Full-Time Program

This course introduces the notion of information systems (IS) used in enterprises, explains how technology supports business operations and strategy through the concept of enterprise architecture, and analyses business processes (BPs) as the fundamental element of modern enterprises and the management of their performance. The course provides practical knowledge and skills for business process modelling using the Archimate modelling language. The course also develops skills for the definition of KPIs business and process performance management, based on the Balanced Scorecard method. Students apply the knowledge acquired in an analysis and design project in a real-life organization.

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

- Understand and apply concepts of Information Systems Analysis Design and Management in the context of an Enterprise (Enterprise Architecture)

- Understand how business processes connect human resources, information systems and technologies and enterprise strategy

- Apply techniques of business process analysis and modelling (Enterprise Architecture modelling) to extract requirements and to formulate specifications for business support through digital technologies

- Understand and apply techniques for the definition of Key Performance Indicators (KPIs) in the context of Business Process Management

- Understand and apply Business Analytics technologies for the management of KPIs

- Understand and apply the Archimate modelling language to define business and technology enterprise architecture.

- Understand the relation between Prescriptive Analytics and Combinatorial Optimization

- Differentiate between solution shape and solution objective

- Familiarize with three main types of Combinatorial Optimization problems

- Understand the insufficiency of using mathematical programming methods for dealing with large-scale combinatorial optimization problems

- Use a modern programming language to develop algorithms for dealing with optimization problems

- Describe and apply local search-based optimization methodologies

- Incorporate efficient guiding mechanisms into local search optimization frameworks

This course is designed to introduce students to modern data management concepts, as evolved in the last few years in the context of big data applications.

After taking this course, students should be able to:

- Develop an application in relational systems: design relational schemas, write SQL, use APIs to connect to a relational database within a programming language.

- Distinguish between different data models and use them appropriately. Understand (and apply) concepts such as database federation, integration, data exchange, connectivity, interoperability.

- Compare in-memory and column-oriented vs. traditional query processing.

- Develop data warehousing applications: defining business goals, identifying data sources, using tools/methods to extract and transform data, designing star schemas and cubes and perform multi-dimensional analysis.

- Understand and apply the additional technologies to bring business intelligence to the big data era.

Primary aim of this course is the understanding and the application of statistical methods in real life business problems. Emphasis is given in the implementation of all methods using R and in problem solving. Interesting real-life datasets and problems are analyzed during this course with aim to provoke their attention and motivate them. Finally, the students are introduced to the basic principles of scientific report writing and storytelling by writing an assignment accompanied with a written scientific report.

Upon completion of this course, students will be able to:

- Identify the key regulatory, legal and ethical issues related to privacy and (personal) data protection

- Understand the adequacy and relevance of the existing law and the regulatory frameworks in privacy and data protection and identify possible weaknesses and deficiencies

- To understand and integrate their studies and professional background into a general social, economic and institutional context.

Successful students:

- leverage in-memory databases to deal with high request loads

- can perform exploratory business analytics tasks by applying Unix command-line tools to extract, transform, filter, process, load, and summarize data

- utilize programming models and leverage software systems to parallelize their code over a distributed compute infrastructure

- can store big volumes of (un)structured data to distributed file systems or databases and analyse them using Spark

- can work with data & stream processing workflows

After completion of the course the student will be able to:

- Fit and understand regression models and their extensions.

- Understand the classification problem and apply a wide range of methods, comparing them and being able to understand whether it is suitable for the problem or not.

- Understand the clustering problem and apply several methods, together with diagnostics to understand the success of them

- Use R for the models taught.

First part

- Learn and understand the fundamentals of Data Analysis, Statistical Learning, and Machine learning.

- Apply the fundamentals using production-quality tools in the Python programming language.

Second part

- Learn and understand concepts related to Machine Learning so as students can formulate and solve data mining related business problems with applications in market basket analysis, customer segmentation, campaign management and optimization etc.

- Learn how to use the following software: SAS Visual Analytics, SAS Visual Data Mining and Machine Learning on SAS Viya.

- Understand the skills, mindset, and drive necessary to be a successful entrepreneur.

- Identify personal strengths and weaknesses in terms of entrepreneurial competences.

- Understand the lean startup methodology and entrepreneurial process through a hands-on approach focusing on the business analytics and technology space.

- Develop initial concept, sales pitch, business model and mock-up of an innovative business venture to be used for business validation

- Identify the drivers and barriers behind a successful business venture and the power of the team.

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

- describe and use data mining techniques on complex datasets.

- understand the benefits and shortcomings of different data representations (such as points, vectors, sets, graphs) in data modelling and analysis.

- select appropriate data mining techniques for emerging big data applications.

- apply data mining techniques on datasets of realistic sizes using modern data analysis frameworks.

The aim of the course is to introduce students to Social Network Analysis (SNA) and its instrumental value for businesses and the society. SNA encompasses techniques and methods for analyzing the constant flow of information over online social networks (e.g. Facebook posts, Twitter feeds, Foursquare check-ins) aiming to identify, sometimes even in real-time, patterns of information propagation that are of interest to the analyst.

The course will provide students with an in-depth understanding of the structural properties and behavioral characteristics of social networks, as well as the opportunities, challenges and threats arising by online social networks as far as businesses and the society at large are concerned. It will also introduce students to the social and ethical issues that often arise by mining the publicly available information across online social networks for business purposes and/or other types of analyses.

By the end of the course, the students will be able to:

- Formalize different types of entities and relationships as nodes and edges and represent this information as relational data.

- Design and execute network analytical computations.

- Use advanced network analysis software to generate visualizations and perform empirical investigations of network data.

- Interpret and synthesize the meaning of the results with respect to a question, goal, or task.

- Evaluate several approaches for performing a SNA task, and make justified decisions on which to choose.

- Apply their knowledge on realistic and real datasets.

- Process the collected raw data to highlight the connections among them and decide the appropriate formalization as a graph.

- Investigate the conditions under which various phenomena, like information diffusion, opinion convergence (asymptotic learning) or herding may occur in online social networks.

- Write academic/professional social network analysis reports.

The course provides a pathway for you to gain the knowledge and skills to apply content analytics and deep learning to your work and level up your technical career. Concretely, you will:

- Understand the capabilities and basic concepts of deep learning,

- Apply deep learning methodologies for a wide range of problems and projects,

- Build content analytics workflows using TensorFlow/Pytorch,

- Handle real-world cases and explore strategies to analyse content.

After completion of the course the student will be able to:

- Understand how data visualization works, in terms of human visual perception and cognition

- Understand about good and bad practices when plotting data

- Learn about practical data visualization, including methods to plot various types of data, interaction techniques, the grammar of graphics concept etc.

- Create data visualizations using R

- Build a Qlik Sense Application by designing a star scheme data model and creating various visualizations

- Know the basic functionalities of cloud computing

- Create a fully integrated cloud-based pipeline that produces predictive analytics on the fly.

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

- Understand the end-to-end process in analytics applications (business goals, data collection, data integration, analysis, interpretation, delivery),

- Understand the requirements and types of analysis in analytics applications in different domains (e.g. healthcare, banking, finance, energy, insurance, etc.)

- Design architectures for analytics applications

After completion of the course the student will be able to:

- Understand the basic concepts of time series.

- Work with data that are time series and apply them to a wide range of problems.

- Apply statistical modelling in networks.

- Understand the challenges of big data era in applying statistical models.

- Understand how simulation can help statistical inference.

- Apply R to solve such problems.

Data science activities, such as statistical analysis and machine learning require processing, cleaning, and transformation of the input data before these can be exploited for knowledge extraction. However, data exists in various systems, models, and formats. Systems vary from state-of-the-art (e.g. Hadoop) to legacy (e.g. IBM mainframes and Cobol). Data can be stored in raw files (csv, json, xml), compressed formats (parquet, avro), or various database formats (relational, document, graphs). Finally, data can be structured or unstructured, such as text and images. Topics in this course will cover the full pipeline which is necessary in most data science activities, including data extraction, data transformation, data integration, data virtualization, entity resolution, and proper indexing for data visualization. After completion of this course, the student will be able to:

- Understand the different types of missing data

- Impute missing data choosing appropriate technique

- Understand the impact of missing data in data analysis

- Understand different technologies to support SQL querying on big data

- Understand the concept of data virtualization

- Be familiar with Apache Impala and Apache Drill

- Be aware of the processes of data integration pipeline

- Understand the ETL components of data warehouses and commercial tools used in practice.

- Be aware of common tasks used in data integration of heterogenous sources, like entity resolution.

Upon successful completion of the thesis, the student will have studied in depth a specific topic from the scientific areas of the master, will have utilized the relevant knowledge acquired during his studies at the master, will have developed the synthetic and analytical ability, will have learned to look for the appropriate scientific information from the relevant scientific literature, will have acquired skill in writing a scientific text and in presenting the topic of the work.

The dissertation or field study project or internship is compulsory and applies to full and part-time students upon completion of the course, i.e. in the semester from 1 August to 31 January of the following year. The students of the program may choose (a) a field study project instead of a dissertation, with few hours of weekly meetings of the student in the company, or (b) an internship of at least 3 months and up to 40 working hours per week in a company-provider, to solve real-life problems related to the subject of the dissertation. The above options shall be equally important and equal Credits as for the dissertation shall be awarded, as specified in the studies regulation.

During the Internship/Field Study Project, students:

- Combine theoretical training with professional experience.

- Develop and highlight practical skills.

- Acquire familiarity with the work environment and its requirements, and knowledge of the rules of work ethics and behavior.

- Are facilitated in making decisions about their professional orientation.

- Can use the knowledge they acquired during their internship in the context of their thesis.

- Acquire a form of work experience that they can refer to in the future.