the other hand, the use of simulation techniques in cases where stochastic methods are of limited applicability. In addition, it discusses the application of all the above in real settings of decision support, using simulation software packages.
Stochastic modeling includes mostly Markov processes and Markov chains, while also examining topics in Queuing Theory, Replacement Theory and basic principles of Stochastic Dynamic Programming
Simulation refers mostly to discrete event simulation, while presenting also techniques for model building and validation and analysis of simulation output. Emphasis is given to the construction of simulation models through appropriate software packages, hence part of the course is implemented via lab exercises and tutorials and through a compulsory project which includes all steps of applying simulation on a real-life problem.

Course contents

The course material includes the following topics:
• Markov processes and chains
• Queuing Theory, Replacement Theory, Inventory Theory
• Stochastic dynamic programming
• Simulation as an experimental methods, applications in Management Science, basic simulation techniques
• Discrete event simulation, entities and activities, events and queues, resources activity cycle diagrams
• Simulation languages and packages, the SIMUL8 software
• Transient and steady state, input and output analysis, random-number generators, experimentation principles
• Simulation modeling of real-life applications, case-study discussion.

The Information and Communication Technologies (ICT) effects on the Marketing theoretical models, strategies and practices have created educational needs upon the new knowledge emerging in the topic of Digital Marketing. The scientific research has illustrated the opportunities and capabilities of applying and extending the existing Marketing knowledge in the context of new conditions, requirements and particular characteristics of the Digital environment mainly since the emergence of the Web. Indicatively, the diffusion of alternative communication and shopping channels usage, the introduction of electronic applications with increased customization and personalization capabilities and the penetration of advanced applications for data collection, processing and exploitation have created new research areas and relevant theoretical and practical issues. Finally, while the course adopts an interdisciplinary approach (i.e. Marketing and Information Systems) it does not focus on “technical” issues and, thus, it does not require advanced Information Technology skills from students.

The Investment Analysis course aims in presenting to the student of modern criteria, methodologies and tools necessary for understanding, evaluating, comparing and obtaining optimal investment decisions as appropriate. It offers a balanced and comprehensive picture of investment options such as those presented in practice and organize the thematic units in such a way so as to facilitate their practical application. The course aims to both theoretical training and familiarity with applications, analytical tools and practical problems. Prerequisite is basic knowledge of mathematics, statistics and finance. During the course there will be references to recent case studies of international and Greek investment space.

Course contents

TThe course includes four main thematic units:
I. Introduction to Investments
II. Investment and Portfolio Management
III. Investment Valuation Models
IV. Shares & Fixed-Income Securities Portfolio Management and Valuation.

The first part of the course deals with the analysis and utilization of the huge amount of data (information, products, services, product evaluations, etc.) available to Internet users and businesses operating in this environment with the aim of understanding and predicting human behavior and its exploitation to provide sophisticated and personalized services. The first part of the course aims to introduce the students to the techniques of analytical processing of interactive behavioral data from heterogeneous sources and to familiarize themselves with algorithms of behavior prediction and personalization of information.

The second part of the course aims to link theory to practice in a field that is critical to many of today's businesses: data analysis to better manage the supply chain and optimally respond to consumer needs. This course will emphasize the necessary theoretical background related to these topics, as well as the practical application of the corresponding concepts and models in different types of enterprises and in the context of collaborative practices. A series of case studies will be presented showing the export of knowledge from the data and the business impact from the practical application of this knowledge. It will also cover issues of modern technologies that support data export and analysis as well as efficient supply and demand chain management.

Περιεχόμενα του μαθήματος

Στο πρώτο μέρος του μαθήματος θα καλυφθούν οι ακόλουθες ενότητες:

• Εισαγωγή στην εξατομίκευση της πληροφορίας
• Συμπεριφορικά μοντέλα
• Καταγραφή και μοντελοποίηση διαδραστικής συμπεριφοράς
• Αλγόριθμοι εξατομίκευσης της πληροφορίας
• Αναπαράσταση χρηστών μέσω παραγόντων ανθρώπινης συμπεριφοράς
• Αξιοπιστία δεδομένων
• Σχεδίαση και υλοποίηση προβλεπτικών αλγόριθμων και συστημάτων προτάσεων
• Αξιολόγηση αλγόριθμων πρόβλεψης και παραγωγής προτάσεων

Στο δεύτερο μέρος του μαθήματος θα καλυφθούν οι ακόλουθες ενότητες

• Συνεργασία στην εφοδιαστική αλυσίδα και ανταλλαγή δεδομένων
• Καθορισμός βέλτιστων επιπέδων αποθέματος και safety-stock
• Πρόβλεψη ζήτησης
• Διαχείριση Αποθέματος με ευθύνη Προμηθευτή (Vendor-Managed Inventory)
• Συνεργατικός Σχεδιασμός, Πρόβλεψη και Αναπλήρωση (CPFR)
• Διαχείριση κατηγοριών
• Ανάλυση δεδομένων πωλήσεων (basket analytics)
• Δυναμική τιμολόγηση
• Market segmentation

The aim of the course is to introduce students to social network analysis (SNA) and their instrumental value for businesses and the society. SNA encompasses techniques and methods for analyzing the constant flow of information over offline networks (e.g. networks of workers in labor markets, networks of organizations in product markets etc.) and online networks (e.g. Facebook posts, twitter feeds, google maps check-ins etc.) aiming to identify patterns of information propagation that are of interest to the analyst. The course will help students to understand the opportunities, challenges, and threats arising by the use of social networks as far as businesses and the society at large are concerned. The issues of innovation diffusion and information spread through networks will also be covered. Finally, students will be introduced to the concepts of the wisdom of the crowds and social learning, investigating the conditions under which opinion convergence (asymptotic learning) or herding may occur in social networks.

Topics

- Basic social network concepts (nodes, edges, network visualization);
- Network centrality, clustering and communities, strong and weak ties;
- Information diffusion, contagion and infection rates in social networks, thresholds and giant components, small-world phenomena;
- Aggregate behavior, opinion manipulation, convergence and consensus of beliefs, naïve and Bayesian learning and herding;

Practical applications of SNA will be addressed, although the course does not adopt an exclusively technical/mathematical perspective on subject coverage.

Learning outcomes of the course

Ιn this course the students will acquire the skills required for applying Machine Learning techniques in practice. We cover the whole gamut of abilities and knowledge involved, from obtaining and working to data, to visualisation, interpreting data, different Machine Learning models and applications, up to and including Neural Networks and Deep Learning. The focus of the course is on the practical applied side, and students will work on projects using real-world tools widely used not just in the academia but in industry as well. This is not a computer science programming course, but students are expected to be proficient in the Python programming language, to be inquisitive and enjoy problem solving.