You applied for a data science job and received an interview call for the data science job role. Well done! Congratulations!
The thought on how to prepare for a data scientist job interview seems to be fretting you all throughout because you don’t know whether you are ready for these data science interviews or not.
A Data Scientist Job Interview is not a test of your math and statistics knowledge but your ability to use it at the right time to create business solution. Studies show that well-prepared candidates fail to clear the tough data science interviews as they are not able to explain their skills in an F2F setting. Even if you are confident about the skills required for the position, have several references at top tech companies and have no impostor syndromes- data science interviews can turn out to be a stressful experience if you are not prepared.
We often receive questions from our data science students asking “What should I do to get hired as a Data Scientist?” and “How to prepare for a data science interview?” The Data Science faculty at DeZyre have a good understanding of the data science industry and their requirements. Here’s a valuable piece of advice from DeZyre faculty and industry experts on “How to prepare for a data science interview?” - for landing a job as a data scientist.
There is lot of excitement in the data science industry with Data Science being crowned as the hottest career option with tons of opportunities, better prospects and the great salary companies willing to shell out for skilled personnel who can extract meaningful insights from data. Considering the multi-disciplinary nature of data science requiring a huge set of skills- data scientist interviews are stressful not just for the candidate but also for the interviewer. Even interviewers are buried under a pile of data analysis requests for the business and want to hire a highly skilled personnel who can help them succeed.
CLICK HERE to get the Data Scientist Salary Report for 2016 delivered to your inbox!
How to prepare for a Data Science Interview?
One need not have a PhD to apply for a data scientist job, anybody who loves working with numbers can apply. You can land a data scientist job even without having some sort of STEM (Science, Technology Engineering or Math) degree. It is true that people from math and statistics background get an extra point when the data science resumes are being analysed. If you are from a social science background and can best bit about your software programming skills and quantitative abilities through various project based certified online courses you have taken and internship jobs – you can never fail to impress the recruiter.
So if you do not hail from a computer science background, you should start taking online MOOC’s from eLearning providers like DeZyre or Coursera for a great start in the data science industry. MOOC’s will help you gain an in-depth understanding on what you are doing with a given dataset.
Spend Time on the Initial Formalities to get an Interview Call
“First impressions are the most lasting. You never get a second chance to make a first impression. “- Andrew Grant
Before you prepare for a data scientist interview it is recommended that you spend sufficient time on crafting the professional material- Resume and Cover Letter. Data science resume and cover letter should matter to you as they show how you chose to represent yourself professionally. Typos are a big NO and any resume or cover letter where the candidate boasts - is a sure way to get the application rejected. Even if you have 15 years of experience, ensure that your cover letter and data science resume should not be pages long. One page cover letter and one page data science resume should suffice to attract the attention of the employer with your recent achievements.
You might be interested to read –
Practice Various Data Science Problems to Exhibit your Excellent Problem Solving Skills
One of the most challenging part of a data scientist job role is finding an effective solution to a business problem with the help of available datasets. Demonstrating this skill in any data scientist interview is very difficult unless you have worked on diverse real data science problems. It is very important for the candidates to familiarize themselves with popular datasets, coding algorithms and data science problems.
The best resource to getting started on this is downloading datasets available on Kaggle and actively participating in various Kaggle competitions to get exposure to the different data science projects and gain experience from them. Kaggle competitions help candidates test their knowledge of various data science skills against the open market.
Similar to Kaggle, you can also enrol yourself for Hackerday, where just a minimum sum of $9 a month, you can work on 2 data science projects, while being guided by an industry mentor.
Building a Project Portfolio
Hiring managers or recruiters are looking for candidates with a great project portfolio and it really does not matter which educational background they are from. Whether you are a computer science, applied physics or a social science graduate, you will need to show that you have worked on projects when you apply for a data scientist role. If you have worked on hands-on projects in data science, then any low grades in school or college are likely to be overlooked by the interviewer.
Applying for a data science job does not require a relevant degree but before landing a top gig as a data scientist the candidate will be tested on specific skills. Data Science requires knowledge of coding in various technologies like Python or R, Hadoop or Spark, Scala, Julia, etc.
Having a GitHub account helps interviewers verify that the candidate can actually code and judge him on various aspects like –how efficient his code is, how familiar is the candidate with a given programming language stack and how curious the candidate is in looking for answers in data.
Another interesting to way to build your project portfolio is enrolling for DeZyre’s data science MOOC in Python and R. The hands-on project based comprehensive training helps candidates build a project portfolio (with lifetime access) on various data science projects that candidates can highlight on their data science resume and LinkedIn profile.
Networking with Peers
Connect with big data and data science influencers in the industry through LinkedIn, Twitter or any other social media. Join various groups that discuss about emerging trends in big data and data science technologies, machine learning, data management to stay up-to-date with the latest advancements.
If you would like more information about Data Science careers, please click the orange "Request Info" button on top of this page.
No matter how much and how hard you prepare for a data science interview, candidates are always curious to know what will be the interview structure and what kind of interview questions will be asked in a data scientist interview.
Data Science interviews are usually categorized into 3 different levels-
- The first level of a data science interview tests a candidate’s knowledge on logical and analytical aptitude through various data science interview questions based on puzzles, guess estimation and some quick math concepts. Sample Data Science Interview Questions asked in Level 1 of Data Science Interview –
- Explain the solution to the Monty Hall Problem.
- You are given 7 marbles, 6 of which weigh exactly the same, but one marble weighs less than the other 6. You also have a weighing scale. Determine the minimum number of attempts to find the lightest marble.
- If it rains on Saturday with probability 45% and it rains on Sunday with probability 15%, what is the probability that it will rain this weekend?
- A birthday cake needs to be cut into 8 equal pieces with just 3 cuts. How can you do this?
- Is 20 times 15 greater than 400?
- How many iPhones are sold in US every year?
Here are some more data science interview questions asked at top tech companies like Apple, Facebook-
- The second level of a data science interview tests a candidates knowledge on the daily activities of a data scientist like data cleaning and data preparation challenges. Most of the data scientist interview questions asked at this level include questions based on various statistical concepts, machine learning concepts and programming based questions in languages like Python and R. Sample Data Science Interview Questions asked in Level 2 of Data Science Interview include -
- Explain the difference between L1 and L2 regularizations.
- What are the limitations resampling methods bring in?
- Explain about the significance of selection bias
- How can you create a logistic regression model through ANN?
- Which command is used to create a Histogram visualization in R language?
Below are few more frequently asked Data Science Interview Question and Answers that you might be interested to read-?
- The third level of a data science interview basically tests hands-on experience of the candidate on various case study and scenario based questions. This level of a data science interview assesses a candidate on his thought process of approaching a given business problem, data preparation methodologies, exploratory data analysis, and modelling capabilities. Sample Data Science Interview Questions asked in Level 3 of Data Scientist Interview include-
- Candidates are given sample datasets and asked to create models based on the given dataset.
- How can you help a company increase their ROI using data science techniques?
Data Science Interview Tips
All Set for your Next Data Science Interview? Here are some handy data science interview tips that you might not want to miss out on -
- It is the responsibility of the interviewee to direct the interviewer towards the concepts he or she is familiar with. Whatever you speak in the interview or write in your CV is an indication for the interviewer to process the application in that direction. Do not talk about deep learning if you do not know much about.
- A jack of all trades but master of none does not work well in a data science interview. Though data science is multi-disciplinary field, a candidate must possess an expertise in at least one skill to help the interviewers overlook other flaws. If you are an excellent R programmer without in-depth knowledge in Machine Learning, it is ok.
- Be well prepared with the data science projects you have worked on and ensure that you know each and every minute detail of the project right from how data was collected, prepared and analysed. If an interviewee is not taking the opportunity to get into the details of the project that they worked on they can never hit the mark in clearing the interview.
- Interviewee always speaks more than the interviewer, probably a 70%/30% ratio. Interviewee should engage in a free flowing conversation to achieve the goal.
- Do not use the phrase “I don’t know” for a question that you do not have the answer for. Instead, use a positive tone to answer such questions like let me think about it.
- Data Science Interviews most likely test your knowledge on how you approach a given business problem. The interviewers are trying to test your approach of solving a problem and not interviewing you to get the exact answer.
Hope we have helped you get started with your data science interview preparation. Do not worry if you have not cleared a data science interview the very first time, there are many opportunities opening up in the data science industry and you can always apply for a new one.
Please share your experiences in similar data science interviews in the comments below. It will help the entire data science community to learn.