What’s Next?

Congratulations! In a short amount of time, you were able to use Dataiku DSS to connect to data, cleanse it, and automate tedious, manual tasks. You also learned that Dataiku DSS provides both code and visual recipes, allowing you to work the way you want.

In this tutorial, we cleansed the data that will be used by the machine learning model, but we did not build the model. To learn about machine learning in Dataiku DSS in a similar quick start program, visit Data Scientist Quick Start.

To learn more about some of the key concepts presented in this tutorial, visit the following Dataiku Academy and Knowledgebase resources:

• Integration with SQL Databases

• Use visual data preparation to clean a dataset

• Flow Zones, Tags, & More Views

• Cleaning text data

• Data Pipelines

• Learn how to automate workflows with metrics and checks

• Learn how to create a Flow whose output is a dataset, to be shared with other projects or externally to Dataiku

• Dashboards

For more information about some of the key concepts presented in this tutorial, visit the following Dataiku DSS Reference materials:

• Data Preparation

• Visual data preparation and cleaning

• Importing code from Git in project libraries

• Code notebooks