Congratulations! You’ve successfully landed an interview for a data analyst position. Now comes the hard part—the interview. However, it doesn’t have to be. An interview is just a company’s way of making sure that you are the right person for the job.
There’s nothing to worry about. Even if it is your first interview for the role of a data analyst, you can tackle it well and increase your chances of landing the job. The most important thing is to be prepared. No interviewer wants to see you caught off guard by a question they ask. We’re here to help you.
Common Data Science Interview Questions
The following article lists down common questions you will come across during your data analyst interview and there are even some helpful hints to make sure that you are ready.
What are the job requirements of a data analyst?
This is a simple question that is designed to make sure you know what the required skills are for a data analyst. The skills you need are as follows:
You must have a strong understanding of multiple programming languages, databases, and reporting packages. It’s not just important to list that you understand these things. Make sure to be specific. Note the programming languages you’re skilled in, the databases you’ve worked with, and demonstrate your skills with reporting packages.
What are the daily responsibilities of a data analyst?
Of all the questions you may come across in your interview, this is the most likely. As a data analyst, you’re tasked with performing the following:
- Collecting and interpreting data
- Analyzing the results of the data.
- Filter data collected from multiple sources
- Provide assistance and support towards every part of data analysis
- Identify patterns hidden within complex datasets
- Ensure databases are secured and well-maintained
There is no particular order in which you list these responsibilities, and feel free to add others if you believe they are relevant to the question. However, these are definitely the most important responsibilities to list so make sure you try to say them all.
What’s the difference between data analysis and data mining?
Just like the previous questions that mention the responsibilities of the job, this question helps ensure the employer you know the fine details of your position. Simply put, data mining is the process of identifying patterns in data.
This is most often used for machine learning. On the other hand, data analysis requires you to collect raw data, clean it, and organize it before performing analysis.
It’s easy to get these processes confused. Make sure you can clearly demonstrate the differences between the positions. If it helps, create a table where you can study the differences and clearly see what they are.
What was the most difficult data analyst project you worked on?
The purpose of this question is for your employer to gain insight into how you approach and deal with difficult problems. It also shows them the type of work you’ve done in the past. Take this question as an opportunity to demonstrate your skills and the role you played in handling a difficult task.
Don’t mention or blame others for things that may have gone wrong. If there was something that went wrong and you can’t avoid mentioning it, make sure to highlight what you learned from the experience and how you applied that knowledge when something similar happened.
What does it mean to clean data/ what is data cleaning?
Data cleaning, data cleansing, or wrangling is all the same. It’s the process of finding and removing errors in data to increase its quality. There is another question that may come along with this one, which involves the process of validating data.
Validating data can be considered as a type of data cleansing. Typically, there are two ways to validate data. The first method is to screen the data through various algorithms to identify any values that aren’t accurate. The second method is to verify the data and then decide on whether it is included in the data set.
Why did you get into data analysis/why do you want to be a data analyst?
This question is designed to help your employer see who you are as a person. It is also a nice icebreaker that many interviewers may ask at the start of the interview or towards the end as a way to close things gently.
Make sure you give focused answers that succinctly explains why you are interested in data analysis. A short anecdotal story that involves you going the extra mile, creating reports, or working on a project at a young age is a great way to demonstrate how you were inspired to become a data analyst.
Where do you see yourself in five years/ten years/ etc.?
This question can be difficult and it is often used for interviewers of all sorts of positions, not just data analysts. The best thing for you to do with this question is to keep things rather general. Talk about what your hopes are with the company.
Suggest that you want to work towards a senior position in data analysis. You can add something about your personal life, such as paying off debts or establishing a great balance between work and life but try to stay on topic and keep things related to the position.
Take a few minutes to explain how you would estimate something.
This could be any number of things. For example, you may be asked to estimate how many shoes a company will sell every month. Whatever the calculation may be, it is important to apply your knowledge as an analyst and show how you’d approach the situation, similarly to how you would do so if you are accepted for the job.
Specifically, make sure the interviewer sees that you are creative, can quickly identify the data segments and variables in the problem, and can clearly communicate the thought process. This question can be difficult to approach when you’re caught off guard, so practice a few scenarios to get more familiar with answering it.
What’s your process when tackling a new project?
Employers want to know that you can take on a new project and hit the ground running. They want to be assured that your time and efforts towards completing a task are efficient and the results are reliable.
This question needs to be answered with very clear steps. Make sure your process is deliberate and you are considering the different variables involved, particularly the deadline. A good way to start your answer is by stating that you’d look over the project thoroughly. This demonstrates that you won’t rush and you’re careful enough to get all the data you need before getting started.
What is a cluster?
A cluster is a way in which you collect or identify data into a specific group or cluster. Clustering algorithms may have the following properties:
- Soft and hard
- Flat or hierarchical
Additional Things to Keep in Mind
These are some of the basic questions that you may come across during your interview, but there are several other things you may have to keep in mind. Some of these topics won’t be mentioned unless you are applying for a more technical or advanced position beyond entry-level work. It’s important to make sure you have a strong understanding of the following.
It’s important to know what tools data analysts use. Even if you aren’t directly asked about these tools, you should still mention them when the opportunity arises and discuss how you use them whenever it is relevant. Some of the most common tools used are Tableau, Google Search Operators and Fusion Tables, KNIME, Solver, NodeXL, io, and RapidMiner.
Time Series Analysis
This is an analysis that is usually performed in a time and frequency domain. This is a process of creating an output forecast by analyzing data from the past and using various techniques, such as log-linear regression and exponential smoothing.
There are several statistical methods commonly used for data analysis that are highly beneficial. You don’t have to name all of them, but it is important to know a few at the very least such as imputation, mathematical optimization, cluster, and spatial processes, Markov process, Bayesian method, and simple algorithm.
Research for Your Data Analyst Interview
Having a list of questions interviewers may ask is important, but you can further increase your chances by doing more research on the company. If it is a large company, you may be able to find a list of common questions they typically ask. This way, you can more appropriately prepare for the job without having to spend as much time guessing at what questions you may come across.
If you have the opportunity, ask other data analysts what questions they came across when they interviewed for the position. Find out if they did or said anything that helped them stand out from other applicants.
No matter what the situation may be, it is important that you relax, communicate effectively, clearly, and answer every question in a way that shows you are ready for the position. If you can answer the above questions well, you are on your way toward having a successful interview.