MIPRO and University of Zagreb, Faculty of Organization and Informatics, Varaždin
Dijana Oreški, Irena Kedmenec, Nikola Kadoić (Faculty of Organization and Informatics, Varaždin)
Dijana Oreški, firstname.lastname@example.org, Irena Kedmenec, email@example.com, Nikola Kadoić, firstname.lastname@example.org (Faculty of Organization and Informatics, Varaždin)
Data is being increasingly recognized by organizations and businesses as potential for significant benefits to business and society. Data science algorithms can efficiently analyze large amounts of data. The purpose of this workshop is to give an overview of decision trees as predictive modeling technique and to explain the process of development and interpretation of the predictive model using decision tree algorithm. Basic idea is to work on data science projects with social impact. Case studies of education (Moodle log datasets) and entrepreneurship are presented.
In this workshop, participants are guided to develop predictive models on real life data by applying BigML tool to solve real-world problems in education and/or entrepreneurship. CRISP DM methodology is used, consisting of six steps: domain understanding, data understanding, data preparation, modelling, evaluation and deployment.
Participants will learn how to prepare data, develop, analyze, evaluate, understand and interpret predictive models in BigML tool.
Anyone interested in learning basics of data analytics on real life data.
- Introduction: Steps of CRISP DM methodology for development of predictive models.
- Working part: Example of Moodle log analysis (data preparation and modeling).
- Conclusion: Evaluation and interpretation of the model.
This workshop will be held in Croatian.
Registration / Fee:
Access to the workshop is free.
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