Author: Rosaria Silipo (KNIME). As first published in DarkReading.

Yes! You can predict the chance of a mechanical failure or security breach before it happens. Part one of a two-part series.

Authors: Jan Lindquist and Johan Tornborg (Redfield)

In the modern business environment, companies have to support a heterogeneous combination of operating systems and technologies. Defining customizations and profiles for these combinations simplifies deployment, speeds up rolling out changes, and generally makes it easier to onboard new users.

KNIME Server has a feature that greatly simplifies the installation of database drivers. Installing drivers in KNIME Analytics Platform involves a number of steps from finding the specific driver, accepting the conditions to use it, downloading the driver, and moving it to a folder within your KNIME installation, which then has to be referred to. That means quite a large number of potential failure points, especially when users have different levels of operating system experience. Any type of manual configuration can introduce errors; it's good to make this kind of process as automatic as possible; KNIME Server profiles achieve this kind of automation.

Author: Michael Berthold (KNIME). As first published in The New Stack.

There is a big push for automation in data science today. Given how complex programming data science applications can be, that is no surprise. It takes years to truly master scripting or programming languages for data analysis — and that’s ignoring that one needs to build actual data science expertise as well. However, code-free solutions can make the nuts and bolts of data science a lot more accessible. This means that the valuable time of data science teams can be spent on actually doing data science so that organizations don’t have to rely on an external, preconfigured, and intransparent data science automation mass product.

Authors: The Evangelism Team at KNIME

End to end data science is a phrase you might have read before, but what does it mean?

The terms “read - transform - analyze - deploy” describe the typical phases of a data science project. We often have our favorite phases of the model we enjoy focusing on, but data science is only end to end if the final deployment phase is also included. This is where data science brings value to a business, when the data science applications that have been created are subsequently productionized.

Authors: Rosaria Silipo and Paolo Tamagnini (KNIME)

Nobody is an island. Even less so a data scientist. Assembling predictive analytics workflows benefits from help and reviews: on processes and algorithms by data science colleagues; on IT infrastructure to deploy, manage, and monitor the AI-based solutions by IT professionals; on dashboards and reporting features to communicate the final results by data visualization experts; as well as on automatization features for workflow execution by system administrators. It really seems that a data scientist can benefit from a community of experts!

Anonymization is a hot topic of discussion. We are generating and collecting huge amounts of data, more than ever before. A lot of this data is personal and needs to be handled sensitively. In recent times, we’ve also seen the introduction of the GDPR stipulating that only anonymized data may be used extensively and without privacy restrictions.

Author: Rosaria Silipo (KNIME)

As first published in Dataversity

Sometimes when you talk to data scientists, you get this vibe as if you’re talking to priests of an ancient religion. Obscure formulas, complex algorithms, a slang for the initiated, and on top of that, some new required script. If you get these vibes for all projects, you are probably talking to the wrong data scientists.

Author: Armin Ghassemi Rudd (Data Scientist & Consultant)

Are you trying to build an attractive CV? Maybe you’ve been searching the web for online CV builders? Using these online CV builders, you have to fill out a form and enter your information like name, contact information, skills, experiences, and so on. There are a few online CV builders that ease the job for you and ask for permission to access your LinkedIn profile and read your information. They are great tools for sure, but they have down points as well.

As first published in Harvard Data Science Review.

Abstract

Given recent claims that data science can be fully automated or made accessible to nondata scientists through easy-to-use tools, I describe different types of data science roles within an organization. I then provide a view on the required skill sets of successful data scientists and how they can be obtained, concluding that data science requires both a profound understanding of the underlying methods as well as exhaustive experience gained from real-world data science projects. Despite some easy wins in specific areas using automation or easy-to-use tools, successful data science projects still require education and training.

Authors: Rosaria Silipo and Mykhailo Lisovyi

Today’s style: Caravaggio or Picasso?

While surfing on the internet a few months ago, we came across this study¹, promising to train a neural network to alter any image according to your preferred painter’s style. These kinds of studies unleash your imagination (or at least ours).