How to Answer the Most Common Data Analyst Interview Questions
Data analytics has become one of the fastest growing fields in the world. It also has become one of the most lucrative fields to be in with chances to advance your career every day. Before going for an interview for a data analyst position, you should prepare for it. One way is to review important concepts such as statistics and data analysis or validation methods. But another way is to practice answers for the most common questions asked from data analysts.
Lucky for you, we have a guide to teach you how to answer them. So, let’s check them out.
What are the steps involved in an analytics project?
This is one of the most basic questions you are going to be asked at the interview. Companies are really asking you if you are familiar with the steps that need to be taken to accurately analyze data.
There are five major steps that are involved in data analysis. You have to be able to understand the problem, collect data using the correct sampling method, clean the data so that the data is understandable, analyze the data accurately and interpret the results to generate a report.
Which step of a data analysis project do you like the most?
This could be a follow-up question to your previous answer, or it may be asked as a company’s attempt to know more about who you are. While it is perfectly normal to like a step more than the others, make sure not to speak negatively about any aspect of the process.
I enjoy all the steps of data analysis. However, I mostly enjoy the data gathering step. I prefer to use a random sampling over a purposive sampling; though I do understand that sometimes purposive is more suitable. Because I use the random sampling method, I have found that I am able to gather a truly wide set of data that allows me to create a more comprehensive report.
What technical tools are you familiar with?
Technical tools are a great help, especially for a field such as analytics where there can be a mountain of data to analyze. Chances are that some companies will mention which tools they want their data analyst to be familiar with in the job profile when you apply but it is always good to mention two or three of the tools that you are most familiar with.
Through my previous jobs, I have experience with a wide variety of data analytic tools. However, I am most familiar with two tools in particular, SQL and Excel. I have used them in two of my three previous workplaces and therefore have more familiarity with their functions. In my third workplace, I primarily used Python, so I have experience with that tool as well.
What are some common problems you face in data analytics?
As any experienced data analyst knows, you always have issues with data. That is why data cleaning is such an important part of data analysis. Companies need to know that you are aware of the different types of problems that can crop up with the data.
There are many problems that can come up in a data analytics project. Data can be duplicated or missing. The collection of data could not be possible at a particular moment and data may need to be purged. In addition, data also needs to be secured and stored and many problems can arise as a result. As an analyst it is my job to find a solution to these problems, maybe by going through the data carefully or even making sure that there is a secure storage facility for when data is collected.
How do you handle missing values in a data set/ What is your process for cleaning data?
Data preparation: that is the cleaning of data in order to remove duplicates or other issues that will make data invalid, is a crucial step in data analysis. The methods that you use in order to clean data will help a company to know whether you are the right fit for their organization.
I always carefully look for data duplicates prior to beginning analyzation. Sometimes, I would use a tool for this such as when there are large amounts of data. However, I prefer to check for duplication by myself when the data sets are smaller. If there are missing values, I prefer to use the listwise deletion method wherein the entire record is excluded. However, if I believe the record to be of some significance to the analysis by careful understanding of the problem, I try to try an average imputation. I also try to cap any outliers in my data.
What are your strengths and weaknesses as a data analyst?
There are many ways in which this question can be asked but this is the most straightforward way. Your answer should reflect your strengths and weaknesses, truthfully, in the best possible light. However, your answer should not be a general answer that is easily forgettable either.
My greatest strength, I believe, is my attention to detail. In 2019, I was awarded the best regional analyst award at the company because of the level of detail that is found in my reports. But I believe that one of my greatest failings is a lack of understanding of Tableau. I have taken steps to overcome this shortcoming by enrolling in a Certification in Tableau at a nearby college.
Tell me about a time when you got unexpected results.
All results are unexpected. What the interviewer is really trying to determine in this question is what kind of person you are. Are you someone who has preconceptions and tries to fit the data to meet it or are you someone who lets the data speak for itself? Your answer has to be very carefully created, therefore.
I am always surprised by the results that I get from data therefore for me all results are unexpected. But one of the most surprising results that I received was when I was charged to discover the reason why one of our customer’s products was not performing as well as it should have. The reason I discovered was that the marketing campaign was geared towards an entirely wrong customer base and therefore was not converted to sales. I conveyed this to the client with the recommendation that they rethink their marketing strategies. Three months later, I was commended in the company because the client was very much impressed with my work.
What statistical methods have you used?
Competence in statistics is a must for data analysts and is a key component of the job. By questioning you on the statistical methods that you have used, the interviewer is really probing the extent of your knowledge in statistics.
I am familiar with a few different statistical methods as I have studied statistics as part of my degree qualification. In addition, I have used mean and regression in most of my previous companies. In fact, at my last workplace, I worked together with a team of five in order to create a statistical model that measured the success rate at our company and increased profits by 10%.
How do you handle large data sets?
Data analysts usually work with large data sets but recently that has changed. There is more data available now than there was in the past and sometimes the amount of data can be overwhelming. Companies need to know your limits and you need to be specific about the amount of data sets and variables that you have worked with.
I have not worked with very large sets of data previously. The largest data set that I have worked with included about 100 entries with 5 variables. However, I believe that practice makes perfect and slowly growing this number is possible.
What do you see as the biggest ethical issues of data analysis?
Because data analysis sometimes includes people’s personal information, ethical considerations are a serious factor for many companies. Protecting people’s privacy and security is often a major consideration but there are several other issues as well. Companies need employees that are aware of the ethical considerations before they hand over personal data to an employee.
I think one of the biggest concerns facing companies at the moment is data bias. It is often too common in data analysis where a person’s personal bias can color the results and lead to unfair or discriminatory reports. I think as an analyst and a person, everyone should be aware of their own personal biases and try to remove them from the analysis as much as possible.
Now you are prepared for the data analyst portion of your interview. But are you prepared for other common interview questions? Try our guide to the most common interview questions and answers to prepare for your interview.
For more helpful hints, try our interview tips and tricks. For more help, visit our interview tips page or our career guides blog to land the job of your dreams.