Data Science - Making Data Mistakes

Making Data Mistakes

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Data Science

A collection of 14 posts

Postmortems: The single most important keystone habit for a team (step-by-step guide)

I’m an ill-disciplined person. My habit of reading books about habit-formation is stronger than any of the habits they’ve help me form. Perhaps because of this, I’m interested in the idea of keystone habits [https://callmebetty.com/17-keystone-habits-what-they-are-and-how-you-can-use-them-to-skyrocket-your-success/#:~:text=Charles%20Duhigg%20defines%20a%20Keystone,of%20hacking%20your%

Make it work, make it right, make it fast

I first heard this phrase from Gael Varoquax [http://gael-varoquaux.info/] (one of the core scikit-learn committers) in a talk[1], and I loved the super-concentrated wisdom of it: * Make it work. First of all, get it to compile, get it to run, make sure it spits out roughly the

How Humans and Machine Learning Deliver the Best Result (Podcast)

I was recently honoured to be a guest on The GrayMeta Podcast - Metadata Matters. In this podcast, I have the opportunity to discuss my perspective on Metadata and how to structure human teams to effectively work with data generated by machine learning. Hosted by the incredibly talented Matt Eaton.

How to reproduce your analysis

But first, an exciting announcement! I'm currently hiring for two Machine Learning Engineering roles, both doing interesting work with high-energy startups led by great people on good missions. Scroll to the bottom of this article for more details! -------------------------------------------------------------------------------- How to reproduce your analysis > “But didn’t you say you

What is precision vs recall, and why should I care?

What do these tasks all have in common? * Looking at an X-ray and deciding whether someone’s leg is broken * Screening a CV for suitability for a job post * Identifying faces of terrorists in a crowd They are all examples of classification tasks, where we need to make a binary

How to Cheat at Data Science (with help from Centaurs and the Wizard of Oz)

What does it take to run a high-performing data science team? In this 15-minute video, I talk about two tools that I’ve found invaluable for doing just that: * Wizard-of-ozzing is a technique for product discovery that can be very helpful for evaluating potential problems to work on [6:00]

What does it take to transition from Senior to Chief Data Scientist?

I’ve been asked this question by a few highly competent Senior Data Scientists over the years. Even just asking the question is usually a good sign that they have a sense of the answer - a sense that the skills that make you a good Senior Data Scientist may

What makes a great data scientist and data science team? (Podcast)

I was recently honoured to be a guest on The Data Pubcast [https://anchor.fm/data-pubcast/episodes/The-Data-Pubcast--Pint-3-ek3l21/a-a3b42vj] - a podcast about making data accessible to everyone, hosted by the incredibly talented Nick Latocha and Andy Crossley. In the episode [https://anchor.fm/data-pubcast/episodes/The-Data-Pubcast--Pint-3-ek3l21/a-a3b42vj] we discuss:

What is Machine Learning?

Machine learning algorithms are faced with the same challenge you had as a pupil at school. There’s going to be an exam. The higher you score the better, and you'll be given some exams from the past with answer sheets to study from.

What is Overfitting?

Overfitting is a common problem in Machine Learning. But what does it actually mean? And why is it an important problem for Data Scientists to overcome?

How to know if your recommendations algorithm is actually doing a good job

I led the team that built Channel 4’s recommender system for All 4 in 2016. It started out as a straightforward project. But after getting lost in a rabbit hole trying to devise a score for ‘provocativeness’ and ‘serendipity’, I learned the single most important lesson about data science.

The Difference: Throughput vs Latency

Latency and Throughput are important concepts for data scientists. How are they distinct? And why is this distinction important, not just for technical systems, but also for team performance?

How to build a model that performs at chance

I’m going to tell you about a horrible screwup. Not my first and not my last, but a happy one, because although it was excruciating it was also instructive.