Overview

The MSc in Data Science Programme is being offered by the School of Science & Technology of the University Center of International Programmes of Studies of the International Hellenic University.

The programme aims to provide graduate level education with an interdisciplinary approach as regards the management, visualization and processing of big data as well as the relevant computational techniques and technologies and is targeted towards graduates and professionals who wish to broaden their knowledge in the field.

For almost half a century now, the world has been producing and collecting data in digital form. However, the past decade hardware and network advances have allowed for very large, cost-effective storage and powerful processing as well as fast transfer speeds. At the same time, scientists have been developing software tools for the analysis of big and complex data, to extract valuable knowledge from them. The goal of data scientists is to make sense of large volumes of different forms of real-world data that may come from the entire spectrum of human activity.

Data Science nowadays is an “umbrella term” that encompasses a variety of scientific fields. Essentially, it is an interdisciplinary field that combines a multitude of different disciplines, some of the most important being the following:

  • Artificial Intelligence & Machine Learning
  • Statistics & Mathematics
  • Database and Big Data technology
  • Software Development & Algorithmics

However, the above list is not exhaustive, as a data scientist often needs to employ other skills, such as hacking, coding, critical thinking, problem understanding etc. All those make the job of the data scientist to be a mashup of different skills that are rarely found together.

Students who apply for the MSc in Data Science of the International Hellenic University, are mainly graduates with a STEM (Science, Technology, Engineering and Mathematics) or an Economics degree, who have a background in statistics and a good knowledge of fundamental concepts of databases and programming.

The courses of the programme are taught exclusively in English. The academic staff comes from Universities in Greece and abroad.

Official Government Gazette:

Key facts
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Start date: October 2023

Application deadline extension:  30 September 2023 or until places are filled

Campus: Thermi, Thessaloniki

Duration/Mode: 18 months (full-time) or 30 months (part-time)/(available also in distance learning mode)/weekdays evenings

Taught language: English

Entry requirements: An undergraduate degree from an accredited University

Language requirements: English language knowledge documented with a relevant certificate, corresponding at least to the State Certificate of Language Learning Level B2 or other certificate proving good knowledge of English. Holders of an undergraduate or postgraduate degree at a Foreign University in English are exempt from this obligation.

Fees: 3,700€ (total)

How to apply: Programme announcement- 2nd Phase of Admission of Graduate Students (en+gr)

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Who can apply

To be considered for the programme, candidates are required to have:

  • an undergraduate degree from a recognized University

  • English language knowledge documented with a relevant certificate, corresponding at least to the State Certificate of Language Learning Level B2 or other certificate proving good knowledge of English. Holders of an undergraduate or postgraduate degree at a Foreign University in English are exempt from this obligation.

Course content

Upon arrival at the IHU all students follow an intensive foundation course titled “Applied Mathematics in ICT” that aims to bring all incoming students to the same level with respect to some of the mathematics knowledge that is required to excel in the programme. During the first term, all students are required to attend five mandatory core courses. During the second term, all students follow a further three required courses and a combination of two elective courses. Finally, during the third term, work is dedicated exclusively to the Master’s dissertation.

The core courses

1st Term Core Courses

Instructor(s): Dr. C. Berberidis
Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam Coursework

Aims

The course will examine fundamental programming concepts and principles in the context of Data Science as well as provide students with the proper way of thinking about problems like a Data Scientist. The course covers data selection, iteration and abstraction, functional decomposition and algorithm design as they are applied in typical programming languages, tools and APIs used in Data Science. Students will also learn how to produce high quality computer code by solving actual Data Science problems.

Learning Outcomes

On completing the course, students will be able to:

  • Understand and apply computational thinking in terms of programming methods and data structures.
  • Capture and represent data and learn the basic data analysis, processing and visualization tasks
  • Become proficient in the basic data analysis algorithms and their implementation.
  • Use software tools and programming languages that are particularly suitable for data science and analytics.

Content

  • Data science methodologies.
  • Types of data, hierarchy and representation.
  • Basic data processing and analysis tasks and algorithms.
  • Data analysis software tools and programming languages.
  • Parallel and distributed programming acceleration techniques.

Reading

  • Igual L., Segui S., Virtia J. et al (2017), Introduction to data science: a Python approach to concepts, techniques and applications, Springer.
  • McKinney W. (2012), Python for data analysis: data wrangling with Pandas, NumPy and iPython, O’Reilly.
  • Wickham, H., Grolemund G. (2017), R for data science: import, tidy, transform, visualize and model data, O’Reilly.

Instructor(s):Prof. V. Peristeras
Teaching Hours and Credit Allocation:30 Hours, 6 Credits
Course Assessment:Exam & Coursework

Aims

The course examines the impact of data science in modern private and public organisations and presents challenges, opportunities and trends in the field. The students will gain the necessary conceptual understanding of the uprising “data economy” with its underlying technological and business characteristics. Business cases will be presented and discussed, while specific business problems will be matched with new data technologies. Data/information management and interoperability topics will be also presented and discussed.

Learning Outcomes

On completing the course, students will be able to:

  • Understand the scope of data science and the role/function of data scientists.
  • Identify different types of data that are relevant in business environments.
  • Know which data science solutions can address specific types of business problems.
  • Be able to design a data governance policy.
  • Understand challenges and opportunity in the data-driven economy and public policy.

Content

  • Defining Data Science.
  • Data-analytic thinking.
  • Big/smart/open/linked/meta/reference/master data.
  • Data interoperability.
  • The data value chain.
  • Business problems and data science solutions.
  • Data governance.
  • Data for policy.
  • Data-driven economy.

Reading

  • Data Science for Business, Foster Provost, Tom Fawcett, O’Reilly Media, 2013.

Instructor: Prof. Panayiotis Bozanis
Teaching Hours and Credit Allocation: 30+10 hours, 6 credits
Course Assessment: Exam & Coursework

Aims

The course examines the mathematical and statistical foundations of Data Science and presents the most commonly used statistical methods in the field. The students will gain the necessary conceptual understanding of statistical methods as they are used to analyze and interpret massive data sets as well as extract meaningful conclusions out of them. The course will provide the students with a solid theoretical background and a collection of techniques which can be applied to a wide range of real world problems.

Learning Outcomes

On completing the course, students will be able to:

  • Understand the basic concepts of probability theory and statistics as they are applied in data science.
  • Apply mathematical tools, models and methods to data analysis tasks, such as data fitting, regression, sampling, hypothesis testing etc.
  • Learn the fundamentals of statistical inference and its implementations.
  • Use modern software suites for data analysis, processing and visualization and develop new software tools.

Content

  • Introduction to probability theory.
  • Random variables (univariate, multivariate).
  • Random sampling.
  • Hypothesis testing.
  • Linear regression.
  • Bayesian approach in statistics.
  • Software tools for statistical and data analysis.

Reading

  • Anderson D.R., Sweeney D.J., Williams T.A., Camm J.D., Cochran J.J, Fry M.J., Ohlmann, J.W. (2020). Statistics for Business & Economics, Cengate, 14th edition
  • Heumann C., Schomaker M. Shalabh (2016). Introduction to Statistics and Data Analysis: With Exercises, Solutions and Applications in R, Springer.
  • McClave J.T., Benson P.J., Sincich T. (2018) Statistics for Business & Economics, Pearson, 13th edition
  • Stinerock R (2018). Statistics with R: A Beginner’s Guide, Sage Publishing

Instructor(s): Prof. K. Diamantaras, Dr. C. Berberidis
Teaching Hours and Credit Allocation:   30 Hours, 6 Credits
Course Assessment:  Exam & Coursework

Aims

Machine learning is an exciting field which entails using computers to run predictive models that learn from existing data and produce conclusions or recommendations. The course covers the fundamental theory, concepts and numerical techniques of machine learning, drawing upon ideas and tools from probability, Artificial Intelligence and algorithm design. The students will also familiarize themselves with common machine learning software libraries and APIs.

Learning Outcomes

On completing the course, students will be able to:

  • Develop an appreciation for what is involved in learning from data.
  • Explain a wide variety of learning algorithms.
  • Understand how to apply a variety of learning algorithms to data.
  • Understand how to perform evaluation of learning algorithms and model selection

Content

  • Introduction to learning from data
  • Classification (KNN algorithm)
  • Decision trees
  • Classifier ensembles
  • Naïve Bayes
  • Clustering (k-means)
  • Model evaluation

Reading

  • Tom Mitchell, Machine Learning, McGraw Hill, 1997.
    Kevin P. Murphy, Machine Learning A Probabilistic Perspective, MIT Press, 2012.
    Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2013.

Instructor(s): Prof. C. Tjortjis
Teaching Hours and Credit Allocation: 30 Hours, 6 Credits
Course Assessment: Exam & Coursework

Aims

The course aims to familiarise students with contemporary database systems, as well as emerging database technologies. It discusses basic aspects of advanced database techniques and exposes tools and technologies that can be used along with “core” database systems. Students are expected to engage in practical database system design through a series of assignments and coursework. The emphasis in the lectures will be on general concepts and theoretical foundations. In addition to the theoretical concepts, the course will require students to use commercial database systems and develop a class project.

Learning Outcomes

  • On completing the course, students will be able to:
  • Develop the logical model of a relational database.
  • Use essential SQL tools to program DB systems.
  • Understand advanced concepts of DM management and architecture.
  • Organize, store and process data efficiently, using contemporary technologies such as Data Warehouses.
  • Understand and apply various emerging technologies, including Data Mining, OLAP, Information Retrieval, and Search engines.
  • Understand and utilise knowledge extracted from large volumes of data.

Content

  • ER model, relational model.
  • SQL.
  • Indexing.
  • Query processing and optimization.
  • Data warehousing and OLAP.
  • Data Mining and Business Intelligence.
  • Information Retrieval.
  • Web Search.

Reading

  • Elmasri R., Navathe S. B., (2010), Fundamentals of Database Systems: Global Edition, 6th Edition, Pearson.
  • Garcia-Molina H., Ullman J., and Widom J., (2009), Database Systems: The Complete Book, 2nd edition, Pearson.
  • Silberschatz A., Korth H., and Sudarshan S., (2010), Database System Concepts, 6th Edition, McGraw-Hill.
  • Ramakrishnan R, Gehrke J. (2002), Database Management Systems, 3rd edition, McGraw-Hill Science/Engineering/Math.

2nd Term Core Courses

Instructor:Prof. Diamantaras
Teaching Hours and Credit Allocation:30 Hours, 6 Credits
Course Assessment:Exam & Coursework

Aims

This course covers advanced topics in machine learning and students are expected to have a solid background in machine learning. The topics to be studied include (among others) Support Vector Machine, graphical models, Kernel methods, unsupervised and semi-supervised learning. Students will gain a deep understanding of both the theoretical and computational aspects of state-of-the-art machine learning and improve their modeling and computational skills by implementing algorithms for some of the above techniques to solve actual problems.

Learning Outcomes

On completing the course, students will be able to:

  • Take an understanding of how to choose a model to describe a particular type of data.
  • Apply the chosen method properly.
  • Know how to evaluate a learned model in practice.
  • Be able to design and implement various machine learning algorithms in a range of real-world applications.

Content

  • Statistical Learning Theory.
  • Mixture Models and the EM Algorithm.
  • Generative and Discriminative Learning.
  • Graphical Models.
  • Hidden Markov Models.
  • Generalization and Model Selection.
  • Support Vector Machines, Kernels.
  • Transduction, Feature Selection.
  • Deep learning.

Reading

  • Duda, Hart and Stork, Pattern Classification.
  • Chris Bishop, Neural Networks for Pattern Recognition.
  • Chris Bishop, Pattern Recognition and Machine Learning.
  • Michael Kearns and Umesh Vazirani, An Introduction to Computational Learning Theory, MIT Press, 1994.
  • Mehryar Mohri, Afshin Rostamizadeh and Amit Talwar, Foundations of Machine Learning, MIT Press, 2012.
  • Shai Shalev-Shwartz and Shai Ben-David. Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press, 2014.

Instructor(s):Prof. M. Drakaki, Dr. S.G. Stavrinides
Teaching Hours and Credit Allocation:30 Hours, 6 Credits
Course Assessment:Exam & Coursework

Time series analysis has been established as a tool for understanding and representing data associated with complex real-life problems. Time series analysis, modelling and forecasting has been widely applied to solve practical problems in a wide range of scientific disciplines including natural, social and political sciences, economics and engineering. Early revolutionary works on applications of time series analysis by using mathematical linear models have demonstrated the suitability of the linear time series methodology in understanding and representing dynamic real time series data. On the other hand, the paradigm of deterministic chaos has influenced thinking in many fields of science. Chaotic systems show rich and surprising mathematical structures. In the applied sciences, deterministic chaos provides a striking explanation for irregular behaviour and anomalies in systems which do not seem to be inherently stochastic. The most direct link between chaos theory and the real world is the analysis of time series from real systems in terms of nonlinear dynamics. Experimental technique and data analysis have seen such dramatic progress that, by now, most fundamental properties of nonlinear dynamical systems have been observed in the laboratory. Great efforts are being made to exploit ideas from chaos theory wherever the data displays more structure than can be captured by traditional methods. Problems of this kind are typical in biology and physiology but also in geophysics, economics, and many other sciences.

Aims

This course aims in providing solid knowledge on a domain that is beneficial to those studying AI and machine learning. Timeseries analysis and forecasting is a domain where computer science, and coding meet mathematics, physics and other natural sciences, engineering, economics, finance and social sciences. Comprehensive knowledge on the theoretical foundations of the area (fundamental principles, elements etc.) is offered. The course includes timeseries analysis by utilizing both linear approaches and nonlinear dynamics. Both modules move towards the final goal which is timeseries forecasting for practical applications.

Learning Outcomes

On completing the course students will be able to:

· Understand the essential mathematics and algorithms behind contemporary timeseries analysis.

· Understand the methods utilized for forecasting the temporal evolution of dynamical systems.

· Implement timeseries analysis both by utilizing linear and nonlinear methods.

· Learn how to analyze, model and forecast time series data by using statistical software packages.

· Successfully implement timeseries modelling and forecasting.

· Understand and estimate the limits of proper and reliable forecasting.

· Have the background needed and experience, to understand the upcoming methods and approaches in the area.

Content

· Introduction to time series analysis

· Basic characteristics of stationary processes

· Time series models (ARMA, ARIMA, SARIMA)

· Time series forecasting

  • Short introduction to Chaos Theory

· Basic characteristics of nonlinear timeseries and their analysis

  • Reconstruction of phase space
  • Dimensions, entropies and other invariant metrics
  • Timeseries forecasting methods and models

Reading

· “Introduction to time series and forecasting” by Brockwell P.J. and Davis R.A., 3rd edition, Springer, 2016.

· “Introduction to Time Series Analysis and Forecasting” by D. C. Montgomery, C. L. Jennings, M. Kulahci, 2nd edition, Wiley, 2015.

· “Nonlinear Timeseries Analysis” by Holger Kantz and Thomas Schreiber (2 nd edition).

· “Elements of Nonlinear Timeseries Analysis and Forecasting” by Jan G. De Gooijer

Instructor(s): Prof. C. Tjortjis
Teaching Hours and Credit Allocation:   30 Hours, 6 Credits
Course Assessment:  Exam & Coursework

Aims

The course covers Knowledge Discovery in Databases (KDD) and Data Mining (DM) as a set of computational tools and technologies, which provide valuable assistance for business analysis and strategic business decision making. This is a hands-on course that provides an understanding of the key methods of data visualization, exploration, classification, prediction, and clustering. Students will learn how to apply various data mining techniques for solving practical problems and how to develop and use simple business analytics systems.

Learning Outcomes

On completing the course, students will be able to:

  • Organise and efficiently process any knowledge, either given a priori or extracted
  • Understand the basic concepts of data mining
  • Understand and apply various data mining approaches, including Classification, Clustering and Association Rules.
  • Understand, evaluate and utilise knowledge extracted from large volumes of data.

Content

  • Introduction to Knowledge Discovery in Databases (KDD) and Data Mining (DM).
  • Classification.
  • Clustering.
  • Association Rules.
  • DM Systems, Data pre-processing and Evaluation.

Reading

  • J. Han and M. Kamber, Data Mining: Concepts and Techniques, 3rd ed., The Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann Publishers, 2011.
  • I. Witten, E. Frank, and M. Hall, “Data Mining: Practical Machine Learning Tools and Techniques”, 3rd Ed., Morgan Kaufmann, 2011.
  • J. Ledolter, Data Mining and Business Analytics with R, Wiley, 2013.
  • P.N. Tan, M. Steinbach, and V. Kumar, “Introduction to Data Mining” Int’l Ed., 1/e, Pearson Higher Education, 2006.
  • R. Sharda, D. Delen, E. Turban, Decision Support and Business Intelligence Systems Int’l Ed. 10/E, Pearson Higher Education, 2015.
  • M.H. Dunham, “Data Mining: Introductory and Advanced Topics”, Prentice Hall, 2003.
  • M.M. Gaber (ed.), Journeys to data mining: experiences from 15 renowned researchers, Springer, 2012.

The elective courses

During the second term students tailor their programme further by choosing elective courses. The choice of elective courses must sum up to 12 ECTS (2 courses). Some of the elective courses may not be offered in a particular year, depending entirely on student demand. 2nd Term Elective Courses

Instructor:Prof. Bozanis
Teaching Hours and Credit Allocation:30 Hours, 6 Credits
Course Assessment:Exam & Coursework

Aims

The big data explosion has led to new computing paradigms, the most prevalent among them being cloud computing. Cloud computing is about vast computing resources on demand, that allow for centralized data storage and online access. Big data is a broad term that includes several concepts and tasks, such as data capture, storage, sharing, management and analysis. This course focuses mostly on the big data storage and management part, rather than the analysis as well as cloud service models, architectures and tools. Students will familiarize with modern big data and cloud technologies, understand the privacy and security concerns and learn about popular big data and cloud computing platforms.

Learning Outcomes

On completing the course, students will be able to:

  • Develop the knowledge, understanding and skills to work with Big Data.
  • Deploy a structured lifecycle approach to data analytics problems.
  • Apply appropriate analytic techniques and tools to analyzing big data.
  • Understand Cloud Computing Concepts and Mechanisms.
  • Learn the concepts, principles, techniques and methodologies you need to manage cloud services and resources.

Content

  • Big data concepts, principles and practical applications.
  • Big data capture, storage, sharing, management and analysis.
  • Cloud Computing Concepts and Mechanisms.
  • Cloud Architectures.
  • Working with Clouds.
  • Managing Cloud Services and Resources.
  • Big Data and cloud computing platforms.

Reading

  • T. Erl, R. Puttini, Z. Mahmood, Cloud Computing: Concepts, Technology & Architecture, Pearson, 2013.
  • EMC Education Services (Editor), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley 2015.

Instructors:Dr. Berberidis, Prof. Papadopoulos
Teaching Hours and Credit Allocation:30 Hours, 6 Credits
Course Assessment:Exam & Coursework

Aims

This course covers the main principles and techniques of Natural Language Processing (NLP) and its associated computational tools, especially with regards to written text. The course provides the required background material on computational linguistics and statistical language analysis and describes the machine-learning-based models that are widely used for analysis. Typical NLP tasks, such as text parsing, classification and translation will also be described and the students will gain familiarity with widely used software tools for these purposes.

Learning Outcomes

On completing the course, students will be able to:

  • Understand how natural language processing (NLP) draws upon other areas of computer science and data analysis.
  • Design and build computer systems and software for various tasks of NLP.
  • Understand and implement the most important algorithms and techniques in NLP and text mining.
  • Formulate models and construct computational solutions to text and speech-based processing problems.

Content

  • Introduction to natural language processing and its challenges.
  • Syntax and parsing (syntactic, semantic).
  • Language and speech modeling.
  • Text classification and clustering.
  • Sentiment analysis.
  • Machine translation.

Reading

  • Manning C., Schutze H. (1999), Foundations of statistical natural language processing, MIT Press.
  • Jurafsky D., Martin J. (2008), Speech and language processing, Prentice Hall, 2nd edition.
  • Bird S., Klein E., Loper E. (2009), Natural language processing with Python: analyzing text with the Natural Language Toolkit, O’Reilly.

Instructor:  Prof. Panagiotis Bozanis
Teaching Hours and Credit Allocation:   30 Hours, 6 Credits
Course Assessment:  Exam & Coursework

Aims

The course covers the basic principles and techniques of information retrieval, which is the process by which a computer system can respond to a query about a given topic. A successful and meaningful response requires efficient data organization and classification, as well as efficient indexing and clustering algorithms. The students will study all aspects of data organization and processing that allow for efficient information retrieval as well as the underlying computational models and tools.

Learning Outcomes

On completing the course, users will be able to:

  • Understand key concepts of information retrieval techniques and be able to apply these concepts into practice.
  • Apply information retrieval principles to locate relevant information in large collections of data.
  • Understand and deploy efficient techniques for the indexing of document objects that are to be retrieved.
  • Implement features of retrieval systems for web-based and other search tasks.
  • Analyse the performance of retrieval systems.

Content

  • Introduction to information retrieval.
  • Retrieval Models.
  • Dictionaries, term vocabulary and postings lists.
  • Index construction and compression.
  • Vector space model and classification.
  • Support vector machines and machine learning on documents.
  • Search systems.
  • Latent semantic indexing.
  • Link analysis.
  • Evaluation.

Reading

  • C.D. Manning, P. Raghavan and H. Schütze (2008), Introduction to Information Retrieval, Cambridge University Press.
  • S. Büttcher, C.L. A. Clarke and G.V. Cormack (20016), Information Retrieval, Implementing and Evaluating Search Engines, MIT Press.
  • Grossman, D.A., Frieder, O. (2004), Information Retrieval, Algorithms and Heuristics, Springer.

Teaching Hours and Credit Allocation:30 Hours, 6 Credits
Course Assessment:Exam & Coursework

Aims

This course examines basic concepts of Knowledge and Knowledge Management, placing emphasis on knowledge encountered in the Web. At first, it briefly deals with the notion of knowledge and its sources, the architecture and life cycle of knowledge management systems, how knowledge is captured, and how knowledge is formally represented using various formalisms. The core theme of the course covers extensively information and knowledge representation and interchange technologies in the Web, such as information representation using XML, information processing using XPath/XSLT, metadata representation using RDF, vocabulary descriptions using RDF Schema, and finally, knowledge representation in the web, using ontologies (OWL), and rules (SWRL, OWL2 RL, RIF). During the course various knowledge management web systems and tools are demonstrated and practised.

Learning Outcomes

On completing the course, students will be able to:

  • acquire essentials skills on Knowledge Management Systems
  • comprehend web Knowledge Management languages and technologies, including XML, XPath, XSLT, RDF, RDFS, OWL
  • use Knowledge Management systems through selected assignments.

Content

  • Basic concepts of Knowledge and Knowledge Management.
  • Knowledge modeling: Ontologies and Linked Data.
  • Representation languages (XML, RDF, RDF Schema, OWL, SPARQL).
  • Web services (SOAP, JSON, OWL-S).
  • Demonstration and practice of various web Knowledge Management systems (e.g., Protégé, Google Knowledge Graph).

Teaching Hours and Credit Allocation:   30 Hours, 6 Credits
Course Assessment:  Exam & Coursework

Aims

This course examines the analysis of multimedia (e.g. image, video, audio) data in large scale datasets for the purposes of identification and extraction of useful information. Students will be exposed to a large array of techniques, ranging from machine learning and pattern recognition to signal processing and computer vision, for multimedia processing and will gain a deep understanding of the unique challenges that arise in terms of scalability, accuracy and semantics.

Learning Outcomes

On completing the course, students will be able to:

  • Use tools for multimedia information analysis.
  • Extract meaningful features from different types of multimedia data.
  • Be able to implement semantic multimedia content classification and annotation.
  • Apply speech and speaker recognition techniques.
  • Know how to perform video indexing and summarization.

Content

  • Intelligent tools and techniques for multimedia information analysis.
  • Semantic content analysis and annotation.
  • Feature extraction.
  • Multimedia information processing.
  • Video summarization and indexing.
  • Multimedia databases.
  • Multimedia data applications and future trends.
  • Cross-modal access to information.

Reading

  • Data Management for Multimedia Retrieval, K. Selçuk Candan, Maria Luisa Sapino (2010).
  • Semantic Multimedia Analysis and Processing, Evaggelos Spyrou, Dimitris Iakovidis, Phivos Mylonas, 2014, CRC Press.
  • Multimedia Data Mining: A Systematic Introduction to Concepts and Theory, Zhongfei Zhang, Ruofei Zhang, 2008, Chapman and Hall/CRC.

Instructors:Dr. Magnisalis, Dr. Baltagiannis
Teaching Hours and Credit Allocation:30 Hours, 6 Credits
Course Assessment:Exam & Coursework

Aims

The course covers the essential exploratory techniques for analyzing and summarizing data, with the aid of visualization techniques.  Students will learn how they can determine the underlying structure and model variables in a problem by examining the data itself, which can provide valuable insight about the problem itself and also suggest the type of formal statistical analysis to be subsequently applied. Visualization and plotting of high-dimensional data will also be studied.

Learning Outcomes

On completing the course, students will be able to:

  • Understand the essential exploratory techniques for summarizing data.
  • Understand and use the plotting systems in Python and/or R
  • Understand basic principles of constructing data graphics.
  • Understand and use common multivariate statistical techniques to visualize high-dimensional data

Content

  • Fundamentals of data visualization.
  • Constructing data graphics.
  • Analytic graphics.
  • Visualizing high dimensional data.
  • Statistical methods for exploratory analysis.
  • Essential exploratory techniques for summarizing data.
  • Dimension Reduction.
  • Perception of discrete and continuous variables.
  • Dynamic graphics.
  • Model visualization.

Reading

  • Unwin, A. (2015), Graphical data analysis with R, CRC Press.
  • W.L. Martinez, A.R. Martinez, J. Solka (2010), Exploratory Data Analysis with MATLAB, 2nd Ed., CRC Press.
  • D.C. Hoaglin, F. Mosteller, J.W. Tukey (2000), Understanding Robust and Exploratory Data Analysis, 1st Ed., Wiley.
  • J. W. Tukey (1977), Exploratory Data Analysis, Pearson.

Teaching Hours and Credit Allocation:30 Hours, 6 Credits
Course Assessment:Exam & Coursework (one mini project, one main assignment)

Aims

With the rise of Social Media (SM), SM data analytics and SM content marketing have become increasingly popular with businesses, researchers, marketers, and the general public.

This course aims to teach the fundamentals of SM marketing and data analytics focusing on tools and methodologies to align SM returns with the business value.

Students will be introduced on the main data models and the fundamentals of social network analysis (SNA), being involved in several SNA use case discussions on social, politics and business related topics.

Both theoretical and practical concepts will be explained, assisting students to realize the tight links between web/data science and marketing strategies in today’s social networking era.

Students will also be assigned a mini project to get deeper insights of a popular social media platform and its tools.

The course will include in-course workshops on SM data retrieval, content curation, keywords research and SM design strategies for business campaigns.

Learning Outcomes

Upon successful completion of this course, students will be able to design and implement a successful SM marketing, research and content plan for a list of popular social media network sites, and utilize available tools for research and content optimization. Students will also gain the required knowledge to extract SM data, define KPIs and recognize/use the appropriate types of data analytics to interpret SM business returns.

Content

  • Introduction to Social Networks and Data Models
  • Social Network Analysis – Use Cases
  • Social Media Types, Tools and Management Platforms, Content Curation tools and methods, Keyword research tools and methods, Competitor analysis tools and methods
  • Social Media Marketing for Business: theory and practice
  • Buyer Personas and Consumer Segmentation
  • Social Media Content Marketing Strategies, use cases of ‘successful’ firms and campaigns
  • Instagram & Facebook Marketing and Research Plan
  • Instagram & Facebook Relationship Building
  • Social media metrics and KPIs, defining engagement in different networks and contexts
  • Social Media Data Analytics and Business Value
  • Web Application Programming Interfaces (APIs), Social Media APIs (Introducing the Graph API) and Data Retrieval

Reading

  1. Social Media Marketing Workbook: How to Use Social Media for Business . (2019). Jason McDonald
  2. Creating Value With Social Media Analytics: Managing, Aligning, and Mining Social Media Text, Networks, Actions, Location, Apps, Hyperlinks, Multimedia, & Search Engines Data (2018). Gohar F. Kha n
  3. Social Network Analysis: Methods and Examples (2017). Song Yang , Franziska B Keller , Lu Zheng

Credit Allocation:6 Credits
Course Assessment:Final deliverable

Aims

The Consulting Project will require students to apply knowledge gained in classroom into practice. Students will tackle real-life problems and challenges facing companies or organisations in order to provide actual business solutions. Following a procedure of specifications/requirements, design and implementation, students will prepare and present their concrete and practical solutions in a final deliverable report.

Learning Outcomes

On completing the course, students will be able to:

  • Understand real-world problem faced by companies/firms and propose functional solutions.
  • Develop critical thinking and ability to integrate data and information towards the optimal solution.
  • Understand the structure, operational mode and challenges of real-world companies.

Content

  • Understanding and recording a company’s needs and challenges.
  • Project requirements.
  • Data analysis, implementation and company feedback.
  • Producing a deliverable.

The Dissertation

During the third term, students work on their Masters Dissertation project, the thematic area of which is relevant to their programme of studies and their interests. The dissertation provides a good opportunity to apply theory and concepts learned in different courses to a real-world Data Science problem or challenge. Students are supervised throughout their projects by a member of the academic faculty and the academic assistants. After submission of the dissertation, students present their projects to classmates and faculty at a special event.

 

Duration of studies

The duration of the full-time study programme in order to obtain the MA degree is three (3) academic semesters. For students who so wish, there is also the possibility, upon request, of attending the programme on a part–time basis. In this case, the duration of the MA will be five (5) academic semesters. Lectures mainly take place on weekday evenings. The programme is also available through distance learning. Distance Learning teaching methods involve: (a) Face-to-face or classroom based learning: Students will be required to be physically present at the University for a weekend at the beginning of each semester (b) Synchronous learning: Student will have to attend remotely the classes which will be held regularly during each semester, weekday afternoons (about 2-4 times per week depending on the mode, always after 17:00) and possible Saturday morning  (c) Asynchronous learning: Students will use online learning resources and will be assessed through a variety of diagnostic tools and formative assessment techniques (d) Summative assessment: Students will be typically required to be physically present at the University for the final exams at the end of each semester.

The Academic Faculty

 

Faculty Members

Dr Christos Tjortjis Dr Christos Tjortjis Associate Professor
Dean of the School
+30 2310 807576
c.tjortjis@ihu.edu.gr
Dr Maria Drakaki Professor Maria Drakaki
Deputy Dean of the School
+30 2310 807524
mdrakaki@ihu.gr
Professor Panayiotis Bozanis Professor Panayiotis Bozanis
+30 2310 807501
pbozanis@ihu.gr
Dr Eleni Heracleous Dr Eleni Heracleous
Associate Professor
e.heracleous@ihu.edu.gr
Dr Vassilios Peristeras Dr Vassilios Peristeras Associate Professor
+30 2310 807539
v.peristeras@ihu.edu.gr
Dr D.Tzetzis Dr Dimitrios Tzetzis Associate Professor
+30 2310 807548
d.tzetzis@ihu.edu.gr
Dr D.Tzetzis Dr Spiros Papakostas Assistant Professor
spapakostas@ihu.edu.gr

 

Other Research and Teaching Personnel

 

Dr Christos Berberidis Dr Christos Berberidis Research and Teaching Staff +30 2310 807534 c.berberidis@ihu.edu.gr
Dr Dimitrios Baltatzis Dr Dimitrios Baltatzis Research and Teaching Staff d.baltatzis@ihu.edu.gr
Dr Georgios Martinopoulos Dr Georgios Martinopoulos Academic Associate +30 2310 807533 g.martinopoulos@ihu.edu.gr
Image not available Dr Leonidas Akritidis Academic Associate
Image not available Dr Dimitrios Karapiperis Academic Associate
Image not available Dr Paraskevas Koukaras Academic Associate
Image not available Dr Nikolaos Serketzis Academic Associate
Image not available Dr Katerina Tzafilkou Academic Associate

 

Visiting Faculty

  • Prof. Stavros Stavrinides
  • Prof. Apostolos Papadopoulos
  • Prof. Konstantinos Diamantaras
  • Prof. Nikolaos Bassiliades
  • Dr. Theodoros Semertzidis
  • Dr. Nikolaos Vretos

Fees & Financing

Fees

The programme fees for the MSc in Data Science is 3,700€. The amount is payable in two instalments for the full time mode or in four instalments for the part time mode at the beginning of each semester. The fees are also eligible for financing through LAEK 0,45% – OAED programme.

Deposits

If you have been accepted to a postgraduate programme, you will need to make a payment of the deposit of 500 Euros to secure your place. This amount will count towards the first instalment of your tuition fees. The deposit is non-refundable once you have commenced your studies at the IHU. Prior to that, a refund can be made but a 20% administrative fee will be retained. The deposit can be paid by bank transfer or bank draft. Credit card payments can be made through electronic banking (contact your Bank as handling fees may apply).

Scholarships

The School of Science & Technology offers a number of scholarships for the programmes it offers, covering a significant proportion of the fees. These scholarships are competitive. Award criteria include the quality of the first degree, the undergraduate grades of the candidate, his/her command of the English language and overall profile. Candidates for scholarships should include a separate letter with their application documents in which they request to be considered for a scholarship, stating the reasons why they think they qualify.

Programme announcement – Admissions

Next MSc in Data Science class starts in October 2023. The application deadline for the MSc programme has been extended. Interested parties are invited to submit their application from July 1st, 2023 to September 30th, 2023  or until places are filled, by following instructions at the application page.

Ideal Career path

According to the European Commission’s European Data Market study the number of “data companies” as well as the need for “data workers” are already high and it is expected to grow even more in the near future. Depending on the focus of your study and skills, there are several career paths you can follow; the list below, although it is non-exhaustive, it covers the spectrum of roles you can play in an organisation:

  • Data Management Professional: Focuses on managing the infrastructure and storage of (usually big) data.
  • Data Engineer/Data Architect: Focuses on the design and implementation of (usually big) data infrastructure, choosing the right database and cloud technologies and deploying them to serve the analytics needs of the organization.
  • Business Analyst: Focuses on the analytics part, trying to process data to build models to form useful and actionable insights. It includes anything related to Business Intelligence, such as creating reports, dashboards etc.
  • Data Analyst/Data Scientist: Focuses on developing and applying machine learning and statistical models on the data at hand. They need to have coding skills, with Python and R being the most popular options right now as well as knowledge of algorithmics, statistics and databases.
  • Machine Learning Researcher: Focuses on developing and testing predictive and descriptive models from data. They need to have a deep understanding of machine learning and statistics to run experiments and evaluate the results. Machine learning research positions are available not only in universities (e.g. PhD candidateships, PostDoc Associates etc.) but also in industry as big companies are being staffed with analytics researchers who try to create custom models for their needs instead of using off-the-shelf products and applying sub-optimal, generic solutions.

In addition to technical skills gained through study, our students benefit from the University’s excellent Careers Office in order to attain essential soft skills (e.g. communication skills, interview preparation, CV writing etc.) to better prepare for the job market.

Location

The MSc in Data Science takes place in the facilities of the School of Science & Technology of the University Center of International Programmes of Studies of the International Hellenic University in Thermi-Thessaloniki.

Contact

Postal address: School of Science & Technology Department of School of Science & Technology University Center of International Programmes of Studies 14th km Thessaloniki – Nea Moudania 570 01 Thermi, Thessaloniki, Greece Tel: +30 2310 807 529 Email: : infotech@ihu.edu.gr