Data Science for Managers - Making Data Mistakes

Making Data Mistakes

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Data Science for Managers

A collection of 7 posts

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

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%

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)

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

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 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.

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?