You're not the only one that finds data science interviews hard. That was me a few years ago until
i learned a few things to get through it. Today I'll tell you everything you need to know to crack
the data science interview. So you've already applied for a data science position and you've
been invited to interview. In summary, there are three stages to a data science interview process.
The first is the screening interview. The second is the technical interview and the third stage is
the on-site interview or in-person interviews. Let's talk about the screening interview. This
stage is the easiest part of the entire interview process. This is simply a phone chat with either
the recruiter or a data scientist from the company you're applying to. The goal of this screening is
to figure out whether you're the right person to be applying for this type of a job and
get a general sense as to your experience based on what you say in your resume. A basic check as
to whether or not you're the person you say you are on your resume. It's a shallow culture type
fit and don't be surprised if they're throwing some basic machine learning questions in there,
such as what is a validation set or what is regularisation. So be sure to brush up on your
basics before you do a screening interview. So once you get past the screening interview, you'll
most likely be invited to do a technical test or a technical interview. As being a data scientist
is a fairly technical role the companies want to know that you have a good foundation in coding and
data science this stage can vary significantly in difficulty based on whether you're applying
for a junior, mid-level or senior roles. And it's usually done in two ways. The first is an
online data science quiz and the second is a take home data science assignment. Let's talk about
the first one. The online data science quiz will usually be a timed multiple choice style questions
or it can be an online console test which requires you to write some code. Almost all of these tests
will require you to answer questions about your understanding of SQL, python, statistics and data
science and these tests will be administered in some sort of an online platform such as
HackerRank, Testgorilla, testDome or Alooba. It'll be a mix of theoretical and practical questions.
So let's go through some examples across all of those four categories I mentioned. So an example
of an SQL test could be the following. Given the following data definition, write an SQL query to
find all the students who had a test score greater than 80 and whose first name is not John. Feel
free to pause the video to take a stab at it. The solution is the following. Again the difficulty of
the SQL related question will vary based on the level you apply for. Examples of Python questions
could be what is a lambda in python or how do you filter based on a particular column value in a
data frame. As for questions related to statistics an example could be give me examples of data that
are not normally distributed. And in this case the answer would be categorical data like the
education level of a person or data that adheres to exponential distributions like the amount of
time in months that a car battery lasts. Finally examples of data science questions could include
what is a random forest or how does it defer to logistic regression. And there's a whole
variety of questions like that that I found online that helped me greatly to prepare for
data science interviews and I'll be sure to leave them in the description below.
Alternatively instead of an online data quiz you might actually have this technical interview with
a data scientist at the company. But the types of questions they ask will be very similar. Let's
move on to the second type of technical test they might give you which is the take home data science
assignment. From my experience this is usually a Kaggle style questions that requires you to
do the working and present it in a Jupyter style notebook and you usually have around two to three
days to complete something like this or you might actually get this assignment online and you might
have a couple of hours to do it. An example of this could be a Kaggle challenge which requires
you to predict sale prices of houses and they might give you a data set of 100 000 houses with
80 of 90 features and the task will be to create a model that can best predict the sale price of
a house. this test is really to show off your technical skills as well as your communication
skills. So really the way you approach this type of problem is firstly you would engage in a
exploratory data analysis and this is really when you interrogate the data set. Some of the analysis
that you might do in a step like this is to figure out the correlations between the features
in the data set and visualising any trends that you might find for example in the case of our
house price challenge you might want to take a look at whether the average number of bedrooms
varies based on the neighbourhood the house is in or try to figure out how the sale price varies
based on the area of the garage. After your analysis you will conduct a data cleaning exercise
and doing any necessary feature engineering that you might need to do after which you'll try out
a couple of algorithms that get the job done and this process will be something that's iterative
and always make sure to explain your reasoning behind a choice of model and your method of
evaluating it or moving on from it to another model. The whole point of this task is to take
your interviewer through a data science journey because really you're building a story around
solving this problem so keep that in mind when you go through this stage. And something useful that I
found is to really hunt down some of these Kaggle challenges and look at the solutions that got the
most number of votes because they usually tend to be the ones that have been great at communicating
the steps that they've taken and they've also been able to present a fairly reasonable approach
to solving the problem and I've really found it very interesting to learn from the best on those
websites. So I couldn't recommend it highly enough and in some cases you might actually get a mix of
the online data quiz and also take home exam I would really encourage you to prepare for
all possibilities as you really don't know which company might use what method. If you're enjoying
the video so far consider subscribing. I make weekly videos about demystifying the land of
data science. Now let's talk about on-site interviews. So you've passed your technical
interviews and you'll be invited to interview with a data scientist or lead data scientist at
the company. Before COVID, this would have been in person but these days it's quite normal to expect
zoom interviews. In these interviews there'll be a mix of experience questions scenario-based
questions and culture fit behavioural style questions. By this stage they've generally
got a fairly good sense as to your technical abilities. The first thing they'll want to know is
about your work experience. So this is where they might ask you to describe a particular data
science problem that you have solved at a previous company or some personal data science project that
you have undertaken. In this step they're really looking to see that you can describe things in a
simple and understandable manner without getting into the weeds because a large part of the role of
a data scientist is liaising with different stakeholders. So the next type of questions
they'll ask is scenario-based question they'll want to assess how your problem-solving abilities
are regarding specific data science problems in the company that you're interviewing for.
Suppose you're interviewing for a social media company with an online platform and they've
recently released a new feature which allows users to upload a 10 second gif and they want you to
figure out whether this feature has increased user engagement on the platform. Questions like this
really require you to fully immerse yourself into the problem space and think practically about an
AB style of experiment. Your answer should be more of a discussion with your interviewer rather than
a rigid answer. Again they want to evaluate your thought process and don't be afraid to ask
clarifying questions. The interviewers want you to succeed. Some of these scenario-based questions
might also involve you picking a specific type of model for a type of problem they're
looking to learn whether or not you can conduct a successful trade-off between different options
while displaying a broad understanding of the various options. In some of these questions
you might feel as though they're poking a hole in your understanding but always be confident.
Answer what you can and don't be afraid to admit if you don't know something. This shows them your
willingness to learn. The last thing they want is someone fabricating their understanding of
a particular topic because there's really no such thing as fake it till you make it in data science.
You'll be working with extremely smart people they'll see straight through you.
Finally after the scenario based questions you'll be asked a couple of culture fit questions
so this will be things like why you want to work for this particular data science team or at this
particular company. Your answers will be defined by how well you've done your research about the
company and the people that work there. So for this step I always find it useful to go through
the company website, look through a couple of the Linkedin profiles of the people that work there
and go through any Glassdoor reviews. All of these will give you a good sense as to
the culture of the company and also give you a couple of things to talk about and additionally be
genuinely curious about your interviews. So have a look at their experience and
prepare a couple of questions to ask them and ideally this should be questions that are specific
about their experience. For example, it could be it's the most rewarding project you've worked on
so far at this company or what's your day-to-day look like and also ask specific questions to
your future role so what are your first three months look like if you get hired or what type
of projects you'll be working on and this will give you a rough understanding as to what you're
getting into. Again I'll leave a link below to all of the interesting resources that have helped me
in my data science interview preparation. Thank you for watching and I'll see you next week.