Course Details for A.Y. 2019/2020

The information on this page refer to the academic year 2019/2020. Therefore, several links are not available and data may be incomplete. To read the current information on the course, if it is still available, go to the university course catalogue . To read information relating other academic years, use the list at the bottom of this page.

A. Year:

2019/2020

Name:

Data Analytics and Data Driven Decision / Data Analytics and Data Driven Decision

Type:

Course

Basic information

Code:

DT0440

Sector:

MAT/09

Credits:

: Master Degree in Mathematics 6 CFU (c)
: Master Degree in Computer Science 6 CFU (c)

Term:

2^nd semester

Degree(s):

Master Degree in Computer Science curriculum GSEEM Elective
Master Degree in Computer Science 1^st anno curriculum NEDAS Compulsory
Master Degree in Computer Science 2^nd anno curriculum SEAS Elective
Master Degree in Computer Science curriculum UBIDIS Elective
Master Degree in Mathematics 1^st anno curriculum Generale Compulsory

Language:

English

Teacher(s):

Giovanni Felici
Fabrizio Rossi

Same as:

Data Analytics and Data Driven Decision
Data analytics and data mining
Data Driven Decision

Course Objectives

Learn fundamental techniques to examine raw data with the purpose of drawing data-driven decisions. The Data Driven Decision module deals with the main methods for supervised and non-supervised learning. Particular attention will be given to the statistical foundations of learning. The most established techniques to extract information from data to orient decisions will be treated both in their theoretical motivations and in their practical details. Open source tools will support the course step by step, providing continuous verification of the material.

Course Content

Introduction to analytics
Data collection, cleaning and preprocessing
Exploratory Data Analysis & Visualization
Statistical inference and regression models
Optimization formulations of data analysis and learning problems
Statistical foundations of learning
Clustering and other non-supervised methods
Decision trees - Logic methods
Support vector machines - Feature selection
Methods and tools for supervised and non-supervised learning

Learning Outcomes (Dublin Descriptors)

On successful completion of this course, the student should

know the main aspects and issues related with the content of the course
know how methods for non supervised learning work
know how methods for supervised learning work
know how to identify, among the methods considered, the one most suited for a given problem
being able to use software system that implement the methods studied

Prerequisites and Learning Activities

Basic programming skills, introductory statistic, linear optimization

Assessment Methods and Criteria

Assignment

Course page updates

This course page is available (with possible updates) also for the following academic years:

To read the current information on this course, if it is still available, go to the university course catalogue .

Course information last updated on: 20 settembre 2018, 14:41