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Cassie Kozyrkov Profile picture
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Apr 27 • 12 tweets • 3 min read
In a #DataScience project, all paths lead back to #Analytics, often with messy inherited data - here are some helpful skills and traits for it, a thread 🧵
🔥domain knowledge🔥helps data scientists and analysts make sense of the chaos and guide their judgment about how to spend their time and effort. 🧵
🔥data design skills🔥help data scientists and analysts inform data collection efforts based on what they’ve discovered. 🧵
🔥collaboration skills🔥help data scientists and analysts know which experts to lean on when they’re out of their depth, for example statisticians when an experimental design problem gets thorny or scientists when the domain is very technical. 🧵
🔥pragmatism🔥helps data scientists and analysts silence their inner perfectionist and do their best to extract value from imperfect data. 🧵
🔥communication skills🔥help data scientists and analysts silence their stakeholders’ inner perfectionists and set reasonable expectations. 🧵
🔥proactive curiosity🔥helps data scientists and analysts ask the hard questions and find additional data sources to interrogate. 🧵
🔥resilience🔥helps data scientists and analysts survive the frustration of dealing with other people’s bad data collection choices. 🧵
🔥restraint🔥helps data scientists and analysts avoid jumping to conclusions from untrustworthy data. Holding your opinions loosely and being carefully about your assumptions is how you avoid many of the traps of working with data you whose origin story is opaque to you. 🧵
🔥humility🔥helps data scientists and analysts avoid taking themselves and their analyses too seriously. 🧵
🔥a sense of humor🔥helps data scientists and analysts... because that shit is often funny. 🧵
Learn more in the article ⬇️
bit.ly/quaesita_reali…
which continues our series on the differences between amateur and professional analysts.

It'll help you understand why analysts spend most of their time in horribly messy and chaotic data... and why that's a good thing.

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More from @quaesita

Cassie Kozyrkov Profile picture
Jan 4
A decision scientist's 2 tips for new year's resolutions involving diet and exercise, a thread. 1/🧵
Diet tip: Calibrate your evaluation window. If you have a resolution to reduce intake of something, don't evaluate your success by comparing today with yesterday. 2/🧵
A day is an arbitrary unit, so why not optimize your evaluation window? A day seems natural, so many people don't ask themselves if it's the best unit. But if you use a shorter window, your chance at a "fresh start" comes sooner. 3/🧵
Read 22 tweets
Cassie Kozyrkov Profile picture
Oct 19, 2021
Leaders! Stop dropping the ball on #machinelearning projects!

This thread is a manual for the leader's role in ML/AI projects, with short guides to each bit. 1/🧵
If you think it's okay for the leader to skip these tasks or punt them to the #AI project's chief nerd, read this: lnkd.in/eziSYhX
2/🧵
lnkd.in/eziSYhX
First, check that ML/AI is right for you. Don't just chase the buzzword. Use it to solve a real problem! Here's how:
3/🧵
Read 15 tweets
Cassie Kozyrkov Profile picture
Jan 15, 2021
1/🧵 Good #DataScience advice that breaks pretty much every rule you learned in class... a thread. (+full blog post linked)

English version: bit.ly/quaesita_rethi…
Spanish version: bit.ly/spanish_rethin…

#AI #MachineLearning #Statistics #RStats #agile

bit.ly/quaesita_rethi…
2/🧵 Allow your approach to be sloppy at first and burn some of your initial time, energy, and data on informing a good direction later. That's right, you're supposed to start sloppily ON PURPOSE.
3/🧵 Have a phase where the only result you’re after is *an idea of how to design your ultimate approach better.*
Read 16 tweets
Cassie Kozyrkov Profile picture
Jan 13, 2021
What are the 5 most important questions to ask when starting with a new dataset? A video ( + a thread 🧵)

bit.ly/quaesita_yt5da…
#1 - Purpose

What was the purpose for which this dataset was collected?
#2 - Competence

How competent were the people who collected this data?
Read 7 tweets
Cassie Kozyrkov Profile picture
Aug 14, 2020
What makes an analyst excellent? Speed! But that's not as simple as it sounds. They need 6 kinds of speed... 1/🧵

#DataScience #Analytics #rstats #DataAnalytics

bit.ly/quaesita_speed
1) Speed of getting data that’s relevant. (Domain knowledge.)
2) Speed of getting data ready for manipulation. (Software skills.)
Read 15 tweets
Cassie Kozyrkov Profile picture
Apr 22, 2020
0/ Essential philosophy for #DataScience, a thread of 32 questions.

Grab a friend (virtually) and tackle these 32 essential questions (all with more than one reasonable answer) that every serious #data professional should answer for themselves.

#philosophy #rstats #statistics
1/ Is it possible to know anything at all?
2/ What does it mean to "know" something?
Read 34 tweets

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