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

Management

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

A product manager recently asked me if one can get better at knowing whether things are easy or hard, and how long they will take. The good news is that with practice, you can help people estimate much better than they can estimate on

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

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 - a podcast about making data accessible to everyone, hosted by the incredibly talented Nick Latocha and Andy Crossley. In the episode we discuss: the spectrum of what "data science" means and

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

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 startups using 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. If you have a friend who works for the company or knows someone senior, a

Management

How to make good, quick, collective decisions

This is a recipe for making good, quick, collective decisions: Write a proposal with: a clear statement of the goals you're trying to achieve (always start with the goals!), your suggested approach, and maybe an argument for why it's a good idea. (A Google

Software

Technical debt isn't like financial debt

I hope you weren’t hoping for a blanket answer to the question of exactly when to pay off technical debt. If you were, the answer is Tuesday. Almost all interesting decisions are about risks and trade-offs. This is a framework for making our intuitions about technical debt more rigorous.

Management

Running a Premortem

A Premortem refocused the hardest death-march I’ve been on, and another one years later was a key step in planning for a complicated (and ultimately successful) major fund-raise. If all goes well, it’ll help highlight your biggest fears, and proactively figure out steps for how to defuse them.

Data Science

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.