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Advanced Data Preparation Using IBM SPSS Modeler (v18.1.1) SPVC

课程编号
Course Code
0E058G 课程级别
Skill Level
高级 课程分类
Curricula
SPSS 面授课程编号
Face2Face
Course Code
授课语言
Language
英文 上机实验
Hands-on Labs
价格 (元)
Price
¥ 1850        时      长
Duration
1D
课程描述/Course Description:
This course covers advanced topics to aid in the preparation of data for a successful data science project. You will learn how to use functions, deal with missing values, use advanced field operations, handle sequence data, apply advanced sampling methods, and improve efficiency.
授课对象/Target Audience:
This advanced course is intended for anyone who wants to become familiar with the full range of techniques available in IBM SPSS Modeler for data preparation.
预备技能/Prerequisites:
• Experience using IBM SPSS Modeler including familiarity with the Modeler environment, creating streams, reading data files, exploring data, setting the unit of analysis, combining datasets, deriving and reclassifying fields, and basic knowledge of modeling. • Prior completion of the Introduction to IBM SPSS Modeler and Data Science course is recommended.
课程目标/Skills Taught:
Please refer to course overview
主要课题/Course Outline:
1: Using functions to cleanse and enrich data • Use date functions • Use conversion functions • Use string functions • Use statistical functions • Use missing value functions 2: Using additional field transformations • Replace values with the Filler node • Recode continuous fields with the Binning node • Change a field’s distribution with the Transform node 3: Working with sequence data • Use sequence functions • Count an event across records • Expand a continuous field into a series of continuous fields with the Restructure node • Use geospatial and time data with the Space-Time-Boxes node 4: Sampling, partitioning and balancing data • Draw simple and complex samples with the Sample node • Create a training set and testing set with the Partition node • Reduce or boost the number of records with the Balance node 5: Improving efficiency • Use database scalability by SQL pushback • Process outliers and missing values with the Data Audit node • Use the Set Globals node • Use parameters • Use looping and conditional execution