Portal

Tutorials

Dataiku DSS Basics

Tutorial: Basics

Take your first steps with Dataiku DSS in this tutorial!


Tutorial: From Lab to Flow

In this tutorial, you will learn how to enrich your data, combine data from multiple datasets, and create a new dataset based on recipe transformations.


Tutorial: Machine Learning

In this tutorial, you will create your first machine learning model.

Dataiku DSS Automation and Production

Tutorial: Automation

In this tutorial, you will learn the basics of scheduling jobs using scenarios and monitoring jobs using metrics and checks.


Tutorial: Deploying to Production

In this tutorial, you will learn how to package flows for deployment, version flows, and deploy packages in a production environment.


Tutorial: Deploying to Real-Time Scoring

In this tutorial, you will learn how to package an API service, which includes a model, for deployment, deploy a service to the real-time scoring environment, and version service packages.

Dataiku DSS Advanced

Using window recipes

If you are trying to:

  • filter rows by order of appearance within a group,
  • compute moving averages or cumulative sums,
  • or perform data manipulation similar to what SQL window functions do,

you can do all these by using a window recipe!


Partitioning datasets

Partitioning is a very powerful tool when working with incremental data. It will help with reducing computation time when you update you data science workflow.


Creating web apps

Web apps are a great way to share your insights. You can build beautiful and interactive data visualizations, or even dashboards for sophisticated reporting!

Use Case Samples

Churn prediction

Churn prediction is one of best known applications of data science in the Customer Relationship Management (CRM) and Marketing fields. Simply put, a churner is a user or customer that stops using a company’s products or services.

In this tutorial you will create a complete data science workflow to predict if a customer is going to churn. It covers the whole process, from data preparation to machine learning, and includes a good amount of feature engineering.