Description
At Sixe Engineering we have been providing official IBM training around the world for over 12 years. Get the best training from our specialists in Europe. We have important discounts and offers for two or more students.
Course details
| IBM course code: 0A018G | Category: IBM SPSS Modeler / IBM SPSS Modeler |
| Delivery: Online & on-site** | Course length in days: 1 |
Target audience
• Business Analysts
• Data Scientists
• Participants who want to get started with data science
Desired Prerequisites:
• It is recommended that you have an understanding of your business data
Instructors
The great majority of the IBM courses we offer are taught directly by our engineers. This is the only way we can guarantee the highest quality. We complement all the training with our own materials and laboratories, based on our experience during the deployments, migrations and courses that we have carried out during all these years.
Added value
Our courses are deeply role oriented. To give an example, the needs for technology mastery are different for developer teams and for the people in charge of deploying and managing the underlying infrastructure. The level of previous experience is also important and we take it very seriously. That is why beyond (boring) commands and tasks, we focus on solving the problems that arise in the day to day of each team. Providing them with the knowledge, competencies and skills required for each project. In addition, our documentation is based on the latest version of each product.
Agenda and course syllabus
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
Do you need to adapt this syllabus to your needs? Are you interested in other courses? Ask us without obligation.
Locations for on-site delivery
- Austria: Vienna
- Belgium: Brussels, Ghent
- Denmark: Cophenhagen
- Estonia: Tallinn
- Finland: Helsinki
- France: Paris, Marseille, Lyon
- Germany: Berlin, Munich, Cologne, Hamburg
- Greece: Athens, Thessaloniki
- Italy: Rome
- Louxemburg: Louxembourg (city)
- Netherlands: Amsterdam
- Norway: Oslo
- Portugal: Lisbon, Braga, Porto, Coimbra
- Slovakia: Bratislava
- Slovenia: Bratislava
- Spain: Madrid, Sevilla, Valencia, Barcelona, Bilbao, Málaga
- Sweden: Stockholm
- Turkey: Ankara
- United Kingdom: London
