85302 - DATA SCIENCE

Conoscenze e abilità da conseguire

Al termine del corso, lo studente conosce i metodi correnti delle tecniche applicati ai data-science usando metodi e software computazionali moderni con una particolare enfasi sul ragionamento rigoroso in statistica. Lo studente è capace di rappresentare e organizzare le conoscenze riguardo a collezioni di dati su larga scala, trasformare i dati in informazioni pratiche usando concetti di "statistical learning" e " data mining" combinati con le tecniche di visualizzazione dei dati e di riproducibilità delle analisi dei dati.

Contenuti

Part I: Introduction to Statistical Learning

Part II: Data Visualization and Reporting

Part III: Supervised Learning

• Cross-Validation
• Naïve Bayes
• Logistic Regression;
  
• k-Nearest Neighbors;
• Nearest Shrunken Centroid;
• Regression and classification trees;
• Introduction to he Bootstrap;
• Bagging; Random Forests; Boosting.
  

Part IV: Unsupervised Learning

• k-means
• Hierarchical clustering
• Gap Statistic and clustering quality measures

Part V [Optional]: Overview of the main machine learning methods

• Support Vector Machines
  

Testi/Bibliografia

The primary text for the course:

• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to Statistical Learning. Second Edition. New York: Springer. ISBN: 978-1-0716-1417-4. E-book ISBN 978-1-0716-1418-1
  
  The book is freely available here:
  https://www.statlearning.com/

In addition, we will use:

• T. Hastie, R. Tibshirani, and J. Friedman (2001) The Elements of Statistical Learning: data mining, inference and prediction. Springer Verlag.
  Freely available at: https://hastie.su.domains/Papers/ESLII.pdf
  

Metodi didattici

Lectures and practical sessions.

Lectures complemented with practical sessions. As concerns the teaching methods of this course unit, all students must attend Module 1, 2 [http://www.unibo.it/en/services-and-opportunities/health-and-assistance/health-and-safety/online-course-on-health-and-safety-in-study-and-internship-areas] on Health and Safety online.

Modalità di verifica e valutazione dell'apprendimento

The learning assessment is composed by a written/practical test. The test is aimed at assessing the student's ability to use the learned definitions, concepts and properties and in solving exercises.

The exam consists of 5-10 questions, both multiple choice and open, some of which to be solved in R. The final grade is out of thirty. Students that, despite having passed the exam, do not feel represented by the obtained result can ask to have an additional (optional) oral exam (within at most 5-7 days) that can change the grade by +/-3 points. Please note: the difficulty of the oral questions will be calibrated based on the written exam grade. Questions will cover theoretical concepts and exercises from the entire syllabus.

During the written exam, students can only use the cheat sheet that is provided on virtuale.unibo.it, containing references to R packages and functions.

Students cannot make use of the textbook, personal notes, artificial intelligence tools nor mobile phones (smart watch or similar electronic data storage or communication devices are not allowed either and must be switched off before taking the exam).

Cheating or the use of unauthorized device is strictly prohibited. Any violation will result in the annulment of the exam and will be reported to the appropriate Division or Campus authorities in accordance with Article 48 of the University’s Code of Ethics.

To take the exam, students must register via the AlmaEsami platform. Students who do not register will not be allowed to take the exam.

Exams can only be taken during official sessions. No exceptions.

 

Students with learning disorders and\or temporary or permanent disabilities: please, contact the office responsible (https://site.unibo.it/studenti-con-disabilita-e-dsa/en/for-students) as soon as possible so that they can propose acceptable adjustments. The request for adaptation must be submitted in advance (15 days before the exam date) to the lecturer, who will assess the appropriateness of the adjustments, taking into account the teaching objectives.

 

Strumenti a supporto della didattica

The following material will be provided: slides of the lectures, exercises with solutions, mock exam.

Orario di ricevimento

Consulta il sito web di Laura Anderlucci