Decision-Making is one of the most important functions of management. Today’s business environment is characterized by high competition, constant changes, extensive globalization, large availability of data and information, and the huge penetration of information and telecommunications technology. In this environment, decision making is increasingly based on the use and analysis of data, through the development of “models”, and the use of user-friendly, PC-based computer packages.
This is what this course is all about. The emphasis of the course will be on understanding and formulating complex problems, as they appear in today’s business environment, developing the appropriate decision models, and using them for effective decision making.
The course introduces the student to the methodology of decision making, as well as to the major models used today. The three major categories of models are covered: Linear and Integer Programming, Decision Analysis, and Simulation. In each unit, the student is exposed to a number of applications, and has the opportunity to apply his/her knowledge to a number of problems and case studies. In addition to developing models, the student is exposed to a number of computer packages, most of them based on Excel, to use in order to solve the problems.
The objectives of this course is to introduce the students to the basic principles of statistical inference and modelling in order to be able to use them in problems of management science. A part of the course will be concerned with an introduction to the essential concepts of statistics so that the students may subsequently be taught and understand regression methodology, which is widely used in economics and management.
- Statistical estimation: Least Squares, maximum likelihood estimation, Bayesian inference
- Hypothesis testing: basic concepts and examples
- Simple linear regression. Applications of the simple linear modelin problems from economics and management science
- Analysis of variance: basic principles and applications via software.
- Multiple linear regression: estimation, multicollinearity issues, applications with statistical software.
The course includes the study of the fundamental knowledge on the description, explanation and management of individual and team behaviors found in organizations. It aims to develop the ability to manage people as individuals and as teams. After the course the students will:
- Understand the personality, the attitudes and behaviors of people in the working environment.
- Obtain a conceptual and theoretical background on leadership
- Be able to motivate their colleagues and partners
- Be able to develop effective teams
- Be able to communicate successfully
- Understand better the corporate culture of their organization
- Introduction to Organisational Behaviour
- Work Attitudes and Behaviours
- Group Dynamics
- Organisational Culture
- Management of Change
- Learning Organisations
Databases began as a simple application in early 70s and grew to one of the most important fields in computer industry, touching hundreds of ΙΤ applications. This outcome was somehow expected, since the focus of database research is the description, storage and usage of data. To describe a database application we need a data model, such as the entity-relationship or the relational model. To retrieve and make use of the stored data, we need a generic query language, such as SQL. Finally, there are numerous ways to store data, depending on how this will be used. The goal of this course is to educate students on how to design properly, build efficiently and use intelligently a database. Furthermore, it should make apparent the various trade-offs that exist in designing, building and using such an application.
- Introduction: Purpose, data models, database languages, users, transactions, architecture.
- Entity-Relationship Model: Entities, relationships, attributes, keys, mapping cardinalities, weak entities, E-R diagrams, mapping to tables, examples.
- Relational Model: Relations, relational schema, relational algebra.
- The SQL Language: Basic structure, nested subqueries, aggregation, views, update, procedural and embedded SQL, triggers.
- Relational Design: Integrity constraints, functional dependencies, decomposition, normalization.
- Storing and Indexing: File organization, indexing, hashing, trees.
- Special Topics: Data warehousing, OLAP, data mining, data streams, OO DBs.
At the end of this course students should be able to:
• have a basic knowledge of the methods and programming techniques used for implementing information systems
• design and build moderately complex applications
• use ready-made libraries and data structures
• reuse design patterns to structure their code
• process complex data structures and sources
• Evaluate alternative technologies and information system evaluation strategies
Development environments and languages; compilers and interpreters; programming with objects; code style; building classes; inheritance; development of large systems: exceptions, assertions, interfaces, abstract patterns, packages; generalizations and threads; data structures: strings, iterators, vectors, stacks, and maps; structuring data with XML; file handling; development of graphical applications; string processing with regular expressions; interfacing with internet applications; handling data in relational databases.