90909 - Workshop 2 (WS4)

Learning outcomes

The course aims at introducing students to the utilization of one of the main statistical packages for data analysis and presenting to them the basic elements of micro-data management and analysis. By the end of the course students will be familiar with the statistical package interface and be able to: load different types of data and different file formats into the statistical package, perform basic data management operations and conduct monovariate and multivariate statistical analyses using the software introduced during the course.

Course contents

WS4: BIG DATA TECHNIQUES WITH R - part I

This workshop presents the Data Mining workflow and it focuses on the machine learning techniques for classification, that is a widely used tool exploited in many applications (such as Text Mining).

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

More in details, the course contents are:

• Introduction to Data Mining;
• Introduction to programming in R;
• Presentation of classification techniques for Big Data;
  
• Implementation of R scripts to classify input data;
• Presentation of some case studies and applications.

Part II is in WS7 (not mandatory).

Readings/Bibliography

Slides by the teacher

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

Teaching methods

Lectures and Laboratory lessons (in presence, hopefully)

Assessment methods

Evaluation of a final written report.

Teaching tools

Slides and script files by the teacher

Office hours

See the website of Elena Morotti