
Data Science Interview Study Guide - dataguy12
https://www.coriers.com/the-data-science-interview-study-guide/
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UweSchmidt
It bothers me that so many of these guides are aimed at passing the _job
interview_ instead of teaching the topic at hand.

\- I want to be excited about the possibility of becoming better at the topic,
not fantasize about crushing an interview

\- If there is a major difference between the skills needed for an interview
and the actual job (as the concept of interview perparation suggests), that's
bad in all kinds of ways

\- The interview prep articles often present the material in an unattractive
way, as a list of requirements that need to be fulfilled, without an
explanation of how it all fits together.

How about leading the student along, from a small sample project, showing the
pitfalls of not understanding statistics properly, to a larger project where
the data is put into a database, motivating the study of SQL, to doing some
programming related to the toy problem at hand, motivating a deeper dive into
programming fundamentals? How about inspiring the student about the beauty of
the topic, about the possibilities and powers they get with each new little
skill that they acquire?

~~~
0-_-0
> If there is a major difference between the skills needed for an interview
> and the actual job...

The problem is that the exact skills required for a job cannot be measured
within the timespan of an interview, so instead closely related skills are
measured. Until somebody solves this problem interview preparation makes a lot
of sense.

~~~
UweSchmidt
I think evaluating the skills is not difficult within the timespan of an
interview, if done right.

Let's sit down at a specific problem I am working on. Let me tell you what I'm
doing with the tools that I have. Your past work was in some way similar, and
you can relate to what I'm doing in some way. Either you can jump right in and
take over the keyboard, or you can tell me how you solved a similar problem
with your tools and frameworks. I can show you how it's done with my setup,
and teach you a few things in the process - which you will easily pick up as
you have related experience. You got either questions that are exciting, maybe
challenging me how I am doing it, or maybe teach me just a keyboard shortcut I
didn't know, proving the time and effort you spent on this. We are both
excited to learn a lot in a short amount of time and I know I want you to work
with us...

~~~
0-_-0
Wouldn't your approach be biased against people who need more time to come up
with a solution, and against people who are not used to discussing their
solutions with others in real time? You approach also doesn't include an
objective measure of performance: Your judgement of the candidate could be
highly dependent on their personality, instead of their actual skills. If I
recall correctly the book "Thinking fast and slow" shows how easily
interviewers are influenced by personality traits. Technical questions like
"how do you invert a binary tree" or "what is KL-divergence" are much less
personality dependent, and the quality of the answer is measurable.

Here is what I think are some important skills in data science:

Can you understand the principles behind existing solutions and build a new
solution from those principles?

(How fast) can you understand a new approach and apply it to a problem?

How long does it take you to translate a solution to working code? How
optimal, readable and reusable is it?

~~~
UweSchmidt
My proposal hopefully doesn't put the candidate on the spot, but allows for
any and all useful contributions throughout the process, be it fast or slow
thinking or anything in between. Hopefully in that hour or so where we sit
down, there is enough time to overcome most of the bias that we had when the
candidate walked in.

Looking at your list of important skills in data science it seems we mostly
agree on what is important...

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eoinmurray92
At Kyso ([https://kyso.io](https://kyso.io)) we see a lot of people get hired
into data-science jobs and the biggest success factor that I've personally
seen is having some example's of projects that the candidate has worked on.
This can be either public projects online (thats what we started Kyso to help
with!) or a description of a project they worked on while studying/working.

Something I've noticed about data-science candidates is that they are very
happy to jump into the technical details of an implemented model - but
sometimes struggle on is communicating the reasons for the model in the first
place and how it can help the company/research project. A lot of data-science
projects are smaller ad-hoc jobs where the data scientist is trying to answer
some business question and here communication is a vital skill.

------
king_magic
“Statistics is a broad concept so don’t get too bogged down in the details of
each of these videos. Instead, just make sure you can explain each of these
concepts at the surface level.”

Lol, well now, there’s a recipe for success right there.

~~~
Frost1x
That's essentially how modern society functions and the type of behavior it
rewards, so it really is a recipe for an economic measure of success. Most
humans these days tend to equate economic success with success in life as
well.

I don't agree with the notion but that's where we are. It's one extreme or the
other: extreme generalization with no depth or extreme depth with no
generalization. Thats what pays and there's seemingly no reward for the in-
between.

------
redstone08
I think people are realizing that data scientist without domain knowledge
cannot create valuable insights. Enterprises seems to hire less data
scientists actually, but they are trying to raise their employees' data
skills. I think that's the cause of the growth of self-analytics tools. Below
are examples of them.

1\. Metatron Discovery : [https://metatron.app](https://metatron.app) 2\.
Metabase : [https://metabase.com/](https://metabase.com/)

