Elective Courses
Elective Courses

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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MSc in Data Science is ranked 13th globally in the Eduniversal rankings

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