Elective Courses
Elective Courses

Understanding knowledge graphs and their relation with large language models. Designing the knowledge graph schema. Populating the knowledge graph. Managing knowledge graph quality and evolution. Applying the knowledge graph.

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Data-oriented techniques for extracting patterns from data. Association rules, decision trees. Collaborative filtering and recommendation algorithms Finding similar items and frequent items. Mining data streams. Mining social network graphs. Mining for Web advertising. Implementing machine learning schemes.

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Bayesian inference. Simulation and random number generation. Markov models and hidden Markov models. Probabilistic graphical models. Bayesian statistical methods, Markov chain Monte Carlo, Metropolis-Hastings algorithm, Gibbs sampling, sequential Monte Carlo methods, approximate Bayesian computation.

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Distributed and parallel data-oriented computation and transaction processing. Integration and management of large scale structured and unstructured data in different information systems environments.

Cloud services, engineering issues, stream processing, graph processing, Cassandra, Dremel, Pregel, Storm, parallel data mining systems (Graph Lab, Mahout).

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Small n large p problems, regularizations, model and variable selection techniques, LASSO, elastic net. Multiplicity. Graphical Models. Techniques for sparse matrices and graphical LASSO. Compressed sensing.

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Basic principles, autocorrelation and autocovariance, Holt-Winters method, AR, ARMΑ, ARIMA models. Regression models, ARCH – GARCH, volatility models.

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Convex and semidefinite optimization (Convex sets and functions, Problems, duality, unconstrained and constrained minimization), Combinatorial optimization (Branch and bound, tabu search, Simulated annealing), Multivariate function optimization (e.g. gradient descent). Linear Programming (Formulations, Algorithms).

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Overview of data mining techniques for sales and marketing: clustering, classification, dimensionality reduction, sequence modeling. Techniques for Customer Segmentation. Churn management. Cross-/Up-sell Campaign Targeting. Next Best Action. Marketing Mix optimization. Omni-Channel Optimization. Loyalty Analytics. Basket Analysis.

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Introduction to epidemiological methods: bias, confounding, sample size. Survival analysis: hazard functions, parameter inference. Methods for categorical data. Analysis of contingency tables, risk assessment in retrospective and prospective studies

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Text vocabulary, automatic indexing, inverted files, fast inversion algorithm, index compression. Evaluation of information retrieval systems. Information retrieval models (Boolean model, vector space model, probabilistic retrieval model), latent semantic indexing. Computing scores, result ranking. Crawling. Link analysis. Search engine architecture and systems issues.

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Neighborhood-based collaborative filtering. Model-based collaborative filtering. Content-based recommender systems. Knowledge-Based recommender Systems. The cold-start problem. Direct vs implicit signals.

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Introduction to the DS methods including data preprocessing, feature selection & engineering, machine learning, graph/text mining and visualization. Introduction to a specific data challenge and its domain specificities presented as a Kaggle competition. The best solutions are presented to the class.

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Basic financial instruments and associated fundamental concepts: time value of money, interest rates and fixed income securities; Simple derivatives: Futures, Forwards and Interest Rate Swaps; Options and the Black-Scholes framework. Statistical measures and error metrics of different distributions. Value at Risk (VaR), Expected Shortfall; Methodologies for VaR calculation; Credit risk and the Basel II capital requirements.

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This course provides a broad introduction to the theory and empirical analysis of advanced econometric models to financial applications such as optimal portfolio construction, performance evaluation and forecasting financial time series. The course introduces the class of multivariate multifactor models, which can be used to model the expected returns of financial time series, and the class of multivariate heteroskedastic models, which can be used to model the covariances/correlations of financial returns. Illustrative examples applying these advanced econometric models/techniques to actual financial data are also presented using the statistical package R. The empirical analysis consists of (a) constructing optimal portfolios of financial series, e.g. stocks and/or indices, (b) evaluating the performance of different mutual fund or hedge fund investments, (c) forecasting financial series e.g. stock returns.

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Business Intelligence has evolved from a niche area for large enterprises and organizations to an essential infrastructure for all business entities, regardless size. The ability to integrate, analyze and aggregate large amounts of data in a simple and efficient manner became a necessity in the last decade or so. This course will present the motivation behind this field and discuss goals, design principles, querying approaches, processing techniques, systems, tools and applications. In addition, in the last few years, given the importance of business intelligence, specific systems have been designed to handle data warehousing, such as column-oriented and main-memory databases.

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Deep Learning has gained significant attention over the past few years leading to state-of-the-art methods for several tasks concerning text and image processing (e.g., machine translation, object recognition in images, etc.). The course will be a mix of theory and practice, covering the basic theory and more advanced deep learning techniques while also providing examples of how to build deep neural networks in practice with popular Python tools, e.g., Keras, Tensorflow, etc.

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This course covers data analysis on social networks, focusing on ways to handle large-scale networks efficiently. It provides the main theoretical results in social network mining as well as hands-on practice on key issues in the area.

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MSc in Data Science is ranked 12th globally in the Eduniversal rankings. https://www.kathimerini.gr/society/562985167/

INDUSTRY PARTNERS

The Master of Science in Data Science has a large and growing number of Industry Partners, offering industrial thesis topics, career days, seminars and short courses, and scholarships, among other forms of collaboration. We are grateful to the Greek industrial community for supporting the program.

AUEB - MSc in Data Science