Course Description
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 Multiple Criteria Decision Making. 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.
Learning Outcomes
Upon completion of this course, students will be able:
- to choose a systematic methodology of the decision making for complex decisions
- to consider the proper technique of decision making depending on the nature of the problem, the available data and the existing technology
- to produce the proper model that will describe the decision making problem
- to consider complex combinatorial optimization problems
- to apply tools of the new technology to make better decisions and producing the right strategies
- to evaluate with a systematic manner the impacts of alternative decisions and strategies
Course Description
- Descriptive Statistics
- Sampling Distribution
- Confidence Intervals
- Hypothesis Testing for one Population
- Hypothesis Testing for two Populations
- Chi-Squared Tests
- Simple Linear Regression
- Multiple Linear Regression
- Analysis of Variance
- Time Series Analysis
Learning Outcomes
Upon completion of this course, students will be able:
- Distinguish between the different types of data (numerical, ordinal, categorical)
- Understanding of the various sampling strategies
- Visual representation of numerical data (histograms, frequency polygons, bar charts)
- Recognizing the shape of the distribution based on the visual representation of data
- Determination of the measures of central tendency for numerical data
- Understanding the connection between frequency and probability
- Familiarity with the basic probability definitions and rules
- Understanding the probability distribution of basic random variables
- Understanding the concept of the sampling distribution and its relation to the population parameters
- Definition of confidence intervals based on sample data
- Grasping the t-student distribution
- Determination of the sample size for defining confidence intervals of certain length
- Understanding the idea of Hypothesis testing
- Distinguish between the errors that may be done when performing a hypothesis test
- Performing independence tests when frequency values are available for different levels of categorical values
- Understanding of the simple linear regression model
- Recognizing the meaning of the slope and intercept
- Distinguish between explained and unexplained variance
- Realizing the requirement for multiple independent variables
- Ability to develop multiple linear regression models
- Ability to develop multiple regression models using pseudo-variables for different levels of categorical variables
- Understanding the concept of variance analysis
- Comprehending that by analyzing variance, conclusions on the means can be reached
- Distinguishing between the basic building blocks of time series
- Ability to smooth time series data
- Estimating future values of time series
Course Description
Organizational behavior addresses the human side of organizations—what people need and desire at work, how they use their time, talent, and energy for collective ends, and how they can work together effectively for a greater good. Leadership guides and influences others to engage in these collective endeavors.
The course therefore offers: 1) learn evidence-based knowledge from the field of organizational behavior in order to identify and apply best organizational practices for leading teams and organizations, and 2) develop the interpersonal skills required to lead diverse groups and organizations effectively.
Learning Outcomes
Upon completion of this course, students will be able to:
- Utilize organizational behavior theories, frameworks, principles, and tactics to prevent OB problems from emerging and, when problems are identified, intervene to fix them.
- Evaluate the benefits and challenges of alternatives to achieve high performance at the individual, team and organizational levels.
- Develop greater confidence and dexterity with developing and writing different types of assignments.
- Create a plan to improve your own personal leadership skills and to manage their career.
Course Description
- 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.
Learning Outcomes
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.
Course Description
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 – functional programming and streams – development of internet applications.
Learning Outcomes
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