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Reproducibility, the open sharing of data, and expanding on the research of others are all at the heart of the scientific process, and we live in an exciting time where it is more possible than ever. This year's Inspire Europe Closing Keynote speaker Dr. Ben Goldacre has recently published a paper examining compliance with the European Commission's guideline that all Clinical Trials registered in the EU Clinical Trials Register must report results to the European Medicines Agency within 12 months of the trial's completion. The bulk of the paper's analysis was performed in the statistical software Stata. With tools like Alteryx or Python, we have easy and open-source ways to process data and derive new knowledge. In this blog, we reproduce some of Goldacre et al.'s analysis in Alteryx and Python and provide both formats for you to further explore the data on your own.
Neural Networks are an approach to artificial intelligence that was first proposed in 1944. Modeled loosely on the human brain, Neural Networks consist of a multitude of simple processing nodes (called neurons) that are highly interconnected and send data through these network connections to estimate a target variable. In this article, I will discuss the structure and training of simple neural networks (specifically Multilayer Perceptrons, aka "vanilla neural networks"), as well as demonstrate an example neural network created by the Alteryx Neural Network Tool.
Building my first linear regression model turned me into an instant celebrity. My roommate, who has acted as a sounding board for my predictive-analytics-learning progress, now believes I can use Linear Regression to predict the winner of the next horse race. While it would be fun to try, a more applicable use case is predicting how much a customer will spend (which, in the case of horse racing could translate to how much someone might spend on a bet). For my use case, I want to predict how much a Lyft driver can expect to receive on their next fare.
Ever wondered how to build a new analytic tool from scratch using the Alteryx Python SDK, but didn’t know where to start? This blog post takes you through the absolute basics to get you up and running - You’ll be creating brand new tools, connectors and advanced analytics in no time with this step-by-step beginners guide!
Voronoi Tesselation and Delaunay Trianglulation both perform spatial calculations on a set of irregular points. Voronoi Cells (sometimes referred to as Thiessen Polygons in the GIS world) make up a Voronoi Tesselation, which is the partitioning of a plane into polygons based on a set of points, so that for each point there is a corresponding polygon where the area in the polygon is closer to the corresponding point than any other point. Delaunay Triangulation is when a set of irregular points are divided into triangles, so that no point in the set is inside the circumcircle of any triangle created from the points.
Both of these processes have a bunch of really neat spatial analysis applications. In this article, we will talk about their implementation in Alteryx.
Have you ever tried to explore those mysterious tools with an R in the lower right-hand corner and been too confused to continue? Or have you not even installed the Predictive, Time Series, and Predictive Grouping tools? Well, here’s your chance to clear through your fog of confusion and utilize these powerful tools.