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Evil Works
Data Science Tooling and Teaching
Iterate Faster
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Skip the Struggle
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Tell your Story
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Iterate Faster 👹 Skip the Struggle 👹 Tell your Story 👹
The first rule of data science: Your code will take far longer to run than you expect it to.
The second rule of data science: While you’re faffing reading the news for 30 minutes, it actually failed 5 minutes in and you just wasted all that time.
So you fix that quick bug you made that was adding celsius to fahrenheit without converting and, guess it’s time to read the news for another 30 minutes. What… it failed again?
When I worked as a quant I thought I was the only one who had this problem but now I know it’s a universal pain felt by all data scientists. So why don’t we do anything about it? Well we have some ideas of what to do.
Try it now
Data Science is a hands on activity. It’s time to get your paws dirty:
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Evil Schemes
Our most cunning plans, documented for posterity, and for your benefit. From tutorials to case studies, here’s how data science can be both brilliant and diabolical.
Come Plot With Us
The world won’t take over itself.
Whether you're plotting your rise or perfecting your algorithms, in our community you'll find immoral support, sinister insights, and a front-row seat to the creation of our most ambitious experiment.
Enterprise Evil
If you’re a Cunning Corporation with Mischievous Machinations underway, let’s talk.
We work with companies to take data science to the next level. Through specialised tooling we reduce the burdens on data scientist time, enabling them to focus on what they’re actually good at: Doing Data Science.
All with enterprise-grade encryption and local execution. Your data stays in your lair, not ours.
New to Data Science?
Click below to find out what a data scientist does and how.
Surveillance Footage
Definitely not educational content... just covert operations caught on camera.
(Okay fine. Video tutorials, demos, and some... questionable experiments.)
We know data science is hard enough when working with metrics. Customer churn, ad clicks, cart abandons. Even the stuff collected internally is patchy with data quality leaving a lot to be desired. But they’re numbers. They behave. To a certain extent.
You know what doesn’t? People. Particularly when the pesky emotions of love are involved. You can’t turn ‘chemistry’ into a float value.
I tried to build a linear regression model to predict romance thinking it would be a fun Valentine’s experiment and an interesting feature engineering challenge. Instead I found inconsistencies, bias and impossible variables. Today we’re going to learn just how important domain knowledge is to data science. Welcome to my data science nightmare.