Introduction to Clementine and Data Mining
Course Description
This course provides you with an overview of data mining and the fundamentals of using Clementine. The principles and practice of data mining are illustrated using the CRISP-DM methodology. You’ll follow the stages of a typical data mining project, from reading data, to data exploration, data transformation, modelling and effective interpretation of results. You’ll also learn how to read, explore and manipulate data with Clementine and then create and use successful models.
Who Should Attend
- Anyone with little or no experience using Clementine. May also be unfamiliar with data mining in general
Prerequisites
- General computer literacy
- It would be helpful if you had an understanding of your organisation’s data, as well as any of your organisation’s business issues that are relevant to the use of data mining
- No statistical background is necessary
Course Content
Following an overview of the main features and an introduction to essential terminology, you will proceed logically through the following topics:
- Introduction to data mining
- The CRISP-DM methodology
- Best practices for data mining
- The basics of using Clementine
- Reading data files
- Working with dates
- Auditing and exploring data quality
- Searching for anomalous data and outliers
- Data manipulation
- Searching for relationships among fields
- Combining data files by appending and/or merging
- Restructuring data files with aggregate
- Sampling data
- Partitioning data for modelling
- Modelling techniques in Clementine
- Automatic modelling for binary outcomes
- Evaluating and comparing model performance
- Deploying and using models
- Running SPSS commands from Clementine
SPSS Products Used
Duration
Level: Beginner
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