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The first step to passing the C2090-013 IBM SPSS Modeler DataMining for Business Partners v2 exam is to understand the exam objectives and topics covered in the exam. The exam is designed to test a candidate's knowledge and understanding of IBM SPSS Modeler and its application to data mining for business purposes.
Exam Objectives:
- Understand the basic concepts of data mining and how it can be applied in business settings.
- Use IBM SPSS Modeler to prepare data for analysis, including data cleaning, data transformations, and data sampling.
- Develop and evaluate predictive models using techniques such as decision trees, regression, and clustering.
- Use IBM SPSS Modeler to analyze data and identify patterns and relationships.
- Understand the principles of model selection and evaluation, including techniques for assessing model accuracy and choosing the best model.
- Understand how to apply data mining techniques to real-world business problems and use the insights gained to drive business decisions.
It is important to study and gain hands-on experience with IBM SPSS Modeler in order to successfully pass the exam. IBM offers a variety of training courses and resources to help candidates prepare for the exam, including online training modules, classroom training, and certification guides.
Exam Details:
- The cost of the exam varies by location and can be found on the IBM website.
- The passing mark for the exam is 67%.
- The exam consists of 60 multiple choice questions and has a time limit of 90 minutes.
- The exam can be taken in-person at a testing center or remotely online.
- The exam format is computer-based.
To pass the exam, it is recommended that candidates have a strong understanding of the exam objectives and topics covered. Additionally, candidates should gain hands-on experience using IBM SPSS Modeler and be familiar with the software's features and functions.
Related Books:
- "Predictive Analytics Using IBM SPSS" by Saumyadipta Pyne and Shweta S. Rao
- "Data Mining with IBM SPSS Modeler: Theory, Exercises and Solutions" by Tilo Wendler and Janez Demsar
- "IBM SPSS Modeler Cookbook" by Keith McCormick, Scott Mutchler, and Dean Abbott
- "Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python" by Galit Shmueli, Peter C. Bruce, Inbal Yahav, Nitin R. Patel, and Kenneth C. Lichtendahl Jr.
Studying these books can help candidates gain a deeper understanding of the concepts and techniques covered in the exam, as well as provide hands-on exercises to practice using IBM SPSS Modeler.
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