Business Intelligence in SPSS

Term: 
Winter
Credits: 
1.0
ECTS credits: 
2.0
Status: 
Elective
Course Description: 

IBM SPSS Modeler is an extensive predictive analytics platform with an intuitive graphical interface. It provides a wide range of advanced statistical and data mining algorithms, which allows users to perform complex data analysis and to extract business value from data.

This introductory course aims to give an overview of the fundamentals of using IBM SPSS Modeler through real business cases. The course structure follows the stages of a typical data mining project, from collecting data, to data exploration, data transformation, and modeling to effective interpretation of the results. The last part of this course will be dedicated to additional features and extensions in Modeler, allowing students to add more advanced functionalities. These techniques include text analytics, Big Data and open source integration such as R and Python.

Learning Outcomes: 

At the end of the course, students will be able to:

-       Understand the basic concept of data mining in IBM SPSS Modeler

-       Define when and how various data mining techniques should be applied

-       Handle unstructured data in order to use in predictive modeling

Execute basic data mining projects using IBM SPSS Modeler and interpret the result

Assessment: 

50% final exam

50% assignments

Prerequisites: 

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