Course Unit Profile

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Basic Information

Course Unit Title: DATA MINING

Course Unit Code: 420AA

Level of course unit

Second Cycle (Laurea magistrale) Degree Programme in Business Informatics

Year of study

Second year

Semester when the course is delivered


Number of ECTS credits allocated: 12

Name of Lecturer(s):

Prof.: Dino Pedreschi

Prof.: Mirco Nanni

Language of instruction


General Information

Learning outcomes

… a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and storyteller/artist to extract the nuggets of gold hidden under mountains of data. Hal Varian, Google’s chief economist, predicts that the job of statistician will become the “sexiest” around. Data, he explains, are widely available; what is scarce is the ability to extract wisdom from them.

Data, data everywhere. The Economist, Special Report on Big Data, Feb. 2010.

The student develops basic capacities of data analytics and mining, and mastering the knowledge discovery process from various kinds of data. Key applications in socio-economic domains are extensively studied by hands-on projects.

Course contents

Fundamentals of the knowledge discovery process
Explorative Data Analysis
Visual analytics
Fundamental data mining techniques: patterns and rules, clustering, classification
Case studies in socio-economic domains (marketing and CRM, mobility and transport, public health, etc.)
Ethical issues of data mining, responsibility of the data scientist, and privacy

Specific Information

Prerequisites, co-requisites, as a prerequisite for further study


For this course the prerequisite/s is/are


Prerequisite for


Mode of delivery


face to face



Teaching methods

Learning activities

Recommended or required reading

Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Addison Wesley, ISBN 0-321-32136-7, 2006

See course wiki at

Assessment methods and criteria

Assessment methods

Assessment criteria

Work placement


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