The Non-Obvious Skills You Need as a Data Scientist | Keith McCormick

About this Episode

Keith is a sought after speaker, who routinely leads workshops at conferences. He’s given keynotes presentations at many international events and is an award winning instructor for UC Irvine's Predictive Analytics certificate program.

You may recognize him as the instructor of thirteen courses on LinkedIn Learning - where’s taught over 250,000 learners through his courses.

FIND KEITH ONLINE

Website: http://www.keithmccormick.com/

Twitter: https://twitter.com/KMcCormickBlog

AboutMe: https://about.me/keithmccormick

Courses on LinkedIn Learning: https://www.linkedin.com/learning/instructors/keith-mccormick

QUOTES

[00:05:32] "What I concluded was that 2012 was really the big year where the dominos started to fall, that we're calling everything A.I. now."

[00:08:28] "I think that most client organizations that I encounter, what they're missing, what prevents them from being effective is effective analytics middle management."

[00:12:38] "Because you always have to rethink things between a prototype and putting it into production. So the notion that you're going to dismiss anyone that's using any tool other than coding in the same coding language that the team has adopted, you're working off a false premise"

[00:14:10] "It seems to me there's an inherent flaw there, if the entire Data science community recognizes that the job descriptions are nuts and therefore everybody should ignore them."

[00:15:39] "We put so much emphasis on the coding that there's this huge gap between running the code that creates something simple like a decision tree, and knowing the basic foundation and concepts of how the tree is growing and how to interpret it."

[00:18:00] "I would say from the statistics side of the house, I want to know if they know when to trust and when not to trust the data. I'm really big on that."

[00:20:35] "One thing I really don't like at all is when organizations use an external resource and they throw the data over the fence and then they just get the solution delivered on their desk. That's a nightmare."

[00:22:39] "I think that before someone contemplates leaving their current department and joining the Data science team, they should never do that until they've done a project from start to finish as a borrowed resource."

[00:31:53] "Your model is still degrading even if you're automatically rebuilding it. And the reason is that the model is only one step in a long process. You're also making assumptions about what data is relevant, what variables are used in the model, and all that."

[00:48:03] "Part of the problem is none of us know what data science is, right. And by that I mean that it's a puzzle that we haven't figured out yet. It's the term is being used in so many different ways."

SHOW NOTES

[00:01:31] Guest introduction

[00:02:45] Keith’s journey into data science

[00:04:42] How much more hyped data science has become since the 90s

[00:05:32] What the year 2012 did for data science

[00:06:29] How tools of the trade have impacted data science adoption in recent years

[00:07:45] Excellent project idea for anybody that's listening

[00:08:15] In order to make machine learning work, we need to have effective teams

[00:10:11] Diversity, recruiting, and data science teams

[00:12:09] How does groupthink inhibit or limit team effectiveness?

[00:13:27] Why hiring and retention in analytics is broken, and how to fix it

[00:15:23] The essential checkboxes for a data science candidate

[00:18:50] The challenges of being the first data scientist in an organization

[00:21:52] Remixing talent inside your organization

[00:23:57] What is the goal of analytics?

[00:25:55] What are insights and how do we use them?

[00:28:06] The goal of achieving a deployable model

[00:30:53] What to do once the model is deployed

[00:34:57] How to measure ROI on your data science projects

[00:36:18] What are some steps that we could take to turn a business problem into a data science research question?

[00:38:06] The difference between a consulting data scientist and an internal data scientist

[00:41:07] Freelancing as a data scientist

[00:45:32] How to be a good leader in data science

[00:47:52] Is statistics the same as data science?

[00:50:04] Embrace ambiguity

[00:52:35] The skill of cognitive empathy

[00:55:35] Why it's important that we have a commitment to our craft

[00:57:28] How can we be more effective with our persuasion skills in Data science?

[01:00:32] The most important part of the data science lifecycle

[01:02:47] Why you need a little skepticism

[01:05:26] It’s 100 years in the future, what do you want to be remembered for?

[01:06:39] The random round

Episode Comments