Data Science without a Ph.D. Using IBM SPSS Modeler (v18.1.1) SPVC - 0E018G

Description

This course focuses on reviewing concepts of data science, where participants will learn the stages of a data science project. Topics include using automated tools to prepare data for analysis, build models, evaluate models, and deploy models. To learn about these data science concepts and topics, participants will use IBM SPSS Modeler as a tool.

If you are enrolling in a Self Paced Virtual Classroom or Web Based Training course, before you enroll, please review the Self-Paced Virtual Classes and Web-Based Training Classes on our Terms and Conditions page, as well as the system requirements, to ensure that your system meets the minimum requirements for this course. http://www.ibm.com/training/terms

Introduction to data science and IBM SPSS Modeler
Explain the stages in a data-science project, using the CRISP-DM methodology
Create IBM SPSS Modeler streams
Build and apply a machine learning model
2: Setting measurement levels
Explain the concept of "field measurement level"
Explain the consequences of incorrect measurement levels
Modify a field's measurement level
3: Exploring the data
Audit the data
Check for invalid values
Take action for invalid values
Impute missing values
Replace outliers and extremes
4: Using automated data preparation
Automatically exclude low quality fields
Automatically replace missing values
Automatically replace outliers and extremes
5: Partitioning the data
Explain the rationale for partitioning the data
Partition the data into a training set and testing set
6: Selecting predictors
Automatically select important predictors (features) to predict a target
Explain the limitations of automatically selecting features
7: Using automated modeling
Find the best model for categorical targets
Find the best model for continuous targets
Explain what an ensemble model is
8: Evaluating models
Evaluate models for categorical targets
Evaluate models for continuous targets
9: Deploying models
List two ways to deploy models
Export scored data

Aim

Please refer to course overview

Participant requirements

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

Participant

Business Analysts
Data Scientists
Participants who want to get started with data science