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Overview of a talk I gave at a deep learning course, focusing on AI ethics as the need for humans to think on the context and consequences of applying AI.

My reasons for switching from WordPress.com to Hugo on GitHub + Cloudflare, along with a summary of the solution components and migration process.

My track record of posting here has been pretty poor in 2020, partly because of a bunch of content I’ve contributed elsewhere. In general, my guiding principle for posting is to only add stuff I’d want to read or cite, e.g., because I haven’t seen it discussed elsewhere. Well, no one has compiled a meta-post of my public work from 2020 (that I know of), so it’s finally time to publish it myself....

Previously, I encouraged readers to test different approaches to bootstrapped confidence interval (CI) estimation. Such testing can done by relying on the definition of CIs: Given an infinite number of independent samples from the same population, we expect a ci_level CI to contain the population parameter in exactly ci_level percent of the samples. Therefore, we run “many” simulations (num_simulations), where each simulation generates a random sample from the same population and runs the CI algorithm on the sample....

The passage of time makes wizards of us all. Today, any dullard can make bells ring across the ocean by tapping out phone numbers, cause inanimate toys to march by barking an order, or activate remote devices by touching a wireless screen. Thomas Edison couldn’t have managed any of this at his peak—and shortly before his time, such powers would have been considered the unique realm of God....

Earlier this year, I gave a talk titled A Day in the Life of a Remote Data Scientist at the Data Science Sydney meetup. The talk covered similar ground to a post I published on remote data science work, with additional details on my daily schedule and projects, some gifs and Sydney jokes, heckling by the audience, and a Q&A session. I managed to watch it a few months ago without cringing too much, so it’s about time to post it here....

Bootstrapping the right way is a talk I gave earlier this year at the YOW! Data conference in Sydney. You can now watch the video of the talk and have a look through the slides. The content of the talk is similar to a post I published on bootstrapping pitfalls, with some additional simulations. The main takeaways shared in the talk are: Don’t compare single-sample confidence intervals by eye Use enough resamples (15K?...

One of the main projects I worked on last year. Recently, Automattic created a Marketing Data team to support marketing efforts with dedicated data capabilities. As we got started, one important question loomed for me and my teammate Demet Dagdelen: What should we data scientists do as part of this team…? Read more on data.blog

Bootstrap sampling techniques are very appealing, as they don’t require knowing much about statistics and opaque formulas. Instead, all one needs to do is resample the given data many times, and calculate the desired statistics. Therefore, bootstrapping has been promoted as an easy way of modelling uncertainty to hackers who don’t have much statistical knowledge. For example, the main thesis of the excellent Statistics for Hackers talk by Jake VanderPlas is: “If you can write a for-loop, you can do statistics”....

I’ve been interested in the area of causal inference in the past few years. In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work. Now, I believe I’ve finally found a book with practical techniques that I can use on real problems: Causal Inference by Miguel Hernán and Jamie Robins....