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Introduction to IBM SPSS Modeler and Data Science (v18.1.1) SPVC

¿Î³Ì±àºÅ Course Code	0E008G	¿Î³Ì¼¶±ð Skill Level	Öм¶	¿Î³Ì·ÖÀà Curricula	SPSS	ÃæÊڿγ̱àºÅ Face2Face Course Code
ÊÚ¿ÎÓïÑÔ Language	Ó¢ÎÄ	ÉÏ»úʵÑé Hands-on Labs	ÓÐ	¼Û¸ñ (Ôª) Price	£¤ 1850	ʱ      ³¤ Duration	2D

¿Î³ÌÃèÊö/Course Description£º

This course provides the fundamentals of using IBM SPSS Modeler and introduces the participant to data science. The principles and practice of data science are illustrated using the CRISP-DM methodology. The course provides training in the basics of how to import, explore, and prepare data with IBM SPSS Modeler v18.1.1, and introduces the student to modeling.

ÊڿζÔÏó/Target Audience£º

• Business analysts • Data scientists • Clients who are new to IBM SPSS Modeler or want to find out more about using it

Ô¤±¸¼¼ÄÜ/Prerequisites£º

• It is recommended that you have an understanding of your business data

¿Î³ÌÄ¿±ê/Skills Taught£º

Please refer to course overview

Ö÷Òª¿ÎÌâ/Course Outline£º

1. Introduction to data science • List two applications of data science • Explain the stages in the CRISP-DM methodology • Describe the skills needed for data science 2. Introduction to IBM SPSS Modeler • Describe IBM SPSS Modeler's user-interface • Work with nodes and streams • Generate nodes from output • Use SuperNodes • Execute streams • Open and save streams • Use Help 3. Introduction to data science using IBM SPSS Modeler • Explain the basic framework of a data-science project • Build a model • Deploy a model 4. Collecting initial data • Explain the concepts "data structure", "of analysis", "field storage" and "field measurement level" • Import Microsoft Excel files • Import IBM SPSS Statistics files • Import text files • Import from databases • Export data to various formats 5. Understanding the data • Audit the data • Check for invalid values • Take action for invalid values • Define blanks 6. Setting the of analysis • Remove duplicate records • Aggregate records • Expand a categorical field into a series of flag fields • Transpose data 7. Integrating data • Append records from multiple datasets • Merge fields from multiple datasets • Sample records 8. Deriving and reclassifying fields • Use the Control Language for Expression Manipulation (CLEM) • Derive new fields • Reclassify field values 9. Identifying relationships • Examine the relationship between two categorical fields • Examine the relationship between a categorical field and a continuous field • Examine the relationship between two continuous fields 10. Introduction to modeling • List three types of models • Use a supervised model • Use a segmentation model