By: AJDA, May 3, 2018
Janez and I have recently returned from a two-week stay in Moscow, Russian Federation, where we were teaching data mining to MA students of Applied Statistics. This is a new Master’s course that attracts the best students from different backgrounds and teaches them statistical methods for work in the industry. It was a real pleasure working at HSE. The students were proactive by asking questions and really challenged us to do our best.
By: AJDA, Mar 28, 2018
We have just concluded our enhanced Introduction to Data Science workshop, which included several workflows for spectroscopy analysis. Spectroscopy add-on is intended for the analysis of spectral data and it is just as fun as our other add-ons (if not more!). We will prove it with a simple classification workflow. First, install Spectroscopy add-on from Options - Add-ons menu in Orange. Restart Orange for the add-on to appear. Great, you are ready for some spectral analysis!
By: AJDA, Jan 5, 2018
We all know that sometimes many is better than few. Therefore we are happy to introduce the Stack widget. It is available in Prototypes add-on for now. Stacking enables you to combine several trained models into one meta model and use it in Test&Score just like any other model. This comes in handy with complex problems, where one classifier might fail, but many could come up with something that works. Let’s see an example.
By: AJDA, Nov 17, 2017
We’ve been having a blast with recent Orange workshops. While Blaž was getting tanned in India, Anže and I went to the charming Liverpool to hold a session for business school professors on how to teach business with Orange. Related: Orange in Kolkata, India Obviously, when we say teach business, we mean how to do data mining for business, say predict churn or employee attrition, segment customers, find which items to recommend in an online store and track brand sentiment with text analysis.
By: AJDA, Aug 4, 2017
As always, we’ve been working hard to bring you new functionalities and improvements. Recently, we’ve released Orange version 3.4.5 and Orange3-Text version 0.2.5. We focused on the Text add-on since we are lately holding a lot of text mining workshops. The next one will be at Digital Humanities 2017 in Montreal, QC, Canada in a couple of days and we simply could not resist introducing some sexy new features_._ Related: Text Preprocessing
By: AJDA, Mar 9, 2017
Why is Orange so great? Because it helps people solve problems quickly and efficiently. Sašo Jakljevič, a former student of the Faculty of Computer and Information Science at University of Ljubljana, created the following motivational videos for his graduation thesis. He used two belowed datasets, iris and zoo, to showcase how to tackle real-life problems with Orange.
By: AJDA, Feb 23, 2017
Recently, I took on a daunting task - programming my first widget. I’m not a programmer or a computer science grad, but I’ve been looking at Orange code for almost two years now and I thought I could handle it. I set to create a simple Concordance widget that displays word contexts in a corpus (the widget will be available in the future release). The widget turned out to be a little more complicated than I originally anticipated, but it was a great exercise in programming.
By: AJDA, Jan 23, 2017
One of the key questions of every data analysis is how to get the data and put it in the right form(at). In this post I’ll show you how to easily get the data from the web and transfer it to a file Orange can read. Related: Creating a new data table in Orange through Python First, we’ll have to do some scripting. We’ll use a couple of Python libraries - urllib.
By: AJDA, Dec 12, 2016
The new Orange release (v. 3.3.9) welcomed a few wonderful additions to its widget family, including Manifold Learning widget. The widget reduces the dimensionality of the high-dimensional data and is thus wonderful in combination with visualization widgets. Manifold Learning widget has a simple interface with powerful features. Manifold Learning widget offers five embedding techniques based on scikit-learn library: t-SNE, MDS, Isomap, Locally Linear Embedding and Spectral Embedding. They each handle the mapping differently and also have a specific set of parameters.
By: AJDA, Nov 30, 2016
Being a political scientist, I did not even hear about data mining before I’ve joined Biolab. And naturally, as with all good things, data mining started to grow on me. Give me some data, connect a bunch of widgets and see the magic happen! But hold on! There are still many social scientists out there who haven’t yet heard about the wonderful world of data mining, text mining and machine learning.