In this Learnathon you will learn more about the data science cycle - data access, data blending, data preparation, model training, optimization, testing, and deployment. We will work in groups to create a workflow-based solution to guided exercises.
The tool of choice for this Learnathon 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.
In this webinar, we will start with an introduction to KNIME Analytics Platform followed by a short presentation about the data science cycle.
For the original workshop we have prepared a few datasets and jump-start workflows for three different groups. Each group focussing on one of three aspects of the data science cycle:
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.
In the current situation, we, unfortunately, can’t meet in person to work together in groups. Instead, everyone can download the workshop material from here and Kathrin is happy to support you remotely, while you work on the exercises.
If you would like to get familiar with KNIME Analytics Platform, you can explore the content of our E-learning course.
Kathrin Melcher, Data Scientist at KNIME
Kathrin Melcher is a data scientist at KNIME. She holds a master degree in Mathematics. She has a strong interest in data science, machine learning and algorithms, and enjoys teaching and sharing her knowledge about it.