Data Analyst Interview Questions, Part 2



While you will be expected to answer questions on career goals, experience and Data Analysis basics, the real proof of your worthiness to fill a Data Analyst role is in your technical proficiency.

A new-age translator for a heavily data-led landscape, it should come as no surprise that much of the content of Data Analyst job interviews would be focused on the technical aspect of the role. Demonstrate your breadth of understanding and hands-on experience with handling huge datasets because this is what will help you get the job.

What’s your understanding of Data Cleansing? Data Profiling?

You may get asked to define or compare particular data analysis terms or practices, such as Data Cleansing, Data Profiling or Collaborative Filtering. These are pretty common topics to arise at interview for Data Analyst jobs. As far as cleaning the data, they want to know that you can do the basics as far as detecting and removing errors and inconsistencies from the data and with regards to profiling, this relates to being able to home in on individual attributes of the data in order to better understand it as far as frequency, length and value.

Have you heard the term Outlier? Explain in detail.

This is a term you would be expected to know. Univariate and Multivariate are the two kinds of outliers that exist and they are used when talking about data values that are higher or lower than a set pattern in a sample. Data Analysts would use either the Box plot method or Standard deviation method to detect an outlier.

How would you deal with missing data?

The interviewer may ask if you know what the protocol is with regards to missing or suspected data, in which case you should be able to talk them through the strategies you’d employ such as the deletion method, single imputation methods and model-based methods to recover missing data.

What challenges might you face when performing data analysis?

Here the interviewer will want to hear that you’re prepared for any problems that may arise in your analysis and how you intend to resolve them. Whether you’re faced with something as simple as typos and spelling errors, variations from multiple data sources or incomplete data, you should be well versed in how to tackle them and deliver good quality analysis.

How would you define an effective data model?

Do you know the difference between a good model and a poor model? Characteristics like predictability of performance, adaptability, responsiveness to change and accessibility to clients and customers should all come into play when it comes to developing a strong data model capable of delivering tangible and lucrative results.

Expect much of the questioning to target the technical aspects of Data Analysis because fundamentally that is what you are being hired for. Data Analyst is the type of role that relies heavily on those hard skills as businesses now rely so much on the insights gleaned from datasets.

Certainly the interviewer will also be looking at your softer skillset in terms of how well you can communicate with the business and understand the business scope. However, it is in being able to relate the data to what the business wants to achieve with that data that is key to your role as Data Analyst.


Back to article list