Mastering the Data Science Interview

I just applied for a Data science internship at Asana. I would love to know what the hiring process is like , what is expected of a candidate and what do you look for in a candidate?

Just totally flubbed a data interview; I've been focusing on Python for months, and they asked a really easy Python question and a hard SQL query (with subtables etc.) (which I had set aside to...

What answers do interviewers expect to hear from experiment design , how do we take A/B testing to next level of hypothesis testing.

What differentiates candidates in data science interviews?

How does asana use data science ?

How would you prepare for machine learning questions?

What are the technologies you would need to be proficient in to get a data science job?

I'm an experienced functional analyst and I want to shift to data science, I have specific knowledge regarding the subject like algorithms, platforms and data, but I have no experience applying...

How do I best highlight my transferable skills so I can get the interview and rock it out?

What kind of projects should a beginner work on?

What is the best way to supplement my math background?

How can a beginner showcase work?

Applying for jobs as a data scientist for the first time, not sure what to expect and am hoping to learn best ways to prepare, what would help me stand out as an applicant and overall feel more...

I am attending GHC this year and will be focusing on data science and analytics positions. Can you give some tips on how to prepare for the interviews?

How would you prepare for product related case questions? How would you prepare for stats questions?

What are the use cases for data at your company? Do you have an environment that is open to a test-and-learn for new applications for data science?

What are the skills that should be highlighted in a resume when applying to a data science job?