Preparing for a data analyst interview can seem like a mystery, and we regularly hear from members about how to study and what to expect.
One of the best tips is this: Work backwards when you create a study plan for your analyst interview. Here’s how:
Ideally, you have about a month to prepare. This will allow you to assess the gaps in your knowledge, develop and practice core concepts, and build your interviewing skills. But even if you have less time, there’s plenty you can do.
This interview prep guide offers direction, covering what to study, how to study, and what you can do to prepare for a data analyst interview.
It’s difficult to study for a test, if you don’t know the format. Fortunately, there are many resources available to you that will offer insights into the process, and help you know exactly what to expect.
Here are interview prep tips and resources you can use:
As a data analyst, your job will be to solve problems for businesses. But what types of problems does the business need you to solve?
This is a question you have to answer before you even begin prepping. Having a strong grasp of the business model, customers and potential problems they may face will help you narrow your study prep.
In particular, you should be thinking about:
Why This Matters Data analyst roles require technical and business savvy. Therefore, you need to think about data from a business perspective, and understand which data insights can help move the business forward.
Answering these questions will also help you direct your attention to the types of questions to study. You can look for analytics case study questions that are similar to the types of problems you’ll be facing, as well as SQL questions that ask you to write queries that pull the types of metrics you expect to pull.
The next phase is plotting out what to study and how to prioritize your study.. Although your focus will vary by the analyst role you’re preparing for, there are three areas you’ll need to focus your attention. They are:
SQL is a must-have skill for analyst roles, and fortunately, practicing SQL is straightforward. You start with easy and medium difficulty problems, and work your way up to advanced problems.
During the interview, your goal is to write clean code, as efficiently as possible. One good way to practice this is benchmarking your progress. Time how long it takes to complete a problem, and see if you can continue to improve your speed in questions at that level. Most 30-minute technical screens for data analyst roles include 1-2 medium-level SQL questions, and therefore, that should be a goal in your prep.
Beyond SQL, the job description will offer clues about other technical subjects to study. Some of the most common include:
Questions to study: Practice easy, medium and hard SQL data analyst problems. Familiarize yourself with core concepts in Excel and Tableau, and a basic understanding of Python or statistical coding can be helpful.
Strong SQL skills will only get you so far; you also need to have solid business intuition. And that’s a more difficult skill to study for.
In particular, you should know:
Developing business sense starts with understanding the company. If you know the business model, understand how the company can make or lose money, and understand how the business acquires customers, defining metrics becomes easier.
Questions to study: Focus on business and analytics case questions. These questions present a business or analytics problem, and you’ll be responsible for determining how to diagnose the problem, the metrics you will track, and how you would go about the work.
Data intuition can be defined as your ability to read numbers and make sense of them quickly. An analyst with good data intuition understands when a conversion rate is low, or if the numbers look off. In other words, they know where to look in the data to find a solution.
Like business sense, you can develop this skill by practicing analytics case and product metrics case questions. This will help you practice diving into data problems and communicating how you would approach the problem.
Additionally, it’s helpful to brush up on statistics and probability concepts. For example, you should be able to talk confidently about causality vs correlation, P-values, confidence intervals, etc.
Questions to study: Practice general data analyst interview questions, as well as hypotheticals and analytics case studies. Statistics and probability questions are also helpful, both definitions and logic-based questions.
Finally, many behavioral questions for data analysts also ask you about past projects, data problems you’ve solved, etc. and these are also great for building this skill.
Everyone’s study plan is different, but here’s one tip for structuring your study time. Prioritize your study by the most important skills for the role, followed closely by where you have gaps in your knowledge.
Here’s how you can do that:
As you study, don’t forget to benchmark your progress. Conducting mock interviews or working with a data science coach is one of the best ways to do this.
At a minimum, you should be tracking the questions you’ve answered, the difficulty of the question, time to solve (for longer-form questions), and how easy it was for you to answer..
Ideally, you would have a month to study for a data analyst interview. That would give you enough time to research the company and role, plot out your study plan, practice and do some mock interviews.
In fact, we recommend that you start studying before you even apply for jobs. But even if you only have a few weeks, or just a weekend, the same rules apply. Focus on core skills for the role and gaps in your knowledge, practice as much as possible, and try to fit in at least one mock interview, even if it’s just a peer-to-peer interview.