Programs

Digital Marketing

Analytical software tools in marketing

  • Class 30
  • Practice 30
  • Independent work 120
Total 180

Course title

Analytical software tools in marketing

Lecture type

Elective

Course code

20-04-002

Semester

3

ECTS

6

Lecturers and associates

Course objectives

The goal is to familiarize students with existing analytical software tools used in marketing. Through lectures, students will be introduced to functionalities of analytical software tools for collection and preparation of data, opportunities they have to perform the preliminary analysis, the method of selection of algorithms for modelling. Through exercises, students will learn how to independently select and apply best tool and solve business challenges.

Content

Students will learn how to use Google Analytics, data analysis in Excel, how to build a predictive model in Microsoft Azure Machine Learning, IBM SPSS Modeler.

Required reading

Albright, S. Ch., Winston, W.; Business Analytics: Data Analysis and Decision Making, 7th Edition. CENGAGE Learning

Additional reading

McCormick, K.; Abbott, D.; Khabaza, T: IBM SPSS Modeler Cookbook, Birmingham, 2013
Jeff Barnes: Microsoft Azure Essentials: Azure Machine Learning, 2015.
SPSS Modeler 18.0 Documentation
Chorianopoulos, A.: Effective CRM using Predictive Analytics

Minimal learning outcomes

  • Perform a descriptive analysis on a given data set and interpret the obtained results using Excel
  • Check the quality of the data on the given data set, eliminate the shortcomings and make a predictive model and interpret the obtained results using SPSS Modeler
  • Check the quality of the data on the given data set, eliminate the shortcomings and make a predictive model and interpret the obtained results using Microsoft Aure ML
  • Propose a solution to a business problem using business analytics tools and CRISP-DM methodology

Preferred learning outcomes

  • Check the quality of the data on the given data set, eliminate the shortcomings and make a predictive model and interpret the obtained results using Excel
  • Link data from multiple sources for analysis and make a predictive model and interpret the obtained results using SPSS Modeler
  • Link data from multiple sources for analysis and make a predictive model and interpret the obtained results using Microsoft Aure ML
  • Derive a solution to a business problem using business analytics tools and CRISP-DM methodologies
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