What is CRISP-DM?

• Data modeling approach

• Business analysis framework

• Problem solving framework

• Project management framework

What does CRISP-DM abbreviation mean?

• Cross-Industry Standard Procedure for Data Management

• Cross-Industry Standard Process for Data Mining

• Cross-Industry Standard Platform for Digital Mindset

• Cross-Industry Standard Patterns for Data Manipulation

Which of the following are valid steps in CRISP-DM?

• Data preparation

• Data consolidation

• Data wrangling

• Data understanding

• Business issue understanding

Which of the following are valid steps in CRISP-DM?

• Validation

• Cross-validation

• Presentation/visualization

• Model training

• Analysis/modeling

What is the correct sequence of steps in CRISP-DM?

• Data understanding, business issue understanding, data preparation, analysis/modeling, validation, presentation/visualization

• Data understanding, business issue understanding, data preparation, validation, analysis/modeling, presentation/visualization

• Business issue understanding, data preparation, data understanding, analysis/modeling, validation, presentation/visualization

• Business issue understanding, data understanding, data preparation, analysis/modeling, validation, presentation/visualization

Please select the questions that belong to business issue understanding CRISP-DM step

• What data is available?

• What decision needs to be made?

• What data is needed?

• What information is needed to inform the decision?

• What type of analysis will provide the information to inform the decision?

What are the common steps used for data preparation?

• Cleansing

• Gathering

• Formatting

• Blending

• Storing

Please select approaches that, according to methodology map, are suitable for addressing a problem where we want to predict a variable with specific time-based values in a data rich business problem

• ARIMA

• Decision trees

• Boosted model

• ETS

• Linear regression

Please select approaches that, according to methodology map, are suitable for addressing a problem where we want to determine if a subject belongs to one of three categories in a data rich business problem:

• Logistic regression

• Decision tree

• Forest model

• Boosted model

What is an optimal approach to address a data poor business problem where we want to predict the outcome of introducing a new feature?

• Numeric prediction

• A/B testing

• Classification