Tutorials

Take your first steps with Dataiku DSS in this tutorial!

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.

In this two-part tutorial, you will create your first machine learning model and then use it to score new records.

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

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.

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

• If you are trying to partition a file based on the values of one of its columns, you will need to read our tutorial on repartitioning file-based datasets.

Web apps are a great way to share your insights. You can build beautiful and interactive data visualizations, or even dashboards for sophisticated reporting! See the web app section of the data visualization portal for tutorials on standard, Bokeh, and Shiny web apps.

You can define Deep Learning architectures in Dataiku’s Visual Machine Learning for a variety of applications, such as image processing, text analysis, and time series, in addition to models for structured data.

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.