Competent analysis is not only about understanding statistics, but about implementing the correct statistical approach or method. In this brief article I will showcase some common statistical blunders that we generally make and how to avoid them. To make this information simple and consumable I have divided these errors into
Using tabs or spaces when writing a new line of code has been one of the fiercest battles ever fought among coders. Because we don’t live in a perfect world where everybody indents and aligns according to the same standards, the debate is ultimately reduced to how source-code is displayed in editing software.
The last stage of the STORM 80-day tour will be driven on November 2 – from Paris to Eindhoven, accompanied by many electrical vehicles. Hacking at ITility In the meantime, we used the results from the hackathon at Stanford University, to dig deeper into that same data set during the
This week, the STORM team arrived in China – after passing 13 countries and 12 borders, and after a long, long wait at the border to receive Chinese driver licenses. Now there will be 16 days spent in this one country. In this post we’ll find out something more about
“The market still needs a little more time to get ready for autonomous driving.”- Interview with Christian Bubenheim
Since January 2015 Christian Bubenheim has been Senior Vice President Marketing & Product of AutoScout24. Before that he was part of the management team of Amazon Deutschland GmbH and responsible for the business unit „Consumables“ including health & beauty as well as foods. From 2003 to 2008 he was General
Data Natives Berlin speaker Allan Hanbury is Senior Researcher and Privatdozent at the TU Wien, Austria. Since 2010, he has coordinated EU-funded research and development projects on analysis and search of medical text and image data. This led to the recent founding of a start-up, ContextFlow, which is bringing the
In the world of data analysis, one tool is often left unused. While being a very powerful analytics tool, cohorts are often pushed aside due to their seemingly complex nature. With a lot to offer in the way of data analysis, let’s take a deeper (yet simplified) look into cohorts.
Data scientists suffer needlessly when they don’t account for the time it takes to properly complete all of the steps of exploratory data analysis There’s a scourge terrorizing data scientists and data science departments across the dataland. This plague infects even the best data scientists, causing missed deadlines, overrun budgets
Highly effective data analysis isn’t learned overnight, but it can be learned faster. Here are 7 habits of data analysis I wish someone told me for effectively incorporating, communicating and investing in data analysis geared towards an engineering team. 1. Value simplicity of analysis over fancy algorithms If you can’t
Dimensionality reduction as means of feature extraction Feature extraction is a very broad and essential area of data science. It’s goal is to take out salient and informative features from input data, so that they can be used further in predictive algorithms. Modern data scientists observe large amounts of data,