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    Statistical Methods in Data Science is a two-semester course aimed at providing the fundamental tools for: setting up probabilistic models; understanding the basic principles of the main inferential problems: estimation, hypothesis testing, model checking and forecasting; understanding and contrasting the two main inferential paradigms, namely frequentist and Bayesian statistics; implementing inference on observed data through both optimization and simulation-based approximation techniques such as: Bootstrap Monte Carlo Markov Chain MCMC understanding comparative merits of alternative strategies developing statistical computations within a suitable software environment like R www.

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    Knowledge and understanding On successful completion of this course, students will: know the main statistical principles, inferential problems, paradigms and algorithms; assess the empirical and theoretical performance of different modeling approaches; know the main platforms, programming languages to develop effective implementations.

    Applying knowledge and understanding Besides the understanding of theoretical aspects, thanks to applied homeworks and a dedicated laboratory in the second semester focused on Bayesian modeling, students will be constantly challenged to use and evaluate all the techniques they have learned as well as to propose new modelization suitable for specific tasks at hand.

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    Making judgements On successful completion of this course, students will develop a positive critical attitude towards the empirical and theoretical evaluation of statistical methodologies and results. Communication skills In preparing the report and oral presentation for the final project of the second semester laboratory, students will learn how to effectively communicate information, ideas, problems and solutions to specialists but also to a general audience.

    Learning skills In this course the students will develop the skills necessary for a successful understanding and application of new statistical methodologies together with their effective implementation.

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    The goal is of course to grow an active attitude towards continued learning throughout a professional career.