Making Data Mistakes

Ease/value ranking

The ease/value ranking is a great way to whittle a big list of product or strategy ideas down to a prioritised backlog, or as a starting point for discussion.

Tensions between Engineering, Commercial, and Product?

How do you feel about the relationships between your Engineering, Commercial, and Product teams? Even in a healthy company, there will always be structural tensions between these teams, because their goals and needs are in tension. But just as in a suspension bridge, that tension is the key to holding

How to kick off a successful project

Project Kickoff meetings help you see potential problems with new projects, and get stakeholder buy-in

Collaboration

How to make your software team much more effective

Programming is the closest we have to magic in the real world. Just like magic, you can conjure rich and powerful creations with the power of your own mind, given the right tools and training. Just as powerful magic could lay waste to an entire army, powerful programming can do

Collaboration

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

Every time something goes wrong, or could have gone better, run a blameless, five-whys-inspired Postmortem to figure out how you could improve going forwards.

Data Science

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

Data Science

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.

Collaboration

How to reproduce your analysis

What do you do when things don’t seem to be behaving the same way now that they did before? Here are a few techniques to avoid that dread scenario.

Collaboration

How to run an Idea Stampede to make a group gallop

aka How to have a conversation productively with 10 people at once (aka How to run brainstorms like a pro)

Classification

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

Wrong answers matter in different ways for a retina scanner outside a bank vault vs a cancer detector. This is why you need to understand precision vs recall.

Data Science

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]

Management

Why we're bad at estimating (and what to do about it)

With practice people can learn to estimate much better.

Software Engineering

A little functional programming goes a long way

Object-oriented and functional programming styles have both been around for a while, and have their own committed adherents. It’s almost certainly possible to be effective with either. But on balance, you should prefer functional programming.

Data Science

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

Data Science

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:

Data Science

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.

Data Science

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?

Programming

How to write good software faster (we spend 90% of our time debugging)

If we spend the majority of our programming time and effort on debugging, we should focus our efforts on speeding up our debugging (rather than trying to write code faster).

Data Science

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.

Philosophy

Necessary vs Sufficient

Philosophers and mathematicians make a distinction between ‘necessary’ and ‘sufficient’. Air is necessary for human life, but it’s not sufficient. In other words, if you don’t have air then it’s game over. But if you do have air, you still need some other stuff to survive. So

Management

How to make your data science team faster (and speed up progress)

What do you do if your CEO complains that progress is too slow? While it’s tempting to think you just need to buckle down and “get more done”, more often than not, simply trying to "increase throughput" won’t work, especially if you’ve misdiagnosed the root cause of the problem.

Software

Why are my emails being sent to spam - a guide for GSuite

“This email to an investor was marked as spam! They only noticed it by accident. Why is this happening?”

Software

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?

Data Science

Will data scientists become obsolete in the next 10 years?

Will tech companies still need data scientists, or are they destined to become just another blip in the history books – disappearing from the world like film projectionists and bowling alley pinsetters before them?

Job-hunting

How to find a job through a recruiter

‌‌Every one of the dozen or so job offers I’ve received in London in the last 10 years has been through a friend’s recommendation, or through recruiters.