Abstract: 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 solve together a number of challenges by hacking a workflow-based solution via 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.
We'll start with an introduction to KNIME Analytics Platform, followed by a short presentation about the data science cycle.
Then we will cover three sets of exercises.
Exercise 1 - Working on the raw data. Data access and data preparation.
Exercise 2 - Machine Learning. Which model shall I use? Which parameters?
Exercise 3 - I have a great model. Now what? The model deployment phase.
You can optionally follow the live workflow building on your own laptop by downloading KNIME Analytics Platform (knime.com/download) and the exercises.
Bio: Paolo Tamagnini is a data science evangelist at KNIME and based in Berlin. After graduating with a master's degree in data science at Sapienza University of Rome, Paolo gathered research experience at New York University in machine learning interpretability and visual analytics tools. Since working at KNIME, Paolo has presented different workshops in the USA and Europe and developed a number of reusable guided analytics applications for automated machine learning and human-in-the-loop analytics.