Data Science is Doomed, But WE Can Save It | Vin Vashishta

One of LinkedIn's 2019 Top Voice's for Data Science shares why he thinks we're all doomed.

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[02:29] The introduction for our guest today

[03:58] How Vin first heard of data science and what drew him into the field

[05:22] Why data science is doomed

[07:44] What separates the great data scientists from the merely good ones

[10:22] What role does being creative and curious play in being successful as a data scientist and how can someone who doesn't see themselves as creative be creative?

[13:04] What are some soft skills that candidates are missing that are really going to separate them from their competition?

[15:23] Why women are excelling in data science

[18:18] Vin talks to us about the growth mindset, gives us his definition of it, and how it's important that data scientists embrace this type of mindset.

[19:58] It's not a zero sum game: If you are of a growth mindset, you're not only will want to teach, you want to learn and those two pieces of communication are essential

[20:38] Vin reflects back on his career and recounts the importance of diversity

[22:23] How a up and coming data scientists can tie a particular ability or a particular requirements with a business need specifically in in cases where one doesn't have any work experience to speak of?

[23:47] How up-and-coming data scientists are actually in a better place then those who have been working on the same team for a long time

[24:58] Could you share some tips or words of encouragement for our listeners who've got a couple of decades, let's say 10 to 20 years of a traditional IT experience under the belt who are now trying to break into data science. What challenges do you foresee them facing and how can they overcome some of those challenges they built?

[27:04] I ask Vin what advice or insight he could share with people breaking into the field who are looking at these job postings? Some that seemingly want the abilities of an entire team wrapped up in one person and they end up feeling dejected or even discouraged from applying.

[30:48] What are some challenges that a notebook data scientists face when it comes time to productionalize a model. And do you have any tips for them to overcome those hurdles?

[33:11] If you've already mastered Python, Vin tells you what programming languages you should learn next

[34:31] We touch on the importance of writing good comments in your code

[35:04] What cloud technology should people pick up prior to breaking into the field? Or is this something they should even focus on if they're just looking to land their first role?

[36:04] The one thing Vin wants us to learn from his story

[36:37] We jump into our lightning round: Python or R?

[36:47] What's your data science super power?

[37:29] What's your favorite algorithm for regression and your favorite algorithm for classification?

[37:51] . So what's the number one book you would recommend our audience read and your most impactful takeaway from it?

[38:13] I go off into a tirade about how much that book has changed my life.

[39:05] Certifications vs self-study

[40:00] What motivates you?

[41:44] The societial impact that COVID-19 is going to have

[43:45] Vin let's us know how we can connect with him and shares a message for smaller businesses going through rough times due to our current global pandemic situation