It can move autonomously in an unfamiliar confined environment and create a detailed map. This is the role of a six-legged rescue robot whose “brain” was designed and tested by Master’s student Jan Bayer from the Faculty of Electrical Engineering at the Czech Technical University (CTU) in Prague. His precisely executed Master’s project with great potential for society at large won him …
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Why Business Intelligence Projects Fail (And How to Avoid It)
by Petr Hájek, Information Management Professional This picture was randomly taken at a small local Asian marketplace. It literally (and I think unintentionally) offers a single item for the price of 3 items. This is funny when you are buying a T-shirt. It is not funny if you are spending millions. It reminds me of a colleague of mine who always says that when a company decides …
Book Review: ‘Fundamentals of Data Visualization‘ by Claus O. Wilke
by Dominik Matula, Data Science Consultant Book details: 390 pages ISBN-13: 978-1492031086 ISBN-10: 1492031089 About the Author: Claus O. Wilke is a computational and evolutionary biologist and chair of the Department of Integrative Biology at University of Texas at Austin, where Wilke studies the evolution of molecules and viruses using theoretical and computational methods. He is also the author of the Cowplot and ggridges plotting packages. A whole book about …
Book Review: ‘How not to be wrong’ by Jordan Ellenberg
by Sergii Stamenov, Data Science Consultant About the Author: Jordan Ellenberg is the John D. MacArthur Professor of Mathematics at the University of Wisconsin-Madison and a 2015 Guggenheim fellow. Author of a few books on number theory. He has written articles on mathematical topics in the New York Times, Washington Post, Wall Street Journal, and many others. Why I liked this book: It’s fun …
AI in Banking – 4 major pain points in the DACH region
by Barbora Janulíková, Business Development Manager Despite the common thrashing German, Austrian, and Swiss banks often get at conferences relevant to the topic; they do put a significant amount of effort into adopting AI for meaningful use cases. However, as with all innovations, the financial industry is still dealing with some pain points concerning AI. 1. Digital Washing – Chatbots are no …
Applied Data Science Research
How Profinit combines scientific research with applications in finance by Barbora Janulíková, Business Development Manager If you have been following Profinit for a while, you know that academic research is deeply encoded in our company‘s DNA. We only pick new colleagues with the best education in mathematical and technical sciences. Moreover, our consultants teach Software Engineering or Big Data technologies at …
Beyond Data-ism: Big Data and the Human Intuition
by Petr Hájek, Information Management Advisor What is intuition? The old ‘wisdom’ says that managers spend their budgets on data and analytics, but finally, they make decisions based mostly on their intuition. Intuition can be explained in many ways – some say that intuition is irrational as opposed to our otherwise rational way of thinking. Another explanation is that intuition …
Tips for a Data Science job interview
1) Brush up on basic maths and statistical concepts You don´t want to stutter when asked about basic concepts such as ROC curves, logistic regression, etc. Can you explain the difference between supervised and unsupervised learning both to a child and a math major? Good! 2) Prepare a set of practical examples to illustrate your value Data Science is all about …
4 Facts about the Global Data Science Job Market
1. Employers are often looking for a unicorn data scientist Everyone working in the field is well aware that the skill set a data scientist brings to the job can vary widely depending on the context. A combination of statistical background, coding knowledge, and business acumen are definitely required, but to find an individual that will have expert abilities in all three …
Book Review: The Book of Why – J. Pearl
by Zdeněk Veselý, Data Scientist at Profinit Why the why? As a statistician or data scientist, I want to answer many why questions. • Why are these customers buying the product? • Why are these patients not getting better? • Why is the number of defaults rising? • Why… And it seems there are many tools in my data scientist toolbox to …