IBM SPSS Modeler: foundations V18.2 (0A069G)

• Teams who have just bought Modeler and do not know where to start.
• Analysts preparing data by hand in Excel before modeling.
• Anyone inheriting Modeler streams built by someone else and having to maintain them.

• Introduction to IBM SPSS Modeler: what data science is, the CRISP-DM methodology, and how models are built and applied
• Collecting initial data: field storage and measurement level, importing from and exporting to various formats
• Understanding the data: auditing it, checking for invalid values, deciding what to do with them, and defining blanks
• Setting the unit of analysis: removing duplicates, aggregating, turning nominal fields into flags, and restructuring
• Integrating data: appending datasets, merging them and sampling records
• Transforming fields: the CLEM language, deriving, reclassifying and binning
• Further transformations: functions, replacing field values and transforming distributions
• Examining relationships between two categorical fields, between categorical and continuous, and between two continuous
• Introduction to modeling: objectives, supervised models and segmentation models
• Improving efficiency: SQL pushback, the Data Audit node, global values, parameters, looping and conditional execution

• Labs on IBM SPSS Modeler with real data: streams built, run and fixed during the course.
• Taught directly by IBM Champion instructors from the SIXE team.
• Credly badges available: credly.com/organizations/sixe/badges
• Flexible format — online, classroom or onsite — in closed groups, priced per attendee, in the language of your choice (ES / EN / FR).

Do I need to program?
No. Modeler is worked by dragging nodes; the closest thing to programming is the CLEM language for deriving fields, and it is covered from scratch.

How much of the course is data preparation?
A good share, deliberately: importing, auditing, handling invalid values, setting the unit of analysis, merging and transforming. Modeling comes at the end, because without decent data no model holds anything up.

And the next step?
The models in detail are machine learning models (0A079G): trees, regression, neural networks, ensembles and clustering.

Can you deliver it at our premises?
Yes, and remotely with a live instructor too. Closed groups of four to ten people, in Spanish, English or French.

• 0A079G — machine learning models
• 0A058G — advanced data preparation
• 0G53BG — SPSS Statistics