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).

Two decades into the AI revolution, deep learning is becoming a standard part of the analytics toolkit. Here’s what it means

By Michael Berthold, KNIME

Pick up a magazine, scroll through the tech blogs, or simply chat with your peers at an industry conference. You’ll quickly notice that almost everything coming out of the technology world seems to have some element of artificial intelligence or machine learning to it. The way artificial intelligence is discussed, it’s starting to sound almost like propaganda. Here is the one true technology that can solve all of your needs! AI is here to save us all!

Authors: Ana Vedoveli and Iris Adä (KNIME)

At the beginning of this year, we sent out a “Help us to Help you with KNIME” survey to the KNIME community. The idea behind the questionnaire was to listen to what the KNIME community wanted and incorporate some of those suggestions into the next releases. There were a few questions about how people are using KNIME Analytics Platform, and also questions designed to help us understand what kinds of new nodes and features people dream about. We additionally promised that we would select one dedicated node - the node most mentioned - and make sure that it would be part of our next major release.

In this post we present this "community node" and we've also put together five tips & tricks garnered from other answers given in the survey.

Authors: Kathrin Melcher, Rosaria Silipo

Author: Julian Bunzel

Keeping track of the latest developments in research is becoming increasingly difficult with all the information published on the Internet. This is why Information Extraction (IE) tasks are gaining popularity in many different domains. Reading literature and retrieving information is extremely exhausting, so why not automate it? At least a bit. Using text processing approaches to retrieve information about drugs has been an important task over the last few years and is getting more and more important¹.

Authors: Paolo Tamagnini and Rosaria Silipo

The ugly truth behind all that data

We are in the age of data. In recent years, many companies have already started collecting large amounts of data about their business. On the other hand, many companies are just starting now. If you are working in one of these companies, you might be wondering what can be done with all that data.

What about using the data to train a supervised machine learning (ML) algorithm? The ML algorithm could perform the same classification task a human would, just so much faster! It could reduce cost and inefficiencies. It could work on your blended data, like images, text documents, and just simple numbers. It could do all those things and even get you that edge over the competition.

Authors: Scott Fincher, Paolo Tamagnini, Maarit Widmann

No matter if we are experienced data scientists or business analysts, one of our daily routines is the easy and smooth extraction of the relevant information from our data regardless of the kind of analysis we are facing.

A good practice for this is to use data visualizations: charts and graphs to visually summarize the complexity in the data. The required expertise for data visualization can be divided in two main areas:

• The ability to correctly prepare and select a subset of the dataset columns and visualize them in the right chart
• The ability to interpret the visual results and take the right business decisions based on what is displayed

Authors: Alfredo Roccato (Data Science Trainer and Consultant) and Maarit Widmann (KNIME)

Wheeling like a hamster in the data science cycle? Don’t know when to stop training your model?

Model evaluation is an important part of a data science project and it’s exactly this part that quantifies how good your model is, how much it has improved from the previous version, how much better it is than your colleague’s model, and how much room for improvement there still is.

In this series of blog posts, we review different scoring metrics: for classification, numeric prediction, unbalanced datasets, and other similar more or less challenging model evaluation problems.