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How To Prepare For A Data Science Interview

7/21/2017

11 Comments

 
Recently, our team of data science consultants had an awesome opportunity to present to a class of future data scientist at Galvanize Seattle. It was a lot of fun and we met a lot of ex-software developers and IT specialists. One student who had come to hear our talk was named Rebecca Njeri. She did not have a background in software engineering. However, she was clearly well adapted to the new world. In fact, for one of her projects she used company data to create a recidivism prediction model among former inmates using supervised learning models.

We love the fact that that her project was not just technically challenging, but that it was geared towards a bigger purpose than selling toasters or keeping customers from quitting your telecommunication plan! She also brought up her experience interviewing for data science roles at Microsoft and other large corporations and how it taught her so much. We wanted to share what she learned so we asked if she would write us a guest post! And she said yes! So without further ado, here is ​

How to Prepare for a Data Science Interview:

are data science interviews difficult

​If you are here, you probably already have a Data Science interview scheduled and are looking for tips on how to prepare so you can crush it. If that’s the case, congratulations on getting past the first two stages of the recruitment pipeline. You have submitted an application and your resume, and perhaps done a take home test. You’ve been offered an interview and you want to make sure you go in ready to blow the minds of your interviewers and walk away with a job offer. Below are tips to help you prepare for your technical phone screens and on-site interviews.

Read the Job Description for the Particular Position You are Interviewing for

Data Scientist roles are still pretty new and the responsibilities vary wildly across industries and across companies. Look at the skills required and the responsibilities for the particular position you are applying for. Make sure that the majority of these are skills that you have, or are willing to learn. For example, if you know Python, you could easily learn R if that’s the language Data Scientists at Company X use. Do you care for web-scraping and inspecting web pages to write web-crawlers? Does analyzing text using different nlp modules excite you? Do you mostly want to write queries to pull dataca from SQL and NoSQL databases and analyse/build models based on this data? Set yourself up for success by leveraging your strengths and interests.

​
Bring a copy of your resume
Review your Resume before each Stage of the Interviewing Process

Most interviews will start with questions about your background and how that qualifies you for the position. Having these things at the tip of your fingers will allow you  allow you to ease into the interview calmly as you won't be fumbling for answers. Use this time to calm your nerves before the technical questions begin.

Additionally, review your projects and be prepared to talk about the Data Science process you used to design your project. Think about why you chose the tools that you used, the challenges that you encountered along the way, and the things that you learned along the way.

Look at GlassDoor for Past Interview Questions

If you are interviewing for a Data Scientist role at one of the bigger companies, chances are they’ve already interviewed other people before you, who may have shared these questions on GlassDoor. Read them, solve them, get a feel of the questions you will be asked. If you cannot find previous questions for a particular company, solve the data science questions from other companies. They are similar, or at the very least, correlated.

Moreover, even if there are no data science questions for that particular company, see what kind of behavioral questions are asked.

Ask the Recruiter about the Structure of the Interview

Recruiters are often your point of contact with the company you are interviewing at. Ask the recruiter questions about how your interview will be structured, what resources you should use when preparing for your interview, what you should wear to the interview, and even the names of your interviewers so you can stalk look them up on LinkedIn and see their areas of specialization.

Do Mock Interviews

Interviewing can be nerve-racking, more so when you have to whiteboard technical questions. If possible, ask for mock interviews from people who have been through the process before so you know what to expect. If you cannot find someone to do this for you, solve questions on a white board or notebook so you get the feel of writing algorithms some place other than your code editor.

Practice asking questions to understand the scope and constraints of the problem you are solving. Once you are hired, you will not be a siloed data scientist. It is reasonable to bounce around ideas and see if you are on the right track. It is not always about getting the correct answer, which often does not exist, but about how you think through problems, and how you work with other people as well.

​
How to data science interview
Is your data science interview ready for you!
Practice the Skills that you Will be Tested On

Your preparation should be informed by the job description and the conversation with recruiters. Study the topics that you know will be on the interview. Look up questions for each area in books and online. Review your statistics, machine learning algorithms, and programming skills.
  • Some positions will want you to have really good programming skills and will not shy away from asking questions usually asked to software engineers. If applying for a programing heavy position, Cracking the Coding Interview is a good resource to practice these skills.
  • Other positions will need you to have really good statistical skills, an understanding of different population distributions, experimental design, A/B testing and hypotheses testing.
  • Most positions will test for your ability to communicate why any of this matters. At the end of the day, Data Science should help inform business decisions to increase efficiency, profitability, etc. Keep the business understanding part of the CRISP DM process in mind throughout your problem solving.

Additionally, Spring Board has compiled a list of 109 commonly asked Data Science Questions.

KDnuggets also has a list of 21 must know Data Science Interview Questions and Answers.

Follow Up with Thank You Emails

This is probably standard etiquette for any interview but remember to send a personalized thank you email within 24 hours of your interview. Also, if you have thought of the perfect answer to that question you couldn't solve during your interview, include it as well. Don’t forget to express your enthusiasm for the work that Company X does and your desire to work for them.

Repeat

If you get an offer after your first round of data science interviews, Congratulations! Close this tab and grab a beer. If you are turned down, like most of us are, use the lessons you learned from your past interviews to prepare for your next interviews. Interviews are a good way to identify your areas of weakness, and consequently become a better candidate for future openings. It’s important to stay resilient, patient, and keep a learner’s mindset. Statistically, you probably won't get an offer for each position you apply for. Like the excellent data scientist you are, debug your interviewing process and up your future odds.

Additional Resources:

  • Erin Shellman’s Crushed it! Landing a data science job
  • Trey Causey’s What it's like to be on the data science job market
  • Contingency plan: Arm yourself with some humor in case your interview goes completely south by watching Silicon Valley's Hot Dog, not Hot Dog Classifier


Other Great Data Science Blog Posts To Help Make You A Better Data Scientist!

How To Ensure Your Data Science Teams And Projects Succeed!
Why And How To Convince Your Executives To Invest in A Data Science Team?



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11 Comments
Manchun Kumar link
9/7/2017 08:17:06 am

Thank you for sharing great list of data science interview questions and answers. I am looking for more question and answer.

Reply
Ben link
9/10/2017 08:26:54 am

Hello Manchun Kumarlink,

Data science has a combination of technical questions that can be asked. We are going to have to write a post to answer this better. However, here are some quick tips.

The most common are programming, statistics and data storage questions.

The depth of each depends on what the role you are applying for requires.

Programming might have questions like

Can you do an in-order tree reversal if you already have the Node class.

Answer

void inOrder(Node root) {


Node last = null;

if(root != last)
{


inOrder(root.left);
System.out.print(root.data + " ") ;
inOrder(root.right);

}
}

This would be a warm-up to much more difficult sorts and implementation type questions. It has been asked before.

For statistics, they might ask you to explain how Bayes theorem works, or why the ROC is necessary?


They may also ask you questions with less concrete answers like:

-How do you create a metric
-How would you create a metric using the data we are providing

The questions vary widely. We will have to write an actual post to answer this question correctly!

Cheers,

Ben

Reply
Joel link
4/23/2018 03:51:58 am

Hi There,

I love all the posts, I really enjoyed.
I would like more information about this, because it is very nice., Thanks for sharing.

am getting the below error when tried to install any lib like urllib,urlib2,request etc
my OS is Windows:7 / 64 Bit

What could be the reason , can you guys help me ?
Python Code: (Double-click to select all)
1 C:\Program Files\Python36\Scripts>pip install urllib
Error:
Traceback (most recent call last):
File "c:\program files\python36\lib\runpy.py", line 193, in _run_module_as_mai
n
"__main__", mod_spec)
File "c:\program files\python36\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Program Files\Python36\Scripts\pip.exe\__main__.py", line 5, in <modu
le>
ImportError: cannot import name 'main'

The allocation of Python heap space for Python objects is done by Python memory manager. The core API gives access to some <a href="https://asha24.com/blog/python-interview-questions-and-answers/"> tools for the programmer to code. </a> Python also have an inbuilt garbage collector, which recycle all the unused memory and frees the memory and makes it available to the heap space.
Excellent tutorials - very easy to understand with all the details. I hope you will continue to provide more such tutorials.

Obrigado,
Joel

Reply
Peter
12/12/2017 03:04:26 am

Good general advice, but not really that specific to data science. I suggest practicing actual data science interview questions: https://www.testdome.com/d/data-science-interview-questions/28
And Python interview questions: https://www.testdome.com/d/python-interview-questions/9

R and Python are the two most used programming languages in the field, so one or the other is good to practice.

Reply
Ben
12/12/2017 10:38:43 am

I totally agree! This advice is more general. Partly because we have seen different companies have a pretty wide swatch of interview practices. Some use questions more geared towards programmers, others more towards statistics, and still others almost focus solely on SQL. Understanding python and R are key!! Thank you so much for your input!

Reply
Readers Fusion link
3/19/2018 11:12:56 am

Data Science is in itself a universe to study. When going for an interview, you must be prepared with the latest technological updates and this blog covers most of it.

Reply
Readers Fusion link
3/19/2018 11:15:06 am

The blog covers most of the interview questions and preparation material. So a heads up to the writer of the blog

Reply
Shivangi Roy link
3/21/2018 02:03:59 am

I appreciate your work on data science. It's such a wonderful read on data science. Keep sharing stuffs like this. I am also educating people on similar technologies so if you are interested to know more you can watch this:- https://www.youtube.com/watch?v=1ek7IdGhbXI

Reply
Shivangi Roy link
3/21/2018 02:14:10 am


I appreciate your work on data science. It's such a wonderful read on data science. Keep sharing stuffs like this. I am also educating people on similar technologies so if you are interested to know more you can watch this:- https://www.youtube.com/watch?v=1ek7IdGhbXI

Reply
Shivangi Roy link
3/29/2018 12:54:44 am

I appreciate your work on Aws. It's such a wonderful read on Aws. Keep sharing stuffs like this. I am also educating people on similar technologies so if you are interested to know more you can watch this:-
https://www.youtube.com/watch?v=1ek7IdGhbXI

Reply
Where is Waldo Wandering link
11/22/2020 08:08:02 pm

God bbless

Reply



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  • Services
    • All Data Science Services
    • Fraud and Anomaly Detection
    • Data Engineering And Automation
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    • Data Analytics As A Service
    • Data Science Trainings >
      • Python, SQL and R Trainings
      • ARIMA And Predictive Model Forecasting
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