SPSS Modeler: data science without a PhD (0A018G)

• Small teams with no statistician on staff.
• Anyone needing a quick first prediction to decide whether investing further is worth it.
• Analysts using the automated node blind who want to know what it is doing.

• Data science and SPSS Modeler: the stages of CRISP-DM, creating streams and applying a model
• Setting measurement levels: what they are, what goes wrong when they are incorrect, and how to fix them
• Exploring the data: auditing, checking invalid values, imputing missing values and handling outliers and extremes
• Automated data preparation: excluding low quality fields, imputing and handling outliers without intervening
• Partitioning the data into training and testing sets, and why it has to be done
• Selecting predictors automatically, and the limits of doing it that way
• Automated modeling: finding the best model for categorical and continuous targets, and what an ensemble is
• Evaluating models for categorical and continuous targets
• Deploying: two ways to put the model to work, and exporting scored data

• 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).

Can automated modeling be trusted?
For a first version, yes, and the course teaches you to read the measures that say whether the result stands. For production it pays to understand the models from the inside, and that is machine learning models (0A079G).

Is one day enough for anything?
It is enough to go from raw data to an evaluated, deployed model using the automated nodes. The price of speed is control, and the course shows where you pay it.

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.

• 0A069G — Modeler foundations
• 0A079G — machine learning models
• 0A008G — introduction to Modeler