Data Science Interviews with Nick Singh

For an episode all about tips for crushing interviews for Data Scientist roles, our guest is Nick Singh — author of the bestselling "Ace the Data Science Interview" book and creator of the DataLemur SQL interview platform.

Nick:
• Co-authored “Ace the Data Science Interview”, an interview-question guide that has sold over 16,000 copies since it was released last year.
• Created the DataLemur platform for interactively practicing interview questions involving SQL queries.
• Worked as a software engineer at Facebook, Google, and Microsoft.
• Holds a BS in engineering from the University of Virginia.

Today's episode is ideal for folks who are looking to land a data science job for the first time, level-up into a more senior data science role, or perhaps land a data science gig at a new firm.

In this episode, Nick details:
• His top tips for success in data science interviews.
• Common misconceptions about data science interviews.
• How to become comfortable with self-promotion and increase your chances of landing your dream job.
• Strategies for when interviewers ask if you have any questions for them.
• The subject areas and skills you should master before heading into a data science interview.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

How to Rock at Data Science — with Tina Huang

Can you tell I had fun filming this episode with Tina Huang, YouTube data science superstar (293k subscribers)? In it, we laugh while discussing how to get started in data science and her learning/productivity tricks.

Tina:
• Creates YouTube videos with millions of views on data science careers, learning to code, SQL, productivity, and study techniques.
• Is a data scientist at one of the world's largest tech companies (she keeps the firm anonymous so she can publish more freely).
• Previously worked at Goldman Sachs and the Ontario Institute for Cancer Research.
• Holds a Masters in Computer and Information Technology from the University of Pennsylvania and a bachelors in Pharmacology from the University of Toronto

In this episode, Tina details:
• Her guidance for preparing for a career in data science from scratch.
• Her five steps for consistently doing anything.
• Her strategies for learning effectively and efficiently.
• What the day-to-day is like for a data scientist at one of the world’s largest tech companies.
• The software languages she uses regularly.
• Her SQL course.
• How her science and computer science backgrounds help her as a data scientist today.

Today’s episode should be appealing to a broad audience, whether you’re thinking of getting started in data science, are already an experienced data scientist, or you’re more generally keen to pick up career and productivity tips from a light-hearted conversation.

Thanks to Serg Masís, Brindha Ganesan and Ken Jee for providing questions for Tina... in Ken's case, a very silly question indeed.

The SuperDataScience show's available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

Courses in Data Science and Machine Learning

This week's guest is super fun Sadie St. Lawrence, an exceptionally popular data science instructor with over 300k students all-time. She fills us in on her exciting new ML Certificate and the global impact of her Women In Data org.

Sadie:
• Teaches data science at the University of California, Davis
• Her Coursera course is one of the all-time most popular
• Is Founder and CEO of Women in Data, a community of 20k across 15 countries
• Holds a Master's in Analytics from Villanova University

In this episode, she digs into:
• The content of her existing iconic data science course
• The curriculum of her epic forthcoming Machine Learning Certificate
• The mission, impact, and vision of the Women in Data organization
• Her path into data science from music performance
• Non-fungible tokens (NFTs) and the future of technology

Thanks to Harpreet Sahota for introducing me to Sadie! I absolutely loved filming this episode.

Listen or watch here.