A collaboration for success

This MSc (Master of Science) in Data Science program is the result of a collaboration between the National Centre for Scientific Research "Demokritos" and the University of the Peloponnese.

NCSR "Demokritos", is one of Europe's leading multi-disciplinary scientific Research Centers. Its Institute of Informatics & Telecommunications, which is responsible for the MSc program, has a strong background on data management and data analytics, focusing on big data technologies. The multidisciplinarity of NCSR "Demokritos" will also be exploited, enriching the MSc program with use cases from the domains of energy, environment and biology.

The University of the Peloponnese has a strong backgound in Postgraduate courses. Its Department of Informatics and Telecommunications, which will have the responsibility of the MSc program, has strong expertise on data bases, data management, statistical methods and data visualisation.

How does this Degree help me?

Having completed the course, you will:
- understand how data can be gathered, managed, processed and mined to support and improve strategic planning, as well as decisions and actions across a variety of domains;
- better position yourself in the job market, through specializing in the analysis of omni-present data;
- gain access to new career options and paths.

Obtaining the MSc in Data Science Degree, you will be a valuable asset for any enterprise, academic organization or public institution handling data. Your access to market opportunities will be strengthened by the international reputation and strong links of the MSc founders with the academia and businesses worldwide. You should also expect a number of opportunities to obtain work experience and engage in European projects, due to the Research Center’s heavy involvement as a coordinator and partner in such endeavours.

Who is it for?

The MSc curriculum, implemented is ideal for:
- professionals who want to expand their knowledge and skills;
- graduate students of engineering, mathematics, statistics, finance, economics, operations research, and computer science schools.

Where will the courses take place?

All classes are held in English (including the Diploma Thesis) and attendance takes place in the evenings - 2-3 times a week - at the NCSR "Demokritos" campus in Athens, Greece.

Living and studying in Athens

The campus is located in Aghia Paraskevi suburb of Athens which allows students to enjoy:
- a rich lifestyle and cultural experience;
- ease of access to travel destinations, within and outside the city;
- exploring Greece and its famous islands.

You can find instructions to reach the NCSR Demokritos campus here.
 

Duration and Fees

This Degree is designed to run in the evenings, 2-3 days a week for two (2) years full-time.
More specifically, in the 1st and 2nd semesters students need to attend four (4) courses per semester on which they will be examined at the end of the respective semester. During the 3rd semester, students choose one (1) compulsory course and three (3) optional courses. In the 4th semester students prepare their diploma thesis. The courses, as well as the assignments and the exams, are all held in the English language.

Tuition fees for all students (Domestic and International) amount to €3.000 in total for both years of their study. Tuition fees are paid in three (3) installments, at the beginning of each semester for the first three semesters of study. The installments are set at €1.000.

1st Semester

(choose all 4)

Instructors:

Spiros Skiadopoulos (UoP), Christos Tryfonopoulos (UoP)

Topics per week

1. Overview
2. Entity relation model
3. Relational model
4. Relational algebra
5. SQL
6. Query processing
7-8. Query optimisation
9. Primary and secondary storage
10. Tree-structured indexes
11. Hash-based structures
12-13. Database tuning and physical design for massive datasets

Instructor:

Ioannis Moscholios (UOP)

Topics per week

1. Review on basic probability theorems
2. Discrete and continuous random variables
3. Bayesian inference and the posterior distribution
4. Point estimation, hypothesis testing, and the MAP Rule
5. Bayesian least mean squares estimation
6. Bayesian linear least mean squares estimation
7. Statistical inference
8. Classical parameter estimation
9. Linear regression
10. Binary hypothesis testing
11. Significance testing
12-13. Introduction to multivariate models

Instructor:

George Petasis (NCSR Demokritos)

Topics per week

1. Introduction to machine learning
2. Supervised learning setup
3. Decision trees. Logistic regression. Perceptron
4. Exponential family. Generative learning algorithms. Gaussian discriminant analysis
5. Naive Bayes. Support vector machines
6. Model selection and feature selection. Ensemble methods
7. Evaluating and debugging learning algorithms
8. Learning theory
9. Unsupervised learning
10. Principal components analysis
11. Large-scale machine learning
12. Deep learning
13. Semi-supervised and active learning

Instructor:

Iraklis Klampanos (NCSR Demokritos)

Topics per week

1. Introduction to data programming
2-3. Python programming
4-5. Data stream processing
6-7. Data acquisition: web services, streams, data transfer
8-9. Octave/Matlab/R for data analysis
10-11. Optimisation considerations, vectorisation, GPUs
12-13. Use-case combining batch processing, streaming and analysis

2nd Semester

(choose all 4)

Instructors:

Christos Tryfonopoulos (UoP), Spiros Skiadopoulos (UoP)

Topics per week

1. Getting to know your (Big) Data
2-3. Architectures for Big Data
4. Distributed object location
5. Distributed file systems (Cassandra, BigTable, HBase)
6. The Map/Reduce paradigm
7-9. Parallel data processing with Hadoop
10. Parallel graph processing (Pregel, Hama)
11. NoSQL databases (key-value/document/graph stores)
12. Column stores
13. Distributed stream processing

Instructors:

Nikos Platis (UoP), Iraklis Klampanos (NCSR Demokritos)

Topics per week

1. Visual perception
2-3. Visualization techniques
4. Interactive visualizations
5-6. Visualization software (Tableau and other tools)
7. Visual communication
8-9. Visualization in Python
10-12. Case studies
13. Presentations of student projects

Instructor:

George Petasis (NCSR Demokritos)

Topics per week

1. Introduction to natural language processing
2. Architectures for natural language processing
3. Big Data analysis
4. Named-entity recognition
5. Disambiguation
6. Sentiment analysis and opinion mining
7. Information extraction and topic modelling
8. Summarization
9. Question answering
10. Natural language processing of Big Data
11. Parallel natural-language processing with Hadoop
12. Large Knowledge-bases
13. Deep learning for language processing

Instructor:

George Giannakopoulos (NCSR Demokritos)

Topics per week

1. Data mining basic concepts
2. Data types and features
3. Use-cases: text representation, representing data from bioinformatics
4. Data preprocessing and cleaning
5-6. Data classification and clustering
7. Relations and sequences
8. Similarity-based data mining
9. Outliers and concept drift
10. Evaluation in data mining
11. Human evaluation, automatic and semi-automatic evaluation problems
12. Big data mining
13. Big data mining tools

3rd Semester

Obligatory Seminar

Instructors:

George Giannakopoulos (NCSR Demokritos), TBA (NCSR)

Topics per week

1. Scientific method overview
2. Hypotheses and testing
3. Risks in hypothesis testing
4. Scientific error and scientific lies
5. Reviewing scientific work: the peer reviewing process; how to do a good review; how to review one’s own work.
6. Communicating scientific results: clarifying science; risks in publication of results
7. Legal and ethical issues overview: overview of legal and ethical risks
8. Data licensing, sharing, openness: how to share or reuse data; licences and their meaning
9. Emerging data formats and publishing (nano-publications; semantic web)
10. Anonymization and profiling: data aggregation and anonymization; discovering user identity through profiling
11. Privacy and Security concerns: difference between privacy and security; privacy in data publication; sensitive data
12. Ethics considerations in data analysis: the effect and impact of scientific discovery; ethics and data analysis
13. Social understanding of data and ethics

Optional Courses (Choose 3 from the following 5)

Instructor

Theodoros Giannakopoulos (NCSR Demokritos)

Topics per week

1. General signal and image processing issues
2. Audio representations and feature extraction
3. Audio content characterization: classification, segmentation, clustering and alignment
4. Music Information Retrieval
5. Speech recognition
6. Image introduction and representations
7. Image segmentation: thresholding, edge-based, region-based
8. Image classification and retrieval
9. Video analysis: motion analysis, flow extraction, temporal event recognition, tracking
10. Deep-learning-based image and video characterization
11. Audio-visual fusion
12-13. Implementation based on open-source audio-visual libraries

Instructors

TBA (UoP)

Topics per week

1. Motivation for a network of sensor nodes
2. Architecture
3. Sensor Layers
4. Management (power, time, localization, security)
5. Adaptive/self managed sensor networks
6. Data-centric sensor networks
7. Mobile sensor databases
8. Query processing in sensor networks
9. Introduction to IoT
10. Protocols and Middleware in IoT
11. IoT Architectures
12. IoT Development Platforms
13. Performance evaluation tools in IoT

Instructor

Nikos Kolokotronis (UoP)

Topics per week

1. Introduction to security
2. Cyber-threat landscape
3. Cryptography for big data: introduction
4. Federated identity management
5. Decentralised systems security: systems (i.e. Hadoop)
6. Decentralised systems security: network
7. Automated trust negotiation
8. Privacy in big data: introduction
9. Privacy in big data: privacy-preserving data mining
10. Cryptography for big data: advanced topics
11. Secure data sharing/outsourcing
12. Secure searching over big data
13. Securing big data in the cloud

Instructor:

Alexandros Artikis (NCSR Demokritos)

Topics per week

1. Complex event recognition. The notion of 'event'; event processing as a reactive decision-making paradigm.
2. The main functions of event processing; real-world applications.
3. Languages. Automata-based methods; temporal reasoning systems.
4. Multi-scale temporal aggregation of events. Multi-scale window specification.
5. Complex event recognition using streaming and historical data.
6. Uncertainty handling. Uncertainty types; uncertainty elimination vs uncertainty propagation
7. Markov Logic Networks and probabilistic programming.
8. Event Pattern Learning. Weight learning; discriminative estimation; generative estimation; structure learning.
9. Hybrid logic programming; incremental learning.
10. Optimization. Optimization dimensions; code optimization and state management; event pattern rewriting.
11. Distribution. Communication efficiency; computation efficiency.
12. Distributed tracking of simple linear functions; geometric approach.
13. Communication cost minimization; latency, memory, energy minimization.

Instructors

Iraklis Klampanos (NCSR Demokritos), Anastasia Krithara (NCSR Demokritos)

Topics per week

1. Introduction to deep learning and indicative examples
2. Revisiting ML basics
3. Deep feedforward networks
4. Regularisation for deep learning
5. Training deep neural networks
6. Convolutional networks
7. Recurrent and recursive networks
8. Linear factor models
9. Unsupervised learning and autoencoders
10. Representation learning
11. Approximate inference
12. Example use-case
13. Practical issues and methodology

4th Semester

MSc Data Science Thesis

The MSc in Data Science is primarily designed for Science and Engineering professionals and graduates.
Candidates who have not yet completed their studies, but are expected to have graduated by the start of the Program in September 2017, can also apply. All relevant certificates will need to be submitted at the time of registration.

Necessary qualifications

  • Relevant Science or Engineering University degree, preferably in Computer Science, Mathematics, Statistics, Finance, Economics, Operations Research or equivalents.
  • Excellent command of the English language

Recommended skills and qualifications

  • Experience in working with data
  • Programming experience
  • Statistics background
  • Basic data analysis experience

How to apply

Candidates are invited to submit their application by Friday 14 July 2017 electronically via the address https://msc-data-science.uop.gr/apply/.

For completing your application you will need:

  • Detailed curriculum vitae (CV) in English
  • Copies of all diplomas/ university degrees received.
  • Transcripts of grades in Greek or English
  • Copy of identity card
  • Candidate’s photograph
  • Two recommendation letters (Candidates may also provide the contact details of the persons who are willing to provide them a recommendation letter)
  • Proof of knowledge of the English language. (Certificate of Proficiency in English from University of Michigan/Cambridge, TOEFL, IELTS or other equivalent)
  • Scientific publications and distinctions (if any)
  • Proof of professional experience (if any)
Data Science-related graduate destinations Data Science-related graduate roles Further study and Research destinations
  • Data management firms
  • Statistical/data analysis firms
  • Content management software firms
  • International security organizations
  • Data and text mining companies
  • Media
  • Data Analyst
  • Big Data Architect
  • Bioinformatician
  • SQL Developer
  • Data Scientist
  • Business Analyst
  • Upon successful completion of the MSc program graduates will be able to apply for relevant positions in research projects, announced regularly by the Institute of Informatics & Telecommunications. Those graduates wishing to contrinue their studies for a PhD Diploma will also have the chance to apply for the positions announced in the context of the joint PhD programs of the Institute with Universities in Greece, Europe and the USA. For more information on the Institute's joint PhD programs, see the Joint PhD programs / Research Collaborations webpage.

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  • What are the deadlines for my application?

The registration deadline for this Degree is Friday 14 July 2017. You should submit your online application in full by 23:59.

Please allow sufficient time to complete your online application. Only applications that are complete by the deadline can be considered by the admissions team. All applications must be made through the online application system available here.

  • What are the start and end dates for the academic year?

Start date is 1 October 2017. End date is 30 June 2018.

  • What is the duration of the Degree?

This Degree is designed to run for two (2) years full time.

  • What is the cost of the tuition fees for the Degree?

Tuition fees for all students (Domestic and International) amount to 3.000 euros in total for both years of their study.