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We have researched and compiled a list of the 3 best IBM SPSS Modeler books that can help you learn about every aspect of it. You will have an insight of each IBM SPSS Modeler book. So that you can easily decide which book you will choose for your study.
IBM SPSS Modeler allows users to quickly and efficiently use predictive analytics and gain insights from your data.
IBM SPSS Modeler is a data mining workbench that enables you to explore data, identify important relationships that you can leverage, and build predictive models quickly allowing your organization to base its decisions on hard data not hunches or guesswork.
Best IBM SPSS Modeler Books
- IBM SPSS Modeler Essentials
- IBM SPSS Modeler Cookbook
- Data Mining with SPSS Modeler: Theory, Exercises and Solutions
This book is ideal for those who are new to SPSS Modeler and want to start using it as quickly as possible, without going into too much detail. An understanding of basic data mining concepts will be helpful, to get the best out of the book.
This book takes a detailed, step-by-step approach to introducing data mining using the de facto standard process, CRISP-DM, and Modeler’s easy to learn “visual programming” style. You will learn how to read data into Modeler, assess data quality, prepare your data for modeling, find interesting patterns and relationships within your data, and export your predictions.
This book provides an overview of various popular data modeling techniques and presents a detailed case study of how to use CHAID, a decision tree model. By the end of this book, you will have a firm understanding of the basics of data mining and how to effectively use Modeler to build predictive models.
What You Will Learn:
- Understand the basics of data mining and familiarize yourself with Modeler’s visual programming interface
- Import data into Modeler and learn how to properly declare metadata
- Obtain summary statistics and audit the quality of your data
- Prepare data for modeling by selecting and sorting cases, identifying and removing duplicates, combining data files, and modifying and creating fields
- Assess simple relationships using various statistical and graphing techniques
- Get an overview of the different types of models available in Modeler
- Build a decision tree model and assess its results
- Score new data and export predictions
If you have had some hands-on experience with IBM SPSS Modeler and now want to go deeper and take more control over your data mining process, this is the guide for you. It is ideal for practitioners who want to break into advanced analytics.
IBM SPSS Modeler Cookbook takes you beyond the basics and shares the tips, the timesavers, and the workarounds that experts use to increase productivity and extract maximum value from data.
Go beyond the basics and get the full power of your data mining workbench with this practical guide.
What You Will Learn:
- Use and understand the industry standard CRISP_DM process for data mining.
- Assemble data simply, quickly, and correctly using the full power of extraction, transformation, and loading (ETL) tools.
- Control the amount of time you spend organizing and formatting your data.
- Develop predictive models that stand up to the demands of real-life applications.
- Take your modeling to the next level beyond default settings and learn the tips that the experts use.
- Learn why the best model is not always the most accurate one.
- Master deployment techniques that put your discoveries to work making the most of your business’ most critical resources.
>>Also Check: Best Books to learn IBM SPSS
This book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs.
The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.