99107 - SCIENZA DEI DATI PER L'AMBIENTE

Learning outcomes

Knowledge of the main topics of analysis and modelling of univariate, bivariate and multivariate data.

Course contents

Prerequisites

Fundamentals of statistics and probability theory.

Program

Elements of data engineering

• Big data
• Collection of raw data
• Data transformation
• Data sharing (database, data lake, data warehouse)

Descriptive statistics

• Representation of data in summary form (tables, graphs)
• Sorting and distribution of data
• Covariance, covariance matrices, and correlation
• Dimensionality reduction of data (singular value decomposition, principal component analysis, factor analysis)
• Recognition of implicit relational structures among data

Learning methods

• Parametric supervised learning: linear regression
• Non-parametric supervised learning: classification
• Non-parametric unsupervised learning: clustering
• Machine Learning (ML)
• Artificial Neural Networks (ANN)
• Genetic Algorithms (GA)

Elements of inferential statistics

Fundamentals of programming in R

Anonymous statistical survey

Once two-thirds of the lessons have been completed, a statistical survey will be conducted to gather students’ opinions about the course in order to make it more effective. Reference sites:

• https://opinionistudenti.unibo.it
  
• https://val.unibo.it/
• https://val.unibo.it/demo.php
• https://gestioneval.unibo.it

Calendar

1. 18/10/2024 9:00-13:00
2. 25/10/2024 9:00-13:00
3. 08/11/2024 9:00-13:00
4. 15/11/2024 9:00-13:00
   
5. 22/11/2024 9:00-13:00
6. 29/11/2024 9:00-13:00
   
7. 06/12/2024 9:00-13:00
   
8. 13/12/2024 9:00-13:00
   
9. 20/12/2024 9:00-13:00
10. 08/01/2025 9:00-13:00
11. 10/01/2025 9:00-13:00
12. 13/01/2025 9:00-13:00
    
13. 15/01/2025 9:00-13:00
    
14. 17/01/2025 9:00-13:00

Readings/Bibliography

• Data Science e Machine Learning: dai Dati alla Conoscenza
  Michele di Nuzzo
• Modern Statistics with R From Wrangling and Exploring Data to Inference and Predictive Modelling
  Måns Thulin
• Data Science. Guida ai Principi e alle Tecniche Base della Scienza dei Dati
  Sinan Ozdemir
• Statistica per Data Science con R
  Enrico Pegoraro
• R for Data Science
  Garrett Grolemund - Hadley Wickham
• Metodi Statistici per la Sperimentazione Biologica
  Alessandro Camussi, Frank Möller, Ercole Ottaviano, Mirella Sari Gorla
  Zanichelli, II edizione, 1995

Teaching methods

The course consists of 6 CFU divided into two modules:

• Theory module, 4 CFU, 32 hours
• Laboratory module, 2 CFU, 24 hours

The lesson typically lasts 4 hours and includes a theoretical part followed by numerical exercises and practical exercises using the students' computers, so as to become familiar with some of the methods underlying the subject.

The software used is R, and each student must install this program on their computer before the start of the lessons.

The program is free and available for the main operating systems:

• Linux
• MacOS (also install XQuartz to enable the graphical interface)
• Windows

It is also required to install the latest version of:

• Pandoc

Optional installation:

• RStudio

Classroom lectures and practical computer sessions.

As concerns the teaching methods of this course unit, all students must attend Module 1, 2 on Health and Safety online: 

https://www.unibo.it/en/services-and-opportunities/health-and-assistance/health-and-safety/online-course-on-health-and-safety-in-study-and-internship-areas

Assessment methods

The assessment of learning aims to verify the acquisition of both the expected theoretical knowledge and practical skills.

The student will have to present a program written in R language for the analysis of a data set agreed upon with the instructor, which will serve as the basis for discussing the topics covered in class.

The final grade reflects an evaluation of the content expressed during the final exam.

Teaching tools

Blackboard (lectures and exercises) and video-projector.

Computational laboratory practicals

Lecture notes (institutional website).

Students who need compensatory tools because of disabilities or specific learning disorders (SLD) can contact:

• https://site.unibo.it/studenti-con-disabilita-e-dsa/en

to agree on the adoption of the most appropriate measures.

Office hours

See the website of Assimo Maris