GETTING STARTED | KNIME ANALYTICS PLATFORM

Here are seven steps for a fast and practical, learning-by-doing start to using it. After you’ve got started, take a look at more educational material, like for example one of our e-learning courses, onsite courses, cheatsheets, e-books, videos, local meetup events, and more. Our “sat nav” for finding the educational resources that most suit your skills and time constraints is here in the blog article Get on Board and Navigate the Learning Options at KNIME!

The Seven Things To Do

1. Explore the welcome page
2. Learn from a pre-built workflow
3. Get familiar with the workbench of KNIME Analytics Platform
4. Find more resources on the KNIME Hub…

Components | Metanodes | KNIME Analytics Platform

Let’s help clarify the differences between metanodes and components in KNIME Analytics Platform.

The common goal: make order in a messy workflow

Both metanodes and components are useful to clean up messy workflows. You can identify isolated blocks of logical operations in your workflows and include them inside either a metanode or a component. Your workflow will appear neat and tidy with less nodes than the original workflow.

And that is where the metanode goal in life ends.

What can a component do that a metanode cannot?

Let’s see now what a component can do additionally in comparison with a metanode.

A component can encapsulate flow variables

“What happens in the component stays in the component.” This sentence describes the vacuum character of a…

VISUAL PROGRAMMING | OPEN SOURCE | KNIME ANALYTICS PLATFORM

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. …

TUTORIAL | PYTHON | KNIME ANALYTICS PLATFORM

Introduction

In this article I create a step by step data science pipeline using a visual and codeless workflow with KNIME.

KNIME is an open source environment where it’s possible to gather and wrangle data, or make sense of it through modeling and visualization techniques, in a drag and drop fashion. This enables the user to forget about coding and focus on the data, data science models and concepts.

Furthermore, a KNIME visual workflow could be effective in increasing the pipeline readability and maintainability in time.

Who Should Read This Article

Python is the most widely used programming language for data science, and many other languages…

OPEN SOURCE | VISUAL PROGRAMMING | KNIME ANALYTICS PLATFORM

by The Editorial Board

As we all know, the data science community never rests. It is time to start a new adventure: A new journal for and by the KNIME community.

We firmly believe that there is still a need for Yet Another Journal about Data Science!

We need more knowledge and information about machine learning algorithms, data science techniques, best practices, open-source tools, and case studies.

In particular, we need to show and learn how to implement advanced data science solutions using an open-source, low-code approach, underlining the advantages of visual programming for fast learning, collaboration, and documentation. …

Author: Rosaria Silipo

I get, often, questions like these from young aspiring data scientists: “What do I need to become a data scientist? Is the three-month course I am taking on Coursera enough? Do I need to learn how to code? Do I need to learn Python? How many years of hands-on experience do I need?” And many other similar questions.

Data Science has emerged in the last few years as one of the most in demand skills. Data Scientists are sought after and often paid well enough. Obviously, the interest has also increased. What does it take to become…

Interviewer: Paolo Tamagnini, KNIME

Some time ago, after his/her keynote talk at an important conference, a colleague of mine, made the statement that nowadays, every presentation about artificial intelligence (AI) [and related challenges for the future] need to be at least apocalyptic. It is true. By now, most documentaries, shows, TED talks, keynotes, and similar presentations clearly use a very technophobic tone. How much of this is true?

Are we really doomed and should we just surrender to AI?

Is AI going to take over humanity?

Is AI black magic?

Is it true that once you set an AI application…

By Maarit Widmann, Data Scientist, KNIME

Quantitative data have endless stories to tell!

Daily closing prices tell us about the dynamics of the stock market, small smart meters about the energy consumption of households, smartwatches about what’s going on in the human body during an exercise, and surveys about some people’s self-estimation of a topic at some point in time. Different types of experts can tell these stories: financial analysts, data scientists, sports scientists, sociologists, psychologists and so on. Their stories are based on models, for example, regression models, time series models and ANOVA models.

Why Are Numeric Scoring Metrics…