Hi! Meet my avatar. I think it looks a bit like me, don’t you think? In a low-resolution kind of way…
My avatar and I are attending the next KNIME Data Talks — Community Edition, where we hope to meet and network with other KNIME user avatars. Yes, for the next KNIME Data Talks event, you need to come with your own avatar!
Let’s proceed now with a little more order.
The KNIME Data Talks — Community Edition will take place on July 7 at two different times: 10:00 AM UTC +2 (Berlin) and 12:00 PM UTC -5 (Chicago). Same…
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!
Let’s help clarify the differences between metanodes and components in KNIME Analytics Platform.
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.
Let’s see now what a component can do additionally in comparison with a metanode.
“What happens in the component stays in the component.” This sentence describes the vacuum character of a…
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. …
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.
Python is the most widely used programming language for data science, and many other languages…
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…
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…
Rosaria has been mining data since her master degree, through her doctorate and job positions after that . She is now a data scientist and KNIME evangelist.