Program’s Courses

Fundamentals in Data Management

This course provides a thorough presentation in the principles, technologies, and practices essential for working with modern data systems. Introduction to data management and data analysis, emphasizing the role of data in decision-making and the data lifecycle. Data modeling, covering Entity-Relationship (ER) modeling and the relational model. SQL and hands-on examples. Brief description of core database concepts such as indexing, query optimization, and transaction processing and distributed databases. Non-relational data paradigms (NoSQL databases), including document stores (MongoDB), key-value stores (Redis), and graph databases (Neo4j). Real-time data processing using stream engines (Azure Event Hubs/Stream Analytics) and Kafka, highlighting their roles in event-driven architectures and data pipelines.

Information Systems & Business Process Management

This course introduces the notion of information systems (I.S.) used in enterprises, links them with business analytics (B.A.) and analyses business processes (B.P.) as the fundamental element of modern enterprises and the management of their performance. It consists of four parts:

• Information Systems for Enterprises: Basic principles and functions of I.S. Presentation of IS categories and applications in enterprises. Strategic advantage and I.S. planning. Managing I.S and I.S resources in organizations.
• Business Analytics in Enterprise I.S.: Developing new insights and understanding of business performance through I.S. Achieving the basic types of analytics through I.S.: decisive, descriptive, predictive, prescriptive. Examples of B.A. systems for marketing, retail sales, supply-chain, financial services, telecommunications, e-commerce etc.
• Business Process Management: Types of B.P and their function in the enterprise. BP process modelling techniques. Application of IT tools for BP process modelling and management. Comprehension of BP architecture. Specification of requirements for new IS and infrastructure.
• Principles of BP Performance Management: Process performance metrics and practical case examples of enterprise and inter-organizational systems: ERP, CRM, MIS, e-commerce and e-government. Process management frameworks and the balanced scorecard approach.

Large Scale Optimization

This course introduces advanced optimization tools and techniques with the main emphasis being on the application of computational intelligence algorithms to different large scale optimization problems and cases which arise in business and industry, such as transportation, logistics, production and services. On completion of this course, students should be able to: broaden their exposure to computational methodologies; analyze and design effective computational intelligence algorithms for complex business problems, and; provide examples and cases of how the computational intelligence algorithms can be used to solve real-life problems. The course material includes the following thematic areas: construction and local search algorithms; simulated annealing algorithms; tabu search algorithms; ant colony optimization; evolutionary algorithms.

Statistics for Business Analytics I

This course aims at introducing basic concepts of probability and statistics useful in a great extend in several other courses. The course assumes that everybody has some basic idea about statistics, so the focus will be given to clarify the usefulness and the importance of the approaches and how Statistics can considerably help the decision making process. To this direction a brief introduction to basic principles of probability theory will be given and their connection to problems in Statistics. Basic statistical ideas for descriptive statistics and data visualization will be discussed together with problems of statistical inference like estimation and hypothesis testing. Regression type models will be discussed, including simple, multiple, logistic regression and a brief introduction to generalized linear models. Issues of statistical processing control will be provided. The Bayesian approach in statistical modeling offering certain possibilities with huge datasets will be introduced and worked. All material will be focused mainly on applications but the basic statistical insight will be discussed in depth. Also focus on problems and their modern solutions with big data will be discussed.

Innovation and Entrepreneurship (short course)

The growth of electronic channels over the last decade paired with developments in social media, Web 2.0 and crowd sourcing, sensor networks and ubiquitous computing has led to an explosion of data. Due to the speed of developments, most of these data remain unexploited and the need to derive meaningful information and knowledge out of them has increased to an unprecedented degree. This fact has created a new landscape for innovation and entrepreneurship, opening up new opportunities for the development of new tools, services and offerings that respond to this need. The objective of this course is to provide the theoretical and practical basis that will allow students to identify business opportunities and innovation areas associated with the exploitation of big data and design innovative services in response to the identified business needs. Moreover, the course will provide guidelines in the area of business planning to support an entrepreneurial mindset. A series of case studies will be discussed under this perspective, while students will have the opportunity to propose their own ideas exploiting big data analytics, evaluate alternative business models and practically develop the respective business plans.

Data Visualization(short course)

Basic concepts in data visualizations. Good and bad practices. Basic Principles for good graphs. Visual perception. Vision and psychology. Data to ink ratio. Color selection. Different color pallettes . Grammar of graphics: the different layers. A gallery of graphics: different plots for different data and purposes. Data for flows. Subsetting and trellis plots. Different types of maps. Dashboards and infographics. Story telling and communication of graohics. Interactive graphics. Basic principles like animation, hoovering, filtering and other. Applications like shiny for interactive graphs.

Business Intelligence & Data Engineering

This graduate-level course on Business Intelligence (BI) and Data Engineering equips students with both the conceptual foundations and practical skills necessary to support data-driven decision making in modern organizations. Introduction to business intelligence, focusing on its role in strategic and operational decision-making. Data warehousing: architectures, identifying data sources, the ETL process, data modeling, multi-dimensional/OLAP analysis, cubes, performance issues, visualization. Data engineering: emphasis in the ETL process. Tools, platforms and programming languages for cleaning and transforming data. Defining and managing data pipelines, data orchestration and tools to support these (e.g. AirFlow). Exploratory business analytics tasks by applying Unix command-line tools to extract, transform, filter, process, load, and summarize data.

Statistics for Business Analytics II

This course aims at presenting state to the art methods used with real data for eliciting important information for decision making. The course will start with basic principles of sampling methodologies and their importance. Then dimension reduction methods like Principal Components Analysis and Factor Analysis and their variants will be discussed. Supervised and Unsupervised Statistical learning methods will follow. For unsupervised methods different types of clustering will be discussed, like partition methods, hierarchical methods and model based methods while problems with large data sets will be illustrated. Supervised learning methods like discriminant analysis, decisions trees, kernel based approaches, nearest neighbors and other classification methods will be also presented. Problems of variables and model selection will be discussed. Finally a brief introduction to Predictive Analytics will be given to elaborate the difference and the importance of predictive approaches in Business analytics. For all topics several examples will be used using R and their libraries.

Requirements Engineering for Analytics (short course)

This course equips students with the principles, methods, and tools needed to translate business needs into clear, actionable requirements for analytics projects. It focuses on bridging the gap between business stakeholders, data professionals, and technical teams through structured elicitation, documentation, and validation of requirements specific to analytics solutions—such as dashboards, predictive models, machine learning systems, and decision support tools.
The course blends classical requirements engineering practices with modern analytics-specific approaches and emphasizes cross-functional communication, data literacy, and iterative development.

Python for Analytics & Artificial Intelligence

The “Python for Analytics and AI” course will cover the following topics: Data analysis, Data visualization, Statistical analysis, statistical significance, statistical power, Machine Learning methods, Machine Learning metrics and evaluation of Machine Learning models, Hyperparameter optimization, Neural Networks and neural network architectures, Attention, transformers, Large Language Models (LLMs), Generative AI, Artificial Intelligence and Machine Learning ethics, bias, fairness, interpretability, security.

Business Analytics Use Cases

This course is offered in collaboration with the Master’s program industrial partners and will cover five or six real analytics implementations on a wide range of vertical sectors, such as finance, marketing, health, energy, public sector, supply-chain, transportation, etc. The goal is to present analytics case studies, covering the stages of the analysis pipeline, such as requirements specification and problem definition, data collection, transformation and integration, model building and visualization.

AI for Business Analytics

The course focuses on the recent developments in Generative AI and Deep Learning. We will study the basic concepts and methodologies and get hands-on experience on effective deep learning techniques and best practices of how to set up, organize and perform AI analytics tasks and applications. A brief overview of the course content: Transformers and Large Language Models (LLMs), Fine-tuning Foundation Models, Advanced Prompt-Engineering, Topics in Natural Language Processing (NLP), Multimodal and Vision LLMs, Retrieval Augmented Generation Systems (RAGs), Building AI Agents, Synthetic Data, Agentic Workflows powered by LLMs, Reasoning Techniques, Explainability, Evaluation.

Advanced Topics in Data Analysis (short course)

This course will cover two areas of data analysis among several, possibly different each year depending on the students mix. These areas include: social network analysis, association rules, time-series analysis, graph analytics.

Data Governance and Privacy (short course)

Data Governance: Introduction to the principles, frameworks, and practices essential for managing data as a strategic asset. Data ownership, stewardship, quality, and lifecycle management, roles and responsibilities within a data governance structure. Governance frameworks and policies: regulatory compliance, risk management, and ethical considerations. Implementation strategies, including governance operating models, data cataloging, metadata management, and the integration of governance with business and IT processes.
Data Privacy: Introduction into basic terms/notions: privacy, data protection, confidentiality, security. Information: regulation and governance. Theoretical and regulatory approaches in Greece, EU and abroad. The notion of personal data. Regulation of the use of personal data in EU/ Greece. Analysis of the main concepts, approaches and requirements of General Data Protection Regulation (legal grounds, principles, rights of data subjects). Data Protection by Design and Data Protection Impact Assessment and BA. Big Data Analytics: characteristics of Big Data Analytics and techno-economical context and impact on personal data governance. Big Data Analytics and Data protection principles (purpose limitation, data minimization). Profiling and Decision making. Artificial Intelligence/ Machine learning and processing of personal data. Accountability, transparency and explainability of AI (applications). The issues concerning discrimination and impact of predicting/decision making. Ethics and Business/ Data Analytics.

Cloud Infrastructures for Analytics (short course)

The course centers on Azure Databricks, covering its architecture, collaborative workspace, data ingestion, transformation using Spark, and integration with Delta Lake for scalable and reliable data pipelines. Students learn how to orchestrate workflows, manage compute resources, and deploy machine learning models in the Databricks environment. Broader cloud data engineering concepts are discussed, including storage solutions (e.g. Azure Data Lake), data movement (e.g. Data Factory), and monitoring. Complementary overviews of comparable services in AWS (e.g., Glue, Redshift, SageMaker) and GCP (e.g., BigQuery, Dataflow, Vertex AI) provide a cross-platform perspective, helping students understand trade-offs in cloud service selection. The course combines theoretical grounding with practical labs, giving students hands-on experience building analytics solutions end-to-end in the cloud.