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Analytical software tools in digital marketing

  • Class 30
  • Practice 30
  • Independent work 60
Total 120

Course title

Analytical software tools in digital marketing

Lecture type

Elective

Course code

22-04-514

Semester

3

ECTS

4

Lecturers and associates

Course overview

The objectives of this module are to enable students to:
• apply specific functionalities of concrete analytics software tools depending on the project objectives
• to apply different primary analysis functions
• to create a simpler analytical model using optimal algorythms

The module enables students to learn with existing analytical software tools used in marketing. Students will be introduced to functionalities of analytical software tools for collection and preparation of data, opportunities they have to perform the preliminary analysis and methods of selection of algorithms for modelling. Students will learn how to perform data analysis using Excel and how to build a predictive model using Microsoft Azure Machine Learning and IBM SPSS Modeller.

This module has a goal to prepare students to be data driven in decision-making process. Students will gain confidence in data analysis and in building data predictive models what will prepare them for their future marketing jobs.

Literature

Essential reading:
1. Albright, S. Ch., Winston, W. (2019) Business Analytics: Data Analysis and Decision Making, 7th Edition. Boston, MA: CENGAGE Learning
2. Winston, W. L. (2016) Microsoft Excel 2016 - Data Analysis and Business Modeling Redmond: Microsoft Press [Online]. Available at: https://download.microsoft.com/download/0/9/6/096170E9-23A2-4DA6-89F5-7F5079CB53AB/9780735698178.pdf (Accessed: 10 May 2021)
3. Barnes, J. (2015) Azure Machine Learning, Redmond: Microsoft Press [Online]. Available at: https://download.microsoft.com/download/0/9/6/096170E9-23A2-4DA6-89F5-7F5079CB53AB/9780735698178.pdf (Accessed: 10 May 2021)
4. IBM (2021) Introduction to IBM SPSS Modeler and Data Science (v18.1.1 Available at: https://www.ibm.com/training/course/0A008G (Accessed: 10 May 2021)

Recommended reading:
1. Chorianopoulos, A. (2016) Effective CRM using Predictive Analytics, 1st edn, Chichester: Wiley and Sons
2. McCormick, K., Abbott, D. and Khabaza, T (2013) IBM SPSS Modeler Cookbook, Birmingham: Packt
3. van den Berg, R.G. (2021) SPSS Tutorials [Oline]. Available at: https://www.spss-tutorials.com/blog/ (Accessed: 10 May 2021)

Further reading:
1. Salcedo, J. and McCormick, K. (2017) IBM SPSS Modeler Essentials: Effective techniques for building powerful data mining and predictive analytics solutions, Birmingham: Packt
2. IBM (2021) Advanced Analytics with IBS SPSS Statistics [Online]. Available at: https://www.ibm.com/cloud/garage/dte/tutorial/advanced-analytics-ibm-spss-statistics (Accessed: 10 May 2021)






Preuzmi vodič za studente

Minimal learning outcomes

  • Perform a descriptive analysis on a given data set and interpret the obtained results using Excel
  • Create predictive model and interpret the obtained results using SPSS Modeler
  • Create predictive model and interpret the obtained results using Microsoft Azzure ML
  • Propose a solution to a business problem using business analytics tools and CRISP-DM methodology

Preferred learning outcomes

  • Evaluate given data set, create a predictive model and interpret the obtained results using Excel
  • Create predictive model for complex data merged from multiple sources and interpret the obtained results using SPSS Modeler
  • Create predictive model for complex data merged from multiple sources and interpret the obtained results using Microsoft Azzure ML
  • Derive a solution to a business problem using business analytics tools and CRISP-DM methodologies
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