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Programme of Course "Data Analytics and Data Driven Decision"

The course is composed by the following modules: "Data Analytics"   "Data Driven Decision"  



Type of course unit:

Master Degree in Computer Science curriculum NEDAS: Compulsory
Master Degree in Computer Science curriculum SEAS: Elective
Master Degree in Computer Science curriculum UBIDIS: Elective

Level of course unit:

Postgraduate Degrees


Module Data Analytics: 2° semester
Module Data Driven Decision: 2° semester

Number of credits:

Master Degree in Computer Science: 6 (workload 150 hours)


Giovanni Felici (giovannidotfeliciatiasidotcnrdotit)
Fabrizio Rossi (fabriziodotrossiatunivaqdotit)

1. Course Objectives

Module Data Analytics: Learn fundamental techniques to examine raw data with the purpose of drawing data-driven decisions.
Module Data Driven Decision: The 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.

2. Course Contents and learning outcomes (Dublin Descriptors)

Topics of the course include:

  • See Module "Data Analytics" and Module "Data Driven Decision"

On successful completion of this course, the student should

Module Data Driven Decision

  • 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

3. Course Prerequisites

Module Data Analytics: Basic programming skills, introductory statistic, linear optimization
Module Data Driven Decision: Basic programming skills, introductory statistic, linear optimization

4. Teaching methods and language

Module Data Analytics: Lectures and practical training
Module Data Driven Decision: Lectures and practical training


5. Assessment Methods

Module Data Analytics: Assignment
Module Data Driven Decision: Assignment

Course information last updated on: 20 dicembre 2016, 14:50