Data Science Learnathon. From Raw Data to Deployment: the Data Science Cycle with KNIME
Data Science Learnathon. From Raw Data to Deployment: the Data Science Cycle with KNIME

Abstract: 

This will be a Learnathon kind of workshop. A Learnathon is a workshop where we allow ourselves the luxury of learning new tools and new techniques.

For this particular event, we will cover the whole data science cycle, from the raw data to the final application on a production machine. That is: data access, data blending, data preparation, model training, optimization, testing, and finally deployment. The tool of choice for this Learnathon will be KNIME Analytics Platform.

KNIME Analytics Platform is an open, open-source, GUI-driven, data analytics platform, that covers all your data needs from data import to final deployment. Being open, KNIME Analytics Platform offers a vast integration and IDE environments for R, Python, SQL, and Spark.

After an initial introduction to the tool and to the data science cycle, we will split in groups. Each group will focus on one of three aspects of the data science cycle:
- Just pure raw data. Data Access and Data Preparation
- Machine Learning. Which model shall I use? Which parameters?
- I have a great model. Now what? The deployment phase

On our side, we will provide: a few datasets; example workflows to be completed according to the chosen task; and expertise in KNIME and data science.

Please bring your own laptop to use during the Learnathon, with KNIME Analytics Platform pre-installed. To install KNIME Analytics Platform, follow the instructions provided in these YouTube videos:
- Windows https://youtu.be/yeHblDxakLk
- Mac https://youtu.be/1jvRWryJ220
- Linux https://youtu.be/wibggQYr4ZA

If you would like to get familiar with KNIME Analytics Platform, you can explore the content of this e-learning course https://www.knime.com/knime-introductory-course . In particular, we advise you to read and watch the units in Chapter 1.

Bio: 

Rosaria Silipo has been mining data, big and small, since her master degree in 1992. She kept mining data throughout all her doctoral program, her postdoctoral program, and most of her following job positions. So many years of experience and passion for data analytics, data visualization, data manipulation, reporting, business intelligence, and KNIME tools, naturally led her to become a principal data scientist and an evangelist for data science at KNIME.

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