AI Guild Podcast: Data Science Interviews

On turning the tables during job interviews as you experience grows Some time ago, I sat down with Leyla from the AI Guild to talk about my own Data Science path but also my experience in job interviews for data positions. Recently, as I’ve been interviewing for positions, I wondered how to find out some more sensitive topics. How is the company keeping it with diversity? Is diversity important for them and do they take active measures to increase e. »

Visa Costs meet Data Viz

I recently stumbled across this data set about visa costs. It is a collection of visa costs for all countries for different kind of visas (tourist, business, work, transit, and some other visas). Each row corresponds to visa relations between a source country (the country applying for the visa) and a target country (the country issuing the visa) together with the cost for the different visa types. Since I had a bit of free time on my hand, I decided to do some “plotcrastinating”, play around with the data and try out some new visualizations. »

Connecting Disinformation with tidygraph

I recently participated in a hackathon organised by EU’s anti-disinformation task force where they gave us access to their data base. The data base consists of all disinformation cases the group has collected since it started in 2015. Their data can also be browsed online on their web page www.euvsdisinfo.eu. The data contains more than 7000 cases of disinformation, mostly news articles and videos, that were collected and debunked by the EUvsDisinfo project. »

House-Cleaning: Getting rid of outliers II

In the previous post, we tried to clean rental offerings of outliers. We first just had a look at our data and tried to clean by simply using threshold derived from our own knowledge about flats with minor success. We got slightly better results by using the IQR rule and learned two things: First, the IQR rule works better if our data is normally distributed and, if it’s not, transforming it can work wonders. »

House-Cleaning: Getting rid of outliers I

Working with real-world data presents many challenges that sanitized text book data doesn’t have. One of them is how to handle outlier. Outliers are defined as points that differ significantly from other data points and they are especially common in data obtained through manual input processes. For example, on an online listing site, someone might accidentally pressed the zero-key a bit too often and suddenly the small rental flat is as expensive as a palace. »