Arrow Electronics, Inc.

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

CODE: ZL1_0E018

LENGTH: 1 day

PRICE: kr2 400,00

Description

Contains PDF course guide, as well as a lab environment where students can work through demonstrations and exercises at their own pace.

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

Objectives

Please refer to course overview

Audience

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

Prerequisites

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

Programme

1:  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

Further Information

Prior to enrolling, IBM Employees must follow their Division/Department processes to obtain approval to attend this public training class. Failure to follow Division/Department approval processes may result in the IBM Employee being personally responsible for the class charges.

GBS practitioners that use the EViTA system for requesting external training should use that same process for this course. Go to the EViTA site to start this process:

http://w3.ibm.com/services/gbs/evita/BCSVTEnrl.nsf

Once you enroll in a GTP class, you will receive a confirmation letter that should show:

  • The current GTP list price
  • The 20% discounted price available to IBMers. This is the price you will be invoiced for the class.

Session Dates
Date
Location
Time Zone
Language
Type
Guaranteed
PRICE

27 okt 2021

English

Self Paced Training

kr2 400,00