How to answer common data analyst interview questions
So you want a data analyst job. It’s a great career! Data analysts are responsible for interpreting data, ensuring it’s accurate, collecting it, and presenting the results so that the company they work for can make strategic decisions.
The average salary for data analysts in the U.S., according to Salary.com, is over $79,000 per year. That figure can run up to around $90,000, depending on where you live. This shows that companies are realizing the value of data-driven analytics in finding and keeping customers.
Now, getting the data analyst job you want is the tricky part. You’ll have to be ready for your interview. Let’s answer some questions you should ask yourself, then cover some questions you’ll likely hear in your data analyst interview.
How do I prepare for a Data Analyst interview?
You need to read the job description carefully. Learn what technical skills they expect from you, and data analytics certification is required.
Getting inside the thought process of hiring managers requires analyzing the information they’ve given candidates about the job. They’ll expect someone who knows data science and is more than likely familiar with multiple programming languages.
You should also study the interviewing process in general. Memorize your selling points, anticipate the interviewer’s questions, prepare your questions for them, and practice your answers. Have something nice to wear, too!
What are the top 3 skills of a Data Analyst?
Companies hiring data analysts want to see that you have:
Understanding what the data means and how it was collected and stored lets companies make decisions about future actions that will affect their overall strategy.
You’ll also have to problem-solve because you will most likely work with interconnected disparate systems. And the platforms you’re tasked with using may be connected by a skillful IT department or cobbled together hastily.
This one seems obvious, but realize that such skills need to be wide-ranging. Everything from Excel, SQL, R, or Python programming to getting the printer to work right may be knowledge sets you need to have.
Having the analytics tools to handle large datasets, both literally and figuratively, gives you value. Companies may also look for certifications that prove you have the training and education they’ll want.
You’ll have to present your findings to people who will probably not be as computer-savvy as you are. They need the data, but they also need to understand the data.
If you’re a good public speaker who can distill complex ideas and big data analysis projects into simple concepts, you’ll be able to give your company what they want.
Since you may be required to oversee people as a part of project management duties, your communication ability needs to be top-notch.
Why should we Hire you as a Data Analyst?
This is one of the vital analyst interview questions and answers you’ll have to get right.
In answering this question:
- Don’t Be Sarcastic: Even if you get the sense that the interviewer will find it funny, don’t take the chance. Answer this question plainly and with respect.
- Don’t Be Dishonest: Don’t lie about your abilities. It will become apparent as you get in over your head if you’re doing some data cleaning and accidentally wipe out a database.
- Don’t Say “I Don’t Know”: If you don’t know, why are you in the interview? They want to hire someone who does know why they’re a good fit for the company.
There are, however, some things you should do. In your response, you should:
- Be Confident: Believe in your abilities and discuss how they’ll benefit the business.
- Talk About Your Successes: If you reduced costs by 30% for your last company, mention that you believe you can do the same for the company that will be hiring you.
- Express Gratitude: Tell the interviewer, “great question…” and tell them you won’t let them down if they hire you.
Above all else, project the feeling that you are the right one for this position.
The most common interview questions for a Data Analyst
These questions may vary depending on where you apply to work. We’ll give you some answers along the lines of what you would want to give.
What do you enjoy most about Data Analytics? Is there anything you like least?
“I enjoy data analytics because I can add value. Using critical thinking skills to find a solution is highly satisfying. Knowing that the company wouldn’t have the knowledge to succeed in their efforts, be it a digital marketing campaign or redesigning a product, without my abilities makes me feel useful.”
“As for what I like least about data analytics, it’s when disparate systems with poor compatibility get in the way of doing my job. I’m always happy to work my way through a couple of programs that don’t like talking to each other, but I’d rather be finding the data I’m looking for than troubleshoot.”
What was your most successful/most challenging Data Analysis project?
“Great question. In my last job, I worked with the local police department and the local sheriff’s office. They wanted a clear data set showing specific reports of crimes in specific areas of the city so they could get more accurate reporting on their new shared crime map. It involved creating graphics, pouring through scores of crime data, and days of data validation between the two organizations.”
“After it was all said and done, they had a much clearer idea of where crimes were happening and who committed them. Thanks to that project, overall crime dropped 8%.”
What’s the largest data set you’ve worked with?
“Well, the largest would be a Spark SQL data set that was so large it crashed the system! We had to increase the ram on the server to handle it. Uncompressed, the data set was 130GB. This translates to about 130 million records in CSV format.”
What are the various steps involved in any analytics project?
“I always start with ‘why.’ That’s step one. Why am I about to pour through this data? To do this, I need to understand the project’s business and scope. Step 2 is finding the data. I will access the appropriate databases, requesting access if I don’t have it, and if applicable, find the right APIs. Depending on the project, I may also be scouring for open data.”
“For step 3, I’ll explore the data and do a thorough data cleaning. Step 4 is enriching the data set to fill in the gaps. Step 5 is to make the data visual, so it’s useful. Step 6 is where things get into the predictive phase. The data can be extrapolated using machine learning algorithms to predict future behavior. Of course, step 7 is to do it all over again!”
“Finding and cleaning data never ends. I’m also Asana Project Management certified, so proper planning and managing the process is always top of mind for me.”
What Data Analytics tasks or responsibilities do you think are most important to our Organization?
“I put a big emphasis on data cleansing and enrichment. Data is no good if it isn’t accurate. While I work hard to make sure that I have as many sources as possible for fresh data, I put in even more effort to eliminate bad data and ensure the good data that’s leftover is complete.”
What is your process for cleaning data?
“Great question. There are many standard processes in cleaning data…looking for regular expression patterns, cross-field verification, range constraints, and so on. I start by looking at mandatory constraints so I can eliminate blank spaces in the data sets. Incompleteness is something I want to eliminate as soon as possible so that the right information can be filled in.”
“Then I check for accuracy, consistency, and uniformity. Once I can trust the integrity of the data, I can collect it for its intended purpose.”
What Data Analytics software are you, familiar, with?
“I personally love Microsoft BI. I have also used Oracle, Qlik, and got my certification in Tableau. If I have to use a mobile device, I rely on AnswerRocket because it has the best interface and voice recognition.”
What scripting languages are you trained in?
What do you do to keep up with data trends?
“I love learning. I spend time reading major computing publications and spending time in forums with other data geeks like myself! I also subscribe to Google alerts to keep me up-to-date in case I miss anything. I also follow Google Trends to see what’s spiking in popularity or to discover new hardware or software that might make me more effective at what I do.”
Explain the differences between data profiling and data mining.
“Data mining is looking for patterns in an existing database. I’m looking for valid and useful data that can be incorporated into a project without any reservations. Data profiling is more about assessing the data quality so that it can be corrected later.”
Are you ready for your interview?
Becoming a data analyst can be an excellent career move, but you’ve got to ace that interview first. Training, learning, getting certifications, and staying on top of trends will put you at the top of the talent pool. But remember, other people want that job too, so it’s up to you to stay ahead of the competition. Good luck!
Was this helpful?
Thanks! What made it helpful?
How could we improve this post?