There’s an old joke in rock climbing: “Sport climbing is neither.” Old-school climbers argue it’s not a sport because there’s no clock, no scoreboard, and no clear winner. They also say it’s not “real” climbing since you clip bolts the whole way and lower off anchors instead of reaching the top of something wild and remote. We usually say this half-seriously and half to poke fun at ourselves. Recently, I’ve been thinking that data science is also neither. ...
Freerider Ground Up And now for something completely different. All Monty Python references completely aside, a friend and I got to talking about personal websites and made me realize I had let mine go stagnant. I wanted to write about my experience climbing El Cap in the spring of 2022 and this seemed like the best way to both revive my website and record my experience for posterity. So, here we go: ...
I recently had the opportunity to attend Rstudio::conf2020 in San Francisco and it was such an enjoyable event. I had the opportunity to meet some of the best and brightest minds in the R community and reaffirm my appreciation of Rstudio and community they have fostered. I wanted to spend a little time reflecting on the event. Keras and Tensorflow in R Prior to the conference, I elected to take a couple additional days off work to take a workshop about deep learning with my good friend and coworker Jen. I wanted to take this workshop because I don’t have the opportunity to tackle many deep learning problems in my day job. I have done a little work with Keras and tensorflow in Python, but I wanted to see what the API was like in R. Also, it seemed like the workshop with the biggest learning opportunity. ...
Picture this scenario: you are building a forecasting tool that leverages time series data and you are deciding on your features. Inevitably you get to the question: how do I handle my cyclic variables such as day of year, day of week, and time of day? These features, whilst not exceptionally explainable, tend to provide a lot of predictive power and should variably go into your model if you don’t have access to the true feature. So what do you do with them? Well we have several options in our toolkit. The first is to take delve into the forecasting realm and use models such as ARIMA, or RNNs. These are great models and are commonly used. However, I’d argue that the most common approach is to just hot encode the cyclic variables and run them through a machine learning pipeline. I think we can do better. We can borrow some approaches from forecasting analysis and use Fourier transforms. This dramatically reduces the dimensionality of the data and provides similar results in less time. ...
Now that I’ve gotten the required first data science/programing blog title out the way, (don’t worry I hear your groans and admonishment from here) welcome! My name is Eric Stern and I am a data scientist based out of Boulder Colorado. I work in the clean energy and the energy efficiency realm. I am most interested in how we can use behaviorial sciences to solve large problems in our climate crisis (more about that in a future post 😊). ...