Tipo Insegnamento:
Obbligatoria
Durata (ore):
60
CFU:
9
SSD:
STATISTICA
Sede:
BRESCIA
Url:
ECONOMIA E AZIENDA DIGITALE/comune Anno: 3
Anno:
2024
Dati Generali
Periodo di attività
Primo Quadrimestre (01/10/2024 - 22/01/2025)
Syllabus
Obiettivi Formativi
The formative goals are consistent with those of the BSc. in “Economics and Digital Firm”. At the end of the course, students will gain skills in the following domains:
(i) Knowledge and understanding: understand the main techniques of data visualization and data analytics and use the software to implement them; developing the ability of interpreting and commenting an output
(ii) Applying knowledge and understanding: apply these techniques to problems that are well grounded in a business context, in order to extract proper information from data, and translating knowledge into business decisions
(iii) Making judgements: use appropriate data analytics methods and understand how to interpret results and the output of statistical software. The course also aims to improve students’ critical skills through the discussion of some contemporary issues in big data and artificial intelligence
(iv) Communication skills: draft report of analyses in plain written English; communicate acquired knowledge to experts and non-experts, also by means of advanced visualization analytic tools
(v) Learning skills: students will augment their learning skills using a mixture of resources and tools made available during the course like books, online materials, scripts, demos. They will exercise themselves in integrating different sources of information, interpreting them critically and discovering new sources of knowledge
(i) Knowledge and understanding: understand the main techniques of data visualization and data analytics and use the software to implement them; developing the ability of interpreting and commenting an output
(ii) Applying knowledge and understanding: apply these techniques to problems that are well grounded in a business context, in order to extract proper information from data, and translating knowledge into business decisions
(iii) Making judgements: use appropriate data analytics methods and understand how to interpret results and the output of statistical software. The course also aims to improve students’ critical skills through the discussion of some contemporary issues in big data and artificial intelligence
(iv) Communication skills: draft report of analyses in plain written English; communicate acquired knowledge to experts and non-experts, also by means of advanced visualization analytic tools
(v) Learning skills: students will augment their learning skills using a mixture of resources and tools made available during the course like books, online materials, scripts, demos. They will exercise themselves in integrating different sources of information, interpreting them critically and discovering new sources of knowledge
Prerequisiti
Courses in statistics in the study plan of the first two years of the B.Sc. program. Good knowledge of Excel and basic knowledge of the R software.
Metodi didattici
Lessons and hands-on (R lab)
Verifica Apprendimento
The assessment will be based on a comprehensive written examination (computer-based) made up of open questions (theory) and theoretical/practical exercises on all units of the course. This will also include the reproduction of some R code/function. This is not an open book examination, but selected code sources will be provided by the instructor and will be directly accessible in the examination room.
Testi
Suggested reading
• Wickham H., Grolemund G. R for data science. Sebastopol, CA: O’Really, 2017 (or subsequent edition)
Other readings
Unit 1
• Ismay C., Kim AY. Statistical inference via data science. Boca Raton, FL: CRC Press, 2020
• Wickham H., Advanced R. Boca Raton, FL: CRC Press, 2019
• Hanck C., Arnold M., Gerber A., Introduction to Econometrics with R., 2019. https://www.econometrics-with-r.org/ (Available for free online.)
Unit 2
• Elmasri R., Navathe SB. Fundamentals of database systems. Hoboken, NK: Pearson, 2015
• Hoffmeyer-Zlotnik JHP., Warner U. Harmonising demographic and socio-economic variables for cross-national comparative survey research. Dordrecht: Springer, 2014.
• Holmes DH. Big data. Oxford: Oxford University Press, 2017
• Boden MA. Artificial intelligence. Oxford: Oxford University Press, 2018
• Cornwell B. Social sequence analysis: Methods and applications. Cambridge: Cambridge University Press, 2015
General references for business statistics
• Freed N., Jones S., Bergquist T. Understanding business statistics. New York: Wiley, 2014
• Newbold, Paul, Carlson, W.L. & Thorne, B., Statistics for Business and Economics, 10th Global edition 2022
• Wickham H., Grolemund G. R for data science. Sebastopol, CA: O’Really, 2017 (or subsequent edition)
Other readings
Unit 1
• Ismay C., Kim AY. Statistical inference via data science. Boca Raton, FL: CRC Press, 2020
• Wickham H., Advanced R. Boca Raton, FL: CRC Press, 2019
• Hanck C., Arnold M., Gerber A., Introduction to Econometrics with R., 2019. https://www.econometrics-with-r.org/ (Available for free online.)
Unit 2
• Elmasri R., Navathe SB. Fundamentals of database systems. Hoboken, NK: Pearson, 2015
• Hoffmeyer-Zlotnik JHP., Warner U. Harmonising demographic and socio-economic variables for cross-national comparative survey research. Dordrecht: Springer, 2014.
• Holmes DH. Big data. Oxford: Oxford University Press, 2017
• Boden MA. Artificial intelligence. Oxford: Oxford University Press, 2018
• Cornwell B. Social sequence analysis: Methods and applications. Cambridge: Cambridge University Press, 2015
General references for business statistics
• Freed N., Jones S., Bergquist T. Understanding business statistics. New York: Wiley, 2014
• Newbold, Paul, Carlson, W.L. & Thorne, B., Statistics for Business and Economics, 10th Global edition 2022
Contenuti
Classes mainly consists of laboratory and hands-on activities, with a theoretical introduction, aimed to strengthen the students’ abilities in the field of computational statistics and data analytics. The course is organized in two main didactic units: (1) Fundamentals of data analytics for business and economics (40h); (2) An overview on some advanced techniques and contemporary debates (20h). For both units, the R software will be used; real examples and case studies in the field of economics and management will be provided. In Unit 1 students will be shown the main techniques of data visualization, interactive web apps, and data analytics in R, including reference to linear statistical models. Participants will be encouraged to think data-analytically, and to use descriptive statistical approaches to data analysis to support data-driven decisions. Unit 2 consists of a series of thematic seminars and short introductions (from a statistical and data analysis perspective) to: (a) database structure, SQL and NoSQL approaches; (b) principles of data harmonization, metadata creation, and databases integration for business statistics (Eurostat); (c) the contemporary debate on big data and artificial intelligence; (d) sequence and network analysis in economics. This Unit may also be propaedeutic to more advanced courses in data science that the students may choose in the MSc/MBA. Testimonials from national/international companies will be invited during the course.
Lingua Insegnamento
English
Altre informazioni
If possible, students should bring their own laptops with R and RStudio installed
Corsi
Corsi
ECONOMIA E AZIENDA DIGITALE
Laurea
3 anni
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