KNIME.com is hosting a Basic Course for KNIME Analytics Platform at the MicroTek Training Solutions, 655 Montgomery St Suite 400, San Francisco, CA 94111, USA on April 25, 2017.
Basic Course for KNIME Analytics Platform is an ideal opportunity for beginners, advanced users and KNIME experts to be introduced to KNIME Analytics Platform, to learn how to use it more effectively, and how to create clear, comprehensive reports based on KNIME workflows.
During this course you will be shown how to access data in all kinds of shape and formats, to transform them, to train models for predictions and for clustering, and how to deploy the trained models.
The course will show the functionalities of KNIME Analytics Platform through interactive sessions and hands.on exercises. Together we will build and apply a Next Best Offer model step by step.
The 6th KNIME Cheminformatics Workshop will be hosted Syngenta on Jealott's Hill in Bracknell, UK on April 25th, 2017 (the day before the UK QSAR Spring Meeting). The meeting will be held in the Eagle Room in the Elements Building. For direction on how to reach the Syngenta campus see instructions below. Upon arrival report to reception at the security gate where you will be given directions to the venue.
This workshop brings together KNIME users from the cheminformatics area in order to discuss questions, suggestions, and solutions to cheminformatics or general KNIME problems and wishes. As always representatives from KNIME will also be present to provide an overview of new features and future developments.
If you are interested in presenting some of your work around KNIME or want to discuss a certain topic, please indicate so during registation or send a mail to Thorsten.
Many thanks to Mark Earll for organizing the event!
Together with our long-standing partner, Dymatrix, we are hosting a meetup on April 26, 2017 for the KNIME community and people who are new to KNIME and Dymatrix. Come to our meetup to find out more about the opportunities and best practices of advanced analytics. Become part of this active community!
KNIME.com is hosting a Course for KNIME Server at the MicroTek Training Solutions, 655 Montgomery St Suite 400, San Francisco, CA 94111, USA on April 26, 2017. We recommend combining this course with the 1-day Basic Course for KNIME Analytics Platform held April 25, 2017.
Course for KNIME Server dives into the details of KNIME Server and KNIME WebPortal and discusses them from three different points of view: the power user, the administrator, and the end user.
All tools and features designed for each one of these three personas are shown in detail and illustrated by means of interactive sessions and hands-on exercises.
Attendees learn how to exchange workflows and data between the server and the client, how to take advantage of the many server dedicated nodes and features when implementing a workflow, how to set access rights on workflows, data, and meta-nodes, share meta-nodes, execute workflows remotely and from the KNIME WebPortal, and how to schedule report and workflow executions, and more.
The course is designed not only for customers, partners, and the community, but also for anyone interested in finding out more about the KNIME commercial platform and its functionalities.
KNIME.com is hosting a Big Data Course for KNIME Analytics Platform at the MicroTek Training Solutions, 655 Montgomery St Suite 400, San Francisco, CA 94111, USA on April 27, 2017.
This 1-day Big Data Course for KNIME Analytics Platform explores the KNIME Big Data Extension to deal with Hadoop and Spark the KNIME way, without scripts. Attendees learn to run in-database processing on Hadoop Hive and to train Spark Machine Learning models from a KNIME workflow, taking advantage of the ease-of-use of KNIME Analytics Platform and parallel processing of a big data platform.
During this course, attendees learn more about Spark, Hadoop, and how to interact with them through interactive sessions and hands-on exercises. Step by step, we will build a predictive model and use it to fix missing values on a big data dataset.