Virtual Data Science Learnathon

September 8, 2020 - Online

Together with our partner IQuartil and the KNIME Community fan Gabriel Cornejo (CEO Minero de Datos) we are hosting a virtual data science learnathon in Spanish.

This free online learnathon is a mix between a hackathon and a workshop. It's like a workshop because we'll learn more about the data science cycle: data access, data blending, data preparation, model training, optimization, testing, and deployment. It's like a hackathon because we'll work in groups to hack a workflow-based solution to guided exercises.

The tool of choice is the open-source, GUI-driven KNIME Analytics Platform. Because KNIME is open, it offers great integrations with an IDE environment for R, Python; SQL, and Spark.

Agenda & groups/breakout rooms

We'll start with an introduction to KNIME Analytics Platform, followed by a short presentation about the data science cycle.

After this presentation we split into three groups. Each group focuses on one of the three aspects of the data science cycle.

Three zoom breakout rooms will be activated for this purpose. You go into the room for the group you sign up for (below) to attend the specific tutorial and exercises.

There will be a KNIME data scientist in each breakout room to help you while you work on the exercises.

Choose which group (Group 1, 2, or 3) you want to join on the zoom registration page.

  • Group 1 - Working on the raw data. Data access and data preparation.
  • Group 2 - Machine Learning. Which model shall I use? Which parameters?
  • Group 3 - I have a great model. Now what? The model deployment phase.

Access the Learnathon materials (in Spanish) here.


How do I join the webinar?

You’ll receive a zoom link with your registration confirmation. Make sure you have a stable internet connection!

Will I be able to ask questions?

Yes, each group will be in different zoom breakout rooms, where you can ask questions.

Where do I find the latest version of KNIME Analytics Platform?

Download the latest free, open source version of knime here:

What other resources will help me to get started in KNIME?
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