Course Topics
This is a second course in statistics covering selected topics in statistical inference, time series, and computational statistics. Topics include the method of moments, maximum-likelihood and Bayesian estimation, Monte Carlo methods and the bootstrap, and likelihood ratio testing. ARMA and regression modelling for time-series data and forecasting. Techniques for dealing with missing data. The course alternates front classes and lab activity, where the methodologies discussed are applied to real and simulated data. This course, by combining theory, computer simulations, and applications, aims to provide a deep understanding and operational knowledge of core techniques in statistical analysis that can be applied to both applied data analysis and theoretical research.

Teaching format
Frontal lectures, discussions and exercises on computer.

Educational objectives
The course belongs to the type "affini o integrative – formazione affine" in the curriculum “Data Analytics”. This course, by combining theory and computer simulations and applications, aims at providing deep understanding and operational knowledge of some core techniques of statistical analysis which can be exploited either for applied data analysis or theoretical research. Knowledge and understanding: • D1.1 - Knowledge of the key concepts and technologies of data science disciplines • D1.8 - Knowledge of the mathematical-statistical principles required for data analysis Applying knowledge and understanding: • D2.1 - Practical application and evaluation of tools and techniques in the field of data science • D2.2 - Ability to address and solve a problem using scientific methods • D2.7 - Practical application of mathematical-statistical tools and methods from the field of data science Making judgments • D3.2 - Ability to autonomously select the documentation (in the form of books, web, magazines, etc.) needed to keep up to date in a given sector Communication skills • D4.1 - Ability to use English at an advanced level with particular reference to disciplinary terminology Learning skills • D5.3 - Ability to deal with problems in a systematic and creative way and to appropriate problem solving techniques.

Assessment
The assessment is based on class and lab participation, homework exercises and a final written exam. The final written exam will include open-ended questions and exercises for students to work out, as well as computational exercises to be solved using tools and programming languages of their choice, e.g., R, Python, MATLAB.

Evaluation criteria
The final grade will be determined by the homework evaluation (30%) and the final written exam (70%). A 2-hour and 30-minute written examination covering all course topics, comprising exercises (to be solved with or without the use of programming languages) and theoretical questions.

Required readings

Randall Pruim, 2018, Foundations and Applications of Statistics: An Introduction Using R. American Mathematical Society, Providence. ISBN 9781470428488. From this book, we discuss topics from chapters 4 and 5.

 

Robert Shumway and David Stoffer, 2019. Time Series: A Data Analysis Approach Using R. CRC Press, Boca Raton. ISBN 9780367221096. From this book, we discuss chapters 1 to 4 and some optional topics from chapters 5 and 8.

 

Subject Librarian: David Gebhardi, David.Gebhardi@unibz.it

Supplementary readings

The lecturer will provide additional materials and readings in class.

Further information
Software used: Students are free to use tools and programming languages of their choice, e.g., R, Python, and MATLAB, for the homework and the final written exam.