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Analytical software tools in digital marketing

  • Predavanje 30
  • Vježbe 30
  • Samostalni rad 60
Ukupno 120

Naziv predmeta

Analytical software tools in digital marketing

Tip predmeta

Elective

Oznaka predmeta

21-04-514

Semestar

3

ECTS

4

Nastavnici i suradnici

Sadržaj i cilj

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.

Literatura

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)






Minimalni ishodi učenja

  • Izvršiti opisnu analizu na zadanom skupu podataka i protumačiti dobivene rezultate koristeći Excel.
  • Izraditi prediktivni model i protumačiti dobivene rezultate koristeći aplikaciju SPSS Modeler.
  • Izraditi prediktivni model i protumačiti dobivene rezultate koristeći aplikaciju Microsoft Azure ML.
  • Predložiti rješenje poslovnog problema korištenjem alata za poslovnu analitiku i metodologiju CRISP-DM.

Željeni ishodi učenja

  • Procijeniti zadani skup podataka, izraditi prediktivni model i protumačiti dobivene rezultate koristeći program Excel.
  • Izraditi prediktivni model za složene podatke spojene iz više izvora i protumačiti dobivene rezultat koristeći aplikaciju SPSS Modeler.
  • Izraditi prediktivni model za složene podatke spojene iz više izvora i protumačiti dobivene rezultate koristeći aplikaciju Microsoft Azure ML.
  • Izraditi rješenje poslovnog problema korištenjem alata za poslovnu analitiku i metodologija CRISP-DM.
Preuzmi vodič za studente
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