Tag Archives: Data Analysis

Geolocation with Google API’s & Python – mapping addresses to GPS coordinates

Google does some pretty impressive things – not just all the web-based search stuff, but they also have lot’s and lot’s of really cool API’s for programmers to peruse. In order to access these API’s, you need a personal authorization … Continue reading

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Vasaloppet 2018 – race time analysis

An analysis of race times for the ~11000 men and ~2000 women that participated in 2018 Vasaloppet. For explanations of the graphs, see earlier posts on Marcialonga or Tour de Ski. [Btw, the weird looking vertical orange/blue “spike” in the … Continue reading

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Marcialonga Ski 2018 – some Analytics

Now, with the power grid finally – after 62 hours! – back in business, I’m able to continue my stats/analytics exploration of the past Marcialonga ski race. First, some basic stats about the race: Total number of participants: 5558, of … Continue reading

Posted in Bayes, Data Analytics, Numpy, Pandas, Probability, PYMC, Python, sports, Statistics | Tagged , , , , , , , , , | Leave a comment

Ugly soup with Python, requests & Beautiful Soup

Web scraping has never been a coveted nor favorite discipline of mine; in fact, for me web scraping is an unfortunate, but sometimes necessary evil. Scraping web-pages, at least for me, is a very unstructured process, basically pure Trial & … Continue reading

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Gender is not a social construct, but a biological reality, clearly demonstrated in sports

Just a quick demo to debunk the contemporary notion that “Gender is a social construct”. Data taken from today’s Tour the Ski sprint qualification times, for the top 30 women vs men, where both men & women used the same … Continue reading

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Bayesian Multi-predictor Regression – Valet2018

[Continuing my exploration of the Swedish election results, but I thought this might be of interest also for those of you not very interested in the Swedish elections, simply because the potential MatStat’s  insights – thus, the text is in … Continue reading

Posted in Bayes, Big Data, Data Analytics, Data Driven Management, Numpy, Politik, Probability, PYMC, Python, Research, Society, Statistics, Sverige | Tagged , , , , , , , , , , | 1 Comment

Val2018 – top50 & bottom50 samtliga valdistrikt i Sverige för samtliga partier

Som komplement till de två tidigare inläggen [1,2] som redogjorde för valdistrikten i Stockholms kommun, så kommer här top50 & bottom50 för samtliga de 6004 valdistrikt som finns med i valnattens preliminära resultat. Top-50: Bottom-50:

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Poor Man’s Climate Change exploration

The Swedish Meteorological & Hydrological Institute (SMHI) provide Open Data, that is, some of its huge databases on all things weather. I downloaded data on temperature from an observation post furthest out in the Stockholm archipelago, daily data on temperatures … Continue reading

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Convolutional Neural Networks with KERAS – Image recognition

A quick test shot with KERAS, inspired by this tutorial using the MNIST dataset of more than 60000 images of hand written digits. Task at hand: correctly identify as many as possible of these 28 x 28 images, looking like … Continue reading

Posted in Big Data, Complex Systems, Data Analytics, KERAS, Machine Learning, Neural networks, Numpy, Python | Tagged , , , , , , , | 2 Comments

Capturing NMEA sentences over WiFi using Python

In order to figure out how the NMEA-WiFi Gateway deals with clients, e.g. if it expects any “handshake” or any other communication setup protocol, I decided to write a simulator mimicing the gateway, and then using iRegatta 2 from Zifago … Continue reading

Posted in Data Analytics, Maritime Technology, Nautical Information Systems, NMEA, Numpy, performance, Python, Simulation, TCPIP | Tagged , , , , , , , , , , , , | Leave a comment