Comet ML Office Hours 4 - 28FEB2021

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March 4th, 2021

1 hr 5 mins 14 secs

Season 10

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About this Episode

Comet provides a self-hosted and cloud-based meta machine learning platform allowing data scientists and teams to track, compare, explain and optimize experiments and models. Backed by thousands of users and multiple Fortune 100 companies, Comet provides insights and data to build better, more accurate AI models while improving productivity, collaboration and visibility across teams.

Register for future sessions here: http://bit.ly/comet-ml-oh

Checkout Comet ML by visiting: https://www.comet.ml/

Or on Twitter: https://twitter.com/CometML

On YouTube: https://www.youtube.com/channel/UCmN63HKvfXSCS-UwVwmK8Hw

Connect with Ayodele

LinkedIn: https://www.linkedin.com/in/ayodeleodubela/

Twitter: https://twitter.com/DataSciBae

Check out her course on LinkedIn Learning: https://www.linkedin.com/learning/supervised-learning-essential-training/supervised-machine-learning-and-the-technology-boom

[00:01:17] We talk about the madness of the data science interview process

[00:02:29] Why are why are we making it so difficult for people to even get considered for an interview?

[00:05:41] Could data science benefit from a series of exams and accreditations, much like actuaries have to do?

[00:07:53] How does one prepare for these technical questions?

[00:11:35] How long should you spend preparing for an interview?

[00:12:58] What are considered medium/hard type of questions?

[00:16:02] Am I expected to have an answer for everything?

[00:20:46] How many projects should I have, and what should they be like?

[00:24:25] How should I be allocating my time in the job search process?

[00:25:17] Book recommendations

[00:28:30] Notebooks or scripts?

[00:30:53] How to explain your projects?

[00:35:07] Mark talks about some stuff he’s doing at work around creating and defining metrics and KPIs

[00:41:16] Tor jumps in with some sage advice

[00:44:46] Measuring the monetary value of your efforts

[00:50:10] How do you get managers buying into creating a data project?

[00:54:21] Explaining the value of your passion projects in an interview

[00:57:03] NLP question time

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