Editor's Picks

• R @ AZ: Building a Community in the Pharmaceutical Industry
  Back in early 2021, a few of us at AstraZeneca got together and reflected on the growing importance of R in our organization. R was far from being a new thing at AstraZeneca and a long way from being a fad...However, we didn’t know who the R users at AstraZeneca were, and there wasn’t any kind of forum where R users could meet...So we decided it was time to start a community of R users at AstraZeneca...then the question was: how to get started?...

• A Framework for Generating Research Ideas [Google Doc]
  Coming up with good research ideas, especially when you’re new to a field, is tough – it requires an understanding of gaps in literature. However, the process of generating research ideas can start after reading a single research paper. In this lecture, I share a set of frameworks with you to help you generate your own research ideas. First, you will learn to apply a framework to identify gaps in a research paper, including in the research question, experimental setup, and findings. Then, you will learn to apply a framework to generate ideas to build on a research paper, thinking about the elements of the task of interest, evaluation strategy and the proposed method. Finally, you will learn to apply a framework to iterate on your ideas to improve their quality...

• Seven Sins of Numerical Linear Algebra
  In numerical linear algebra we are concerned with solving linear algebra problems accurately and efficiently and understanding the sensitivity of the problems to perturbations. We describe seven sins, whereby accuracy or efficiency is lost or misleading information about sensitivity is obtained...

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Data Science Articles & Videos

• The Contract-Powered Data Platform
  A very significant amount of my time over the past five years has been dedicated to data platform automation, reducing cross-team friction, and improving data quality...Schemas have played a critical role in the process; this post outlines the why and the how. But before diving straight into the role of schemas (er... contracts) let's talk data platforms...

• How dbt fails
  In the data industry, no company is more likely to end up in an HBS classroom than dbt Labs...Though I don’t think that it’ll happen, there is some dark timeline out there that ends with a room full of twenty-something MBA students having a snotty debate about how they would’ve saved dbt Labs from its eventual fall from grace. Prior to that discussion, they’ll all read a case study that chronicles what went wrong...This is my guess as to what it’ll say...

• Introduction to the Conjugate Gradient Method Without the Agonizing Pain
  The Conjugate Gradient Method is the most prominent iterative method for solving sparse systems of linear equations. Unfortunately, many textbook treatments of the topic are written with neither illustrations nor intuition, and their victims can be found to this day babbling senselessly in the corners of dusty libraries. For this reason, a deep, geometric understanding of the method has been reserved for the elite brilliant few who have painstakingly decoded the mumblings of their forebears. Nevertheless,the Conjugate Gradient Method is a composite ofsimple, elegantideas that almost anyone can understand. Of course, a reader as intelligent as yourself will learn them almost effortlessly...

• Why Data Cleaning is Failing Your ML Models – And What To Do About It
  Model accuracy doesn’t start or end with data cleaning in your notebook with the few tables you use to inform, train, and validate your model. It starts with the ETL pipeline and the instant you choose what to measure to solve your problem...Let’s walk through a semi-hypothetical scenario that contains real examples we’ve seen in the wild to highlight some common failure points. We’ll then discuss how they can be avoided with an organizational commitment to high-quality data...

• Bad Data, or Interesting Fact?
  Data quality is definitely boring...What makes things interesting is the combination of problem solving and interesting facts...Data quality techniques can be used to find interesting insights...

• How the Guardian approaches quote extraction with NLP
  A recent trend for media companies is to explore how fields like Natural Language Processing (NLP) and Information Extraction (IE) can modularize content like a long-form article as reusable elements for different storytelling formats (e.g., a podcast, information graphic, or blog). This push is called modular journalism and many media companies are building towards it to automate customized stories to meet individual user needs for a variety of media forms...

• Microsoft adds DALL-E to its Office suite
  Microsoft is adding AI-generated art to its suite of Office software with a new app named Microsoft Designer...The app functions the same way as AI text-to-image models like DALL-E and Stable Diffusion, letting users type prompts to “instantly generate a variety of designs with minimal effort.”...

• Pooling In Convolutional Neural Networks
  In this article, we explore the whys and the hows behind the fundamental process of pooling in CNN architectures, and then compare two common techniques: max and average pooling...

• Low-Rank Approximation Toolbox: Nyström Approximation
  As I discussed in a previous post, many matrices we encounter in applications are well-approximated by a matrix with a small rank. Efficiently computing low-rank approximations has been a major area of research, with applications in everything from classical problems in computational physics and signal processing to trendy topics like data science. In this series, I want to explore some broadly useful algorithms and theoretical techniques in the field of low-rank approximation...

• Mastering the Game of No-Press Diplomacy via Human-Regularized Reinforcement Learning and Planning
  No-press Diplomacy is a complex strategy game involving both cooperation and competition that has served as a benchmark for multi-agent AI research. While self-play reinforcement learning has resulted in numerous successes in purely adversarial games like chess, Go, and poker, self-play alone is insufficient for achieving optimal performance in domains involving cooperation with humans. We address this shortcoming by first introducing a planning algorithm we call DiL-piKL that regularizes a reward-maximizing policy toward a human imitation-learned policy. We prove that this is a no-regret learning algorithm under a modified utility function...

• Building Billion-Scale Vector Search
  Did you know that algorithms and data structures for approximate vector search can search across billions of vectors in high dimensional space in a few milliseconds?...In this blog post, we look at how fast is fast enough in the context of vector search, and how the answer to this question impacts how we design and build a billion-scale vector search solution...

• gganimate
  gganimate extends the grammar of graphics as implemented by ggplot2 to include the description of animation. It does this by providing a range of new grammar classes that can be added to the plot object in order to customise how it should change with time...

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