AIMS AND CONTENT

LEARNING OUTCOMES

The main objective of the module is to provide students with basic knowledge and tools for data management and analysis.

At the end of the course, students will be able to:

Understand the basic concepts of data science and the role of data in scientific contexts.
Become familiar with the main types of data and know how to organize and prepare them in Excel.
Use basic and advanced Excel functions to analyze datasets (filters, formulas, pivot tables).
Create charts and tables to summarize data.
Interpret the results of analyses to draw useful conclusions.

AIMS AND LEARNING OUTCOMES

The learning objectives that will be evaluated for the purpose of passing are summarized in the following scheme:

Knowledge and understanding: Knowledge of the main tools for the synthesis and presentation of data, through the acquisition of the main techniques of descriptive statistics.

Ability to apply knowledge and understanding: Ability to use the appropriate techniques based on the type of data under analysis; be able to carry out basic descriptive analyzes for univariate and bivariate phenomena using the main summary indices; know how to read statistical analyses carried out with the methodologies presented in the course.

Making judgements: Be able to understand and comment on the results obtained from statistical analyses in practical examples based on the context of the application, thus being able to use the results in decision-making processes.

Communication skills: Acquire the basics of technical statistical language to communicate clearly and without ambiguity with both statisticians and non-statisticians.

Learning skills: Be able to correctly read the results of statistical analyses, also in contexts of greater complexity than those presented in the course.

PREREQUISITES

None.

TEACHING METHODS

Blended teaching, with on-site and online classes.

SYLLABUS/CONTENT

Introduction to statistics. Descriptive and inferential statistics. Populations and samples.
Distributions, frequencies, and cumulative frequencies. Graphs to describe categorical variables, time series, and quantitative variables. Graphs and tables to describe relationships between variables.
Location measures: mean, median, mode, percentiles.
Variability measures. Box-plot. Measures of  concentration.
Measures of relationships between variables.
Linear regression.

RECOMMENDED READING/BIBLIOGRAPHY

Newbold, Carlson, Thorne, Statistica. Nona edizione. Pearson (2021). Foreign students can refer to the original version of this book. For a topic not covered by the textbook, documentation will be provided in Aulaweb by the teacher.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

https://corsi.unige.it/en/corsi/11758/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The partial exam for the open badge consists of a written/practical test with multiple-choice questions and some exercises to be solved using Excel.

ASSESSMENT METHODS

The multiple-choice questions are designed to assess the knowledge and understanding of the topics covered in class, while the exercises are intended to evaluate the student's ability to apply the methods learned.

FURTHER INFORMATION

Working students and students with DSA certification, disability or other special educational needs are advised to contact the teacher at the beginning of the course to agree on teaching and exam methods that, in compliance with the teaching objectives, take into account individual learning methods.