Introduction to Data Mining

Language: English

Category: Computer and Data Sciences

Duration: 120 Hours

Target: Credits, Lifelong Learning

Attendance: Free

1097 Enrolled Students

Apr 22

2016

Not Set

Overview

The Introduction to Data Mining pathway teaches you how to use the data mining methodology to analyze both structured, semi-structured and un-structured data. The pathway consists of the following courses; Classification, Clustering and Association, and Text Mining. You will learn how to develop data mining workflows using the KNIME open source software platform. You are not required to code any programs while KNIME allows you to use open source programming languages and powerful commercial software environments; R, Weka, Matlab, Python, Java, ... and to access data from powerful platforms such as Twitter and Google.
Background and Requirements

Basic knowledge of probability, statistics, and mathematics.
Course sequence

You should attend the three courses of the Pathway in the following order: Classification, Clustering and Association, and Text Mining. However, if you know how to use the KNIME open source platform, have basic knowledge of the R programming language, then, no special order applies.
Evaluation and Certificates

Each course of the Pathway issues an Attendance Certificate whether the following conditions are fulfilled: all practice sessions associated with each lecture are accomplished; the KNIME workflow associated with each practice session is uploaded to the course platform.
Instructors

FABIO STELLA

Department of Informatics, Systems and Communication