Thomas is a bioinformatician, turned software engineer, who enjoys developing tools for data scientists. His main interests are in the tools that bring the scientist closer to their data, whether it be through intuitive and powerful APIs or through visualization. He describes himself as a creative spirit who enjoys photography as well as generative art and graphic design, and he tends to try and combine this with his interest in programming whenever possible. Thomas lives just north of Copenhagen with his wife and two kids.
Neural Networks 101
Machine Learning Classifier: Basics and Evaluation
Demystifing Graph Algorithms
Datacast Episode 6: Data Science in the Travel Industry with Ewan Nicolson
Datacast Episode 5: Applied Statistics in Data Science with Christopher Peters
Christopher Peters is a full-stack data scientist at Zapier. He was both Zapier and Treehouse's first data scientist. Prior to his work as a data scientist, he was a research associate at LSU’s Center for Energy Studies where he was an energy economist. He has a real passion for working with, sharing, visualizing and analyzing data of all kinds using statistical, visual and machine learning techniques.
Greedy Algorithm and Dynamic Programming
In an algorithm design there is no one 'silver bullet' that is a cure for all computation problems. Different problems require the use of different kinds of techniques. A good programmer uses all these techniques based on the type of problem. In this blog post, I am going to cover 2 fundamental algorithm design principles: greedy algorithms and dynamic programming.