Data Analytics Interview Questions – Set 10

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What do you do for data preparation?

Since data preparation is a critical approach to data analytics, the interviewer might be interested in knowing what path you will take up to clean and transform raw data before processing and analysis. As an answer to this data analytics interview question, you should discuss the model you will be using, along with logical reasoning for it. In addition, you should also discuss how your steps would help you to ensure superior scalability and accelerated data usage.

What are the most common questions you should ask a client before creating a dashboard?

Well, the answer to this question varies on a case-to-case basis. But, here are a few common questions that you can ask while creating a dashboard in Excel.

  • Purpose of the Dashboards
  • Different data sources
  • Usage of the Excel Dashboard
  • The frequency at which the dashboard needs to be updated
  • The version of Office the client uses.

List of some best tools that can be useful for data-analysis?

  • Tableau
  • RapidMiner
  • OpenRefine
  • KNIME
  • Google Search Operators
  • Solver
  • NodeXL
  • io
  • Wolfram Alpha’s
  • Google Fusion tables

What is the criteria to say whether a developed data model is good or not?

  • The developed model should have predictable performance.
  • A good data model can adapt easily to any changes in business requirements.
  • Any major data changes in a good data model should be scalable.
  • A good data model is one that can be easily consumed for actionable results.

Why should you choose data visualization?

Since it is easier to view and understand complex data in the form of charts or graphs, the trend of data visualization has picked up rapidly.

What do you know about interquartile range as data analyst?

A measure of the dispersion of data that is shown in a box plot is referred to as the interquartile range. It is the difference between the upper and the lower quartile.

Differentiate between variance and covariance.

Variance and covariance are both statistical terms. Variance depicts how distant two numbers (quantities) are in relation to the mean value. So, you will only know the magnitude of the relationship between the two quantities (how much the data is spread around the mean). On the contrary, covariance depicts how two random variables will change together. Thus, covariance gives both the direction and magnitude of how two quantities vary with respect to each other.

How do you feel about data? (Swedish)

This question is a measure of your enthusiasm and passion for the field; it serves as a pretty good ice breaker or an en passant between questions. Really about the only thing you don’t want to say is that you don’t have any sort of feeling for data.

I feel that data is king. If you just think about it at a sensory level, data propels everything we do. We take sensory input such as sight, taste, sound, smell, or touch, and we convert that data into actionable insights: only we do it so fast we don’t even realize. But that’s exactly what we do. I’m just the weird type of person who stops to think about the sources of that data and wants to learn what more I can glean from data and how I can use it both more efficiently and effectively.

In your role as a data analyst, have you ever recommend a switch to different processes or tools? What was the result of your recommendation?

or hiring managers, it’s important that they pick a data analyst who is not only knowledgeable but also confident enough to initiate a change that would improve the company’s status quo. When talking about the recommendation you made, give as many details as possible, including your reasoning behind it. Even if the recommendation you made was not implemented, it still demonstrates that you’re driven and you strive for improvement.

Example 
“Although data from non-technical departments is usually handled by data analysts, I’ve worked for a company where colleagues who were not on the data analysis side had access to data. This brought on many cases of misinterpreted data that caused significant damage to the overall company strategy. I gathered examples and pointed out that working with data dictionaries can actually do more harm than good. I recommended that my coworkers depend on data analysts for data access. Once we implemented my recommendation, the cases misinterpreted data dropped drastically.”

Can you add 1-100 together right now? (Dealer.com)

This question is straightforward enough. You could, theoretically, compute the solution simply by adding the numbers in sequence, like so: 1+2+3… But this is impractical and probably not what the interviewer is looking for. Fortunately, there’s a formula called a series sum. It’s the number multiplied by itself plus 1, and the resulting solution divided by 2.

n(n+1)/2

Sample answer: Thankfully, there’s a formula that can help with this: 100(100 + 1) = 10,100; 10,100 / 2 = 5,050.