Here are five different methods of sharing your data analysis with key stakeholders

Author: Paolo Tamagnini (KNIME). As first published in DevPro Journal.

There are many different scenarios when building a data science workflow. No matter how complex the data analysis, every data scientist needs to deal with an important final step: communicating their findings to the different stakeholders — decision-makers, managers, or clients. This final step is vital because if the findings cannot be understood, trusted or valued, then the entire analysis will be discarded and forgotten.

Authors: Kathrin Melcher, Rosaria Silipo (KNIME). As first published in Dataversity.

Decision trees represent a set of very popular supervised classification algorithms. They are very popular for a few reasons: They perform quite well on classification problems, the decisional path is relatively easy to interpret, and the algorithm to build (train) them is fast and simple.

Authors: Iris Adä and Phil Winters (KNIME)

Want a workflow that uses available in-DB capabilities and moves to a production Spark setup? At the same time it should use special Google services before comparing a KNIME Random Forest to an H2O Random Forest and then automatically choose the correct model to create data that are automatically added to your favourite CRM - so that the new score is placed back into the CRM? No problem in KNIME.

Authors: Maarit Widmann and Rosaria Silipo (KNIME). As first published in The New Stack.

The full big data explosion has convinced us that more is better. While it is of course true that a large amount of training data helps the machine learning model to learn more rules and better generalize to new data, it is also true that an indiscriminate addition of low-quality data and input features might introduce too much noise and, at the same time, considerably slow down the training algorithm.

By Felix Kergl-Räpple and Maarit Widmann (KNIME)

How cohort analysis reveals a comprehensive view of our business

Artifical Intelligence models are empty, neutral machines.
They will acquire a bias when trained with biased data

By Rosaria Silipo, KNIME. As first published in InfoWorld.

Bias in artificial intelligence (AI) is hugely controversial these days. From image classifiers that label people’s faces improperly to hiring bots that discriminate against women when screening candidates for a job, AI seems to inherit the worst of human practices when trying to automatically replicate them.

The risk is that we will use AI to create an army of racist, sexist, foul-mouthed bots that will then come back to haunt us. This is an ethical dilemma. If AI is inherently biased, isn’t it dangerous to rely on it? Will we end up shaping our worst future?

Author: Martyna Pawletta (KNIME)

Today: Ontologies – or let’s see if we can serve pizza via the semantic web and KNIME Analytics Platform. Will they blend?

Authors: Jaime Rodríguez-Guerra, Dominique Sydow, Andrea Volkamer (Volkamer Lab, Institute of Physiology, Charité Universitätsmedizin Berlin)

Jupyter Notebooks offer an incredible potential to disseminate technical knowledge thanks to its integrated text plus live code interface. This is a great way of understanding how specific tasks in the Computer-Aided Drug Design (CADD) world are performed, but only if you have a basic coding expertise. While users without a programming background can simply execute the code blocks blindly, this rarely provides any useful feedback on how a particular pipeline works. Fortunately, more visual alternatives like KNIME workflows are better suited for this kind of audience. 

In this blog post we want to introduce our new collection of tutorials for computer-aided drug design (Sydow and Wichmann et al., 2019). Building on our Notebook-based TeachOpenCADD platform (Sydow et al., 2019), our TeachOpenCADDKNIME pipeline consists of eight interconnected workflows (W1-8), each containing one topic in computer-aided drug design.

By Michael Berthold, KNIME. As first published in Techopedia.

There is a lot of talk about data science these days, and how it affects essentially all types of businesses. Concerns are raised by management teams about the lack of people to create data science, and promises are made left and right on how to simplify or automate this process.

Yet, little attention is paid to how the results can actually be put into production in a professional way.