B5578 - WORKSHOP ON DATA SCIENCE B

Learning outcomes

Workshops are designed to provide students with transversal and multidisciplinary skills that can prove useful in their future careers. At the end of the course, students have basic knowledge about Data Mining and have experienced a programming language to write simple data analysis programs.

Course contents

This workshop propose students a wide overview of the Data Science discipline. It focuses on the processing techniques for Exploratory Data Analysis for structured data sets and on the machine learning prediction task of Classification.

More in details, the course contents are:

• Introduction to the Principles of Artificial Intelligence
  Foundations of learning from data: supervised and unsupervised learning.
  From statistical learning to machine learning, deep learning, and generative models.
  Focus on generative language models and emerging sociological challenges in the use of AI.
  
  
• Data Analysis in the Context of AI
  What is Data Analysis?
  Types of data and data structures.
  Understanding and managing datasets.
  
  
• Exploratory Data Analysis
  Descriptive statistics: central tendency and dispersion.
  Introduction to data distributions and their interpretation.
  
  
• Classification
  Understanding the supervised learning workflow with training/testing.
  The decision tree algorithm for classification.
  Confusion matrix, accuracy, precision, recall, and other performance metrics.
  

• Introduction to programming in R
  Getting started with R and RStudio.
  Understanding data types, data frames, and basic syntax.
  Working with packages for data analysis and visualization.
  Implementation of R scripts with specific libraries for data visualization.

Topics will be introduced theoretically but also verified in R-based softwares during the laboratory hours.

ATTENTION: the class attendance is mandatory.

 

Students with DSA or temporary or permanent disabilities:

It is recommended to contact the responsible University office in good time (https://site.unibo.it/studenti-con-disabilita-e-dsa/it ): it will be their responsibility to propose any adaptations to the students concerned, which must however be submitted, with a 15-day notice, to the approval of the teacher, who will evaluate the opportunity also in relation to the educational objectives of the course.

 

Readings/Bibliography

Slides by the teacher

ROBERT, I., et al. "R in action: data analysis and graphics with R". 2011.

Teaching methods

Lectures and computer lab sessions.

Assessment methods

The final assessment consists of two phases:

1. Multiple-choice quiz: The quiz includes 20 questions covering the entire course program. Passing the quiz (minimum score 13/20) is necessary to proceed to the next phase.

2. Project evaluation: Students must complete a project in which they analyze data using the R programming language. The project will be positively evaluated if it meets the criteria established in the project guidelines (discussed during the class hours) and demonstrates proficiency in using R and understanding the results obtained.

At the end of the workshop, a pass/fail qualification will be recorded (idoneità), rather than a grade expressed on a 30-point scale.

Teaching tools

Slides by the teacher and R scripts developed during the course

Office hours

See the website of Elena Morotti