ID:
SCV0879
Duration (hours):
48
CFU:
6
SSD:
ZOOLOGIA
Year:
2025
Overview
Date/time interval
Primo Semestre (23/09/2025 - 17/01/2026)
Syllabus
Course Objectives
The course is aimed at giving basic theoretical and practical knowledge to efficently collect, organise and extract knowledge from data sets, structured or not, in a multidisciplinary context.
Particular emphasis will be given to data classes and sources typical of biological and environmental contexts.
Topics covered will include the basic theoretical data organization and analysis frameworks, with particular reference to data sources related to natural resources mamagement as well as productive context linked to natural resources use.
At the end of the course it is expected that students will acquire the following skills:
- knowledge of the fundamentals processes and paradigms in data nanagement;
- familiarity with the technologies and methodologies presently available to produce and share knowledge from data;
- ability in using data management software autonomously and efficiently.
Particular emphasis will be given to data classes and sources typical of biological and environmental contexts.
Topics covered will include the basic theoretical data organization and analysis frameworks, with particular reference to data sources related to natural resources mamagement as well as productive context linked to natural resources use.
At the end of the course it is expected that students will acquire the following skills:
- knowledge of the fundamentals processes and paradigms in data nanagement;
- familiarity with the technologies and methodologies presently available to produce and share knowledge from data;
- ability in using data management software autonomously and efficiently.
Course Prerequisites
There are no required propaedeutic courses. General requirements include basic knowledge on Personal Computer use, basic mathematics and analytical geometry, base concepts in Botany, Zoology and Ecology. Knowledge of English language is recommended, since most of the technical documentation is in English.
Teaching Methods
The course consists of 48 hours of lectures (6 ECTS credits) of active "hands-on" lectures in computer laboratory, all taught by the teacher in charge, to sustain theoretical concepts with real-world case studies and applications. Course attendance is recommended, but not mandatory: at that purpose, all the materials (slides, data sets, etc.) used are made available on the e-learning platform.
Classroom activities in computer laboratory, although not mandatory, are deemed as fundamental to achieve the capacity of organizing and analyzing a coherent data set.
The teacher will be available at students' request at the beginning of each lesson to explain concepts and topics exposed in the previous lessons. Students are encouraged to interact with the teacher asking for clarifications when felt necessary at any time.
All the materials used for practical activities (i.e. in the interactive parts in computer laboratory) will be made available not only for an autonomous use in preparation of the final exam, but also to support students unable to participate to all the classes.
Classroom activities in computer laboratory, although not mandatory, are deemed as fundamental to achieve the capacity of organizing and analyzing a coherent data set.
The teacher will be available at students' request at the beginning of each lesson to explain concepts and topics exposed in the previous lessons. Students are encouraged to interact with the teacher asking for clarifications when felt necessary at any time.
All the materials used for practical activities (i.e. in the interactive parts in computer laboratory) will be made available not only for an autonomous use in preparation of the final exam, but also to support students unable to participate to all the classes.
Assessment Methods
The final test is an oral examination. During the course, some selected case studies will be proposed to the students as intermediate assignments, whose result will contribute to the formulation of a final scora. During the oral examination, the student will answer and elaborate on some questions (one of which on a subject of choice) based on the course syllabus, both on general topics and on specific methodologies.
The final examination is aimed at assessing the achievement of the learning objectives defined above, first evaluating the comprehension of basic general concepts, then delving further into the ability to use them to solve real-world issues.
The final score (expressed in marks out of 30) will be based on comprehension, capacity to apply theoretical concepts to real cases, autonomy of judgement, and communication skills.
The final examination is aimed at assessing the achievement of the learning objectives defined above, first evaluating the comprehension of basic general concepts, then delving further into the ability to use them to solve real-world issues.
The final score (expressed in marks out of 30) will be based on comprehension, capacity to apply theoretical concepts to real cases, autonomy of judgement, and communication skills.
Contents
The course will cover the following topics: Principles of scientific programming: communicating with the computer to solve data analysis problems. Introduction to the R language and the RStudio (or Posit) environment: familiarization with the interface, configuration (font, line wrapping, spaces instead of tabs, non-fatiguing color scheme), and management of installations in laboratory environments with proxies. Consulting documentation: using `?command` and `??search`. Data management and manipulation: basic techniques for importing, exporting, and "cleaning" data (data wrangling). Data normalization: concept of data tidiness, elimination of redundancies, and management of anomalies (insertion, modification, deletion). Normal forms (1NF, 2NF, 3NF, BCNF, 4NF, 5NF, DKNF) will be illustrated, along with concepts of "long format" and "wide format", and data pivoting techniques. Data analysis: basic approaches: how a measure varies, how it changes between groups, relationships between measures, causal effects. Data aggregation: subdividing data into groups (categorical, by interval, time-based). Aggregation functions and descriptive statistics: count, sum, mean, median, standard deviation, interquartile range. Exploratory Data Analysis (EDA) and graphical analysis. Grammar of Graphics (ggplot2): description of plot construction as a summation of elements (data, axes, scales, panels, mappings, modifiers). Examples of `ggplot` usage. Hypothesis testing: t-test, ANOVA, Kruskal-Wallis for group comparisons. Contingency table analysis, chi-square test, Poisson regression for count data. Literate programming: use of "notebooks" (R Markdown) to document code and analysis. Commonly used R packages: `foreign`, `readxl`, `ggplot2`, `circular`, `nlme`, `rggobi`, `ade4`/`vegan`. Tidyverse: `dplyr`, `tidyr`.
Course Language
Italian (textbooks in English)
More information
The teacher in charge is always available, subject to the arrangement of an appointment by e-mail.
The use of the e-learning patform (forums, glossaries) is highly recommended to share among students any requested issue.
The use of the e-learning patform (forums, glossaries) is highly recommended to share among students any requested issue.
Degrees
Degrees
BIOLOGY AND SUSTAINABILITY
Master’s Degree
2 years
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People
Docenti di ruolo di Ia fascia
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