In the world of machine learning and artificial intelligence, good old data analytics is often overlooked. Aspiring professionals need to understand that data analysis is a fundamental part of building any machine learning models or AI products. A good data analyst, with a strong understanding of the business, can do wonders. To become a good data analyst, and prove you’re the one in an interview, here are some data analytics interview questions you can prepare for.

We have suggested some answers here. Remember to take them and improvise. 

  • Use real-life examples from your own experiences, even if it’s a small personal project. 
  • Talk about data analytics in the context of the business, not as statistics on its own.
  • Answer truthfully. The interviewer might be kind to someone who doesn’t know but is willing to learn, but no one likes a liar. 

Let’s see some questions now.

Theoretical Data Analytics Interview Questions

Before getting into specific questions about statistical methods and tools, first, get a big picture view. Think of why the world needs data analysts, what would happen if there were no data analysts, when can a data analyst make a difference, etc.

#1 What process would you follow if you were given a project?

This question is a test of your critical thinking and problem-solving skills. Focus less on the tools you will use or methods you’ll follow. And focus more on the overall process. Some of the key steps are:

  • Understanding the business problem
    This is the first step in the data analysis process. This will tell you what are the questions you’re seeking answers for, what hypothesis are you testing, what parameters to measure, how to measure them, etc. 
  • Collecting data
    An important function of the data analytics job is to find the data needed to provide the insights you’re seeking. Some of these might be existing data, which you can access instantly. You might also need to collect new data in the form of surveys, interviews, observations, etc. Gathering the information in an accurate and actionable way is crucial.
  • Data exploration and preparation
    Now, understand the data itself. The parameters, empty fields, correlations, regression, confidence intervals, etc. Clean your data by removing errors and inconsistencies to make sure it’s ready for meaningful analysis.
  • Data analysis
    Manipulate the data in various ways to notice trends and patterns. Pivot tables, plotting, and other visualisation methods can help see the answers clearer. Based on the analysis, interpret and present your conclusions. 
  • Presenting your analysis
    As a data analyst, you will regularly take the findings back to the business teams in a form that they can understand and use. This could be as presentations, or through visualisation tools like Power BI.
  • Predictive analytics
    Depending on whether it’s your role or not, some data analysts also build machine learning models and algorithms as part of their day job.

#2 What are some issues you have come across in data analytics?

This is a great question to have a meaningful discussion about the challenges in data analytics. Be open and tell your story. The quality of data is a huge problem for analysts. Incomplete, inconsistent, error-prone or badly formatted data sucks a lot of the data analysts’ time and energy. Give examples from your own personal projects to support this point.

Also, remember to mention how you solved them. Whether you spent extra time in data cleaning, or wrote scripts to automate it, or re-structured data collection processes, talk about it. Don’t just highlight the issues, also present possible solutions.

#3 Who would you closely work with as a data analyst?

The right answer is everyone. In a data analyst’s role, you might collect data from the entire organisation, work with product owners to help build features, collaborate with marketing to shape strategy, present insights to the C-Suite, etc. A good data analyst must have the communication and people skills to work with anyone productively.

Technical Interview Questions

Let’s get into the specifics of statistics, mathematics and data science.

#4 What statistical methods do you know and can use?

Some of the key concepts that you need to learn are descriptive statistics (mean, mode, standard deviation, etc.), correlations, simple and multivariate regression, confidence intervals, Bayesian method, Markov process, imputation, spatial and cluster processes, etc.

#5 What data analytics tools do you know?

Mention only the tools you know. It helps to indicate your proficiency level as well. For example, say, “I’m very comfortable with MS Excel and SQL, reasonably comfortable with Power BI, but I’ve used Tableau only once.” 

#6 Data Analytics Interview Questions: What is an outlier?

In statistics, outlier is a data point that lies far away from other values in a random sample. This occurs for two reasons: It can be due to variability in measurement or an error. Either way, outliers disproportionately impact statistical analysis and need to be taken care of.

#7 What is the KNN imputation method?

k-nearest neighbors (KNN) is an algorithmic method to replace missing values in a dataset with some plausible values. KNN assumes that you can approximate a missing value by looking at other values closest to it. It is more effective/accurate than using mean/median/mode, and can be performed easily using libraries like Scikit-Learn.

#8 What is K-means clustering?

Analysts use K-means clustering to partition observations into k non-overlapping sub-groups called clusters. It is a popular technique for cluster analysis in data mining.

#9 Data Analytics Interview Questions: What is time series analysis?

A time series is, as the name suggests, a series of data points indexed in time order. Time series analysis is exploring this data for insights. It is often used to predict future occurrences of events, based on past observations. This could be weather forecasting, predicting natural disasters like earthquakes and hurricanes, stock market fluctuations, etc.

#10 What is the difference between variance, covariance and correlation?

Variance is the measure of how far from the mean is each value in a dataset. The higher the variance, the more spread the dataset. This measures magnitude.

Covariance is the measure of how two random variables in a dataset will change together. If covariance of two variables is positive, they move in the same direction, else, they move in opposite directions. This measures direction.

Correlation is the degree to which two random variables in a dataset will change together. This measures magnitude and direction. Covariance will tell you whether or not the two variables move, the correlation coefficient will tell you by what degree they’ll move. 

#11 What is univariate, bivariate, and multivariate analysis?

Univariate analysis is when there is only one variable. This is the simplest form of analysis like trends, you can’t perform causal or relationship analysis this way. For example, growth in the population of a specific city in the last 50 years.

Bivariate analysis is when there are two variables. You can perform causal and relationship analysis. This could be the gender-wise analysis of growth in the population of a specific city.

Multivariate analysis is when there are three or more variables. Here you analyse patterns in multidimensional data, by considering several variables at a time. This could be the break up of population growth in a specific city based on gender, income, employment type etc.

This set of questions is only a start. There are several other concepts and practices that go into a data analytics job. For in-depth learning and hands-on practice, consider Springboard’s online program data analytics career track. In addition to an up-to-date and career-focused curriculum, it also has 1:1 mentorship from leading industry practitioners, career coaching from certified coaches and comes along with a job guarantee. Apply Now!