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.
Program for the exams of the 1st and 3rd semester - academic year 2018-2019
Posted: 4 months 6 days ago
| Classes | |
|---|---|
| Monday | Data Management 18:00-21:00 |
| Tuesday | Machine Learning 18:00-21:00 |
| Wednesday | Data Programming 18:00-21:00 |
| Thursday | Large-scale statistical methods 18:00-21:00 |
| Classes | |
|---|---|
| Monday | Natural Language Analytics 18:00-21:00 |
| Tuesday | Data Visualization 18:00-21:00 |
| Wednesday | Big Data Management 18:00-21:00 |
| Thursday | Big Data Mining 18:00-21:00 |
| Classes | |
|---|---|
| Monday | Multi-Modal Information Processing and Analysis 18:00-21:00 |
| Tuesday | Advanced Big Data Mining 18:00-21:00 |
| Wednesday | Applied Data Science 18:00-21:00 |
| Thursday | Deep Learning 18:00-21:00 |
| Friday | Big Data Security, Privacy and Trust 18:00-21:00 |
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?
This Degree is a two-year-long, full time programme consisting of three semesters of classes and one semester allowed for Thesis writing.
The MSc in Data Science is structured so as to accommodate professionals, with evening classes taking place at NCSR "Demokritos" campus in Agia Paraskevi, Athens, Greece.
It is also ideal for graduate students of engineering, mathematics, statistics, finance, economics, operations research, and computer science schools who want to further their education and improve their skills.
Where will the courses take place?
All classes are held in English (including the Diploma Thesis) and attendance takes place in the evenings 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 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.
This Degree is a two-year-long, full time programme consisting of three semesters of classes and one semester allowed for Thesis writing.
The MSc in Data Science is structured so as to accommodate professionals, with evening classes taking place at NCSR "Demokritos" campus in Agia Paraskevi, Athens, Greece.
It is also ideal for graduate students of engineering, mathematics, statistics, finance, economics, operations research, and computer science schools who want to further their education and improve their skills.
Spiros Skiadopoulos (UoP), Christos Tryfonopoulos (UoP)
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
Ioannis Moscholios (UOP)
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
Anastasia Krithara (NCSR Demokritos), George Petasis (NCSR Demokritos)
1. Different types of learning algorithms:
- Supervised learning
- Unsupervised learning
2. Basic machine learning algorithms:
- Linear Regression
- Decision Trees
- Logistic Regression
- KNN (K- Nearest Neighbours)
- K-Means
- Hierarchical clustering
- Naïve Bayes
- Support Vector Machines
- Dimensionality Reduction
3. Ensembles:
- Voting
- Random Forests
- AdaBoost
- XGBoost
4. Applied Machine learning:
- Exploratory Analysis
- Data Cleaning/Data Wrangling
- Feature Engineering
- Feature selection
- Algorithm selection
- Model training
- Model evaluation
Iraklis Klampanos (NCSR Demokritos)
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
Christos Tryfonopoulos (UoP), Spiros Skiadopoulos (UoP)
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
Nikos Platis (UoP), Iraklis Klampanos (NCSR Demokritos)
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
Anastasia Krithara (NCSR Demokritos), George Petasis (NCSR Demokritos)
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
George Giannakopoulos (NCSR Demokritos)
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
George Giannakopoulos (NCSR Demokritos), Alexandros Nousias (NCSR Demokritos)
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
Theodoros Giannakopoulos (NCSR Demokritos)
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
Nikos Kolokotronis (UoP), Konstantinos Limniotis (UoP)
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
Alexandros Artikis (NCSR Demokritos), Nikos Katzouris (NCSR Demokritos)
1. Introduction to complex event recognition.
2. Case study: complex event recognition for maritime monitoring.
3. Complex event recognition languages.
4. Automata-based event recognition.
5. Temporal reasoning systems.
6. Big Data complex event recognition.
7. Uncertainty handling.
8. Probabilistic programming.
9. Markov Logic Networks.
10. Complex event pattern learning.
11-12. Online learning over relational streams.
13. Complex event forecasting.
Iraklis Klampanos (NCSR Demokritos), George Petasis (NCSR Demokritos)
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
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 2018, can also apply. All relevant certificates will need to be submitted at the time of registration.
Necessary qualifications
Recommended skills and qualifications
How to apply
Candidates are invited to submit their application by Friday 22 June 2018 electronically via the address https://msc-data-science.uop.gr/apply/.
For completing your application you will need:
| Data Science-related graduate destinations | Data Science-related graduate roles | Further study and Research destinations |
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Relevant Articles about the Data Market:
The registration deadline for this Degree is Friday 22 June 2018. 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.
Start date is 1 October 2018. End date is 30 June 2019.
This Degree is designed to run for two (2) years full time.
Tuition fees for all students (Domestic and International) amount to 3.000 euros in total for both years of their study.
© National Centre for Scientific Research "Demokritos" for the Institute of Informatics and Telecommunications and University of the Peloponnese for the Department of Informatics and Telecommunications 2017. Contents of the "MSc in Data Science" website may be copied freely for non-commercial research and study.
