22 Top Data Analyst Interview Questions and Answers

If you’re considering applying for a data analyst position, then this information might be of your best interest.

What questions will they ask? What answers should you give? Under which parameters will you be assessed? These are all important questions that need answering.

In this blog post, we will clarify all of your doubts about the most common data analyst interview questions and answers that you should be aware of when looking for a data analyst job.

What Does a Data Analyst Do?

A data analyst’s role consists of interpreting and analyzing numerical data in order to extract meaningful information. Data analysts often work with large databases containing customer’s credit card transactions, sales figures, or government records.

Although a data analytics degree isn’t always required for entering this field, most employers prefer candidates who have at least two years of experience working as a database administrator or web analytics specialist before hiring them full time. Many potential candidates prepare themselves by attending a data analytics boot camp or joining a data analytics certification program

If you’re looking for help with your interview process, these are the top Data Analyst Interview Questions and Answers that might come up in an interview:

Data Analyst Interview Questions

These are the most common questions you should expect to be asked in a job interview for a data analyst position to see if you meet the criteria specified in the job description:

1. What is a hash table collision? How can it be prevented?

A hash table collision is when two different pieces of data end up in the same place. It can be prevented by making sure your hashing algorithm doesn’t use a key that’s too small, which will make it more likely for collisions to happen.

Also, there are some other tips that can help reduce the risk of hash collisions by:

Separate chaining — In this method, each slot is filled with the data that hash to a particular value.

Open addressing — This method stores the item in the first available slot.

2. How are Data Mining and Data Profiling Different From Each Other?

You should be able to answer by stating the differences with no hassle. Try to pinpoint the following differences:

Data mining is the process of discovering patterns in a dataset. Data profiling is the process of analyzing data sets that are stored on databases to determine what kind of information they have or how large these datasets are. The two terms often overlap when it comes to appearance and perform similar tasks but generally speaking, each term has its own use case scenario.

3. What are the differences between variance and covariance?

In order to impress employers, you should be able to identify key differences between the two concepts. The following should be noted:

Variance is a measure of dispersion about an average value, while covariance measures how two variables vary together. Variance takes into account the level to which each observation differs from its mean (average), whereas covariance looks at fluctuations in both variables.

4. What is data cleansing?

Data cleansing or data cleaning is the process of detecting and correcting errors, inconsistencies, or missing values in data. Data cleansing can be done manually by a human analyst who will then employ tools such as Excel’s Data Validation feature to identify issues that require further attention. Or it may involve software packages with algorithms that detect anomalies and prescribe corrections for them.

You might want to be accurate when answering this question since data cleansing should be done on a regular basis at most companies.

5. What are some common problems you can find as a data analyst while performing an analysis?

Every data analyst will encounter different problems depending on the type of analysis they’re performing. However, some common ones include:

  • Data points that don’t match up with the rest of the dataset (I.e., not enough data)
  • Data being recorded incorrectly or inconsistently
  • The model built to perform an analysis is inadequate to deal with the problem
  • Data analysis being performed on data that doesn’t have enough context
  • Data quality and data verification are not reliable
  • The model built to perform an analysis is too complicated for a person without a data scientist background.

5. Do you know the differences between R-Squared and Adjusted R-Squared?

This is a technical question that might be hard to answer at first. In order to answer correctly, you should be able to identify the following differences:

R-Squared is a statistical measure that tests whether there is an association between two series or events. If the statistic value is high, then it means that the data shows enough of a dependency between variables.

Adjusted R-Squared is a measure of how well the regression line fits the data. It’s basically just more complex than R-squared and measures different things about whether or not your model is good, but it doesn’t really matter what they are since you should only be using them to check estimation error by comparing them to other models.

6. Can you define an N-Gram?

This might be a tricky question since it’s quite technical. An N-Gram is a sequence of words, letters, or other symbols. They are often used in statistical analysis to predict the next word based on what has been said before it and can also be helpful when trying to find out if people with certain characteristics use different language than others.

7. Are you familiar with the KNN imputation method?

You should be able to answer this question if you happen to have experience or a solid background in data analysis.

The KNN imputation method is a way of estimating the values for missing data points in an incomplete dataset. It works by finding similar observations or “neighbors” within that dataset and using their known information to fill in the gaps.

8. Can you describe the process of Data Analysis?

If you’re trying to land a data analysis job, you should be able to answer this question easily. You might want to look for candidates that can answer something similar to the following:

Data analysis is the process of gathering data, organizing it into a format that is easily readable and understandable, then performing one or more analyses on the data. Data analysis can be performed on many different types of data, and there are a wide variety of methods for analyzing the results.

9. What’s an outlier?

You definitely want to go ahead and answer this question if you want to be considered a strong candidate.

In data analysis, an outlier is an observation point that is well outside the overall pattern of data. There are many different types of outliers, with some being significantly more important than others.

For instance, a single outlier data point on a graph charting the height of hundreds of people is less significant than a standalone data point that’s significantly different from all other points.

10. What is a waterfall chart?

This is a basic question that you should be able to answer correctly. A waterfall chart is a type of data visualization that shows how one variable changes over time.

It often starts with the initial value and ends at the final, cumulative point. This figure is typically used to show change in revenue or expenses over a period of time.

11. What’s univariate analysis?

Univariate analysis is a type of data exploration that shows the relationship between one variable and another.

This can be done in two different ways: cross-tabulations or histograms. Data analysts use these analyses to find correlations, trends, outliers, patterns, and other statistical relationships in order to generate insights about the data set being analyzed.

You’ll want to make sure your knowledge of this type of analysis is fresh as it might come in quite handy in the interview.

12. What’s bivariate analysis?

It’s important that you can answer with a concise definition for this type of analysis.

The bivariate analysis does just what it says: analyzes two variables of data at the same time. Data analysts use this type of analysis to find correlations, trends, outliers, patterns, and statistical relationships in order to generate insights about the data set being analyzed.

13. What’s multivariate analysis?

This is another type of analysis that data analysts use. Data analysts conduct multivariate analyses to answer specific questions about a research subject, and it’s the most complex form of statistics.

The main goal of this type of statistical analysis is to use different variables in order to determine which ones have the strongest correlations or associations with each other on certain aspects of interest. It’s often used to identify a pattern in the data, but it can also be used for exploratory purposes.

14. What’s the Hierarchical Clustering Algorithm?

The Hierarchical Clustering Algorithm is a form of statistical analysis that data analysts often use in order to answer certain questions about their research subject. It’s a hierarchical method that identifies groups or clusters within the data so they can be analyzed separately, and it assigns them labels based on how dissimilar from each other they are.

15. What’s the definition of clustering?

Clustering is a technique that data analysts use in order to organize and categorize their subjects of interest. SQL data clustering can be used for both exploratory purposes, or as the first step towards an analytical procedure such as regression analysis.

16. Are you familiar with collaborative filtering?

Collaborative filtering is a technique that data analysts often use to improve the efficiency of their research. It’s based on contributions from other people who are familiar with similar subjects, and it can be used for anything from recommending books or movies, to suggesting which products somebody should buy next.

17. In data analysis, what is “normal distribution?

Normal distribution, also known as a Gaussian Distribution in data science, is a pattern that often appears in data. It’s the statistical representation of how frequently certain events will occur over time and space.

It’s important because it’s a common distribution and has specific properties that data analysts can use to compare one dataset with another.

18. Do you know the differences between R-Squared and Adjusted R-Squared?

This is a technical question that might be hard to answer at first. In order to answer correctly, you should be able to identify the following differences:

R-Squared is a statistical measure that tests whether there is an association between two series or events. If the statistic value is high, then it means that the data shows enough of a dependency between variables.

Adjusted R-Squared is a measure of how well the regression line fits the data. It’s basically just more complex than R-squared and measures different things about whether or not your model is good, but it doesn’t really matter what they are since you should only be using them to check estimation error by comparing them to other models.

19. What do you need in order to become a good data analyst?

This is the million-dollar question. There are many different skills that you need to become a good data analyst. Some of these include:

– An analytical mindset, which includes being able to break down a problem and find the best solution quickly. Data analysts will be asked “What is your recommendation for X?” or “How would you solve this issue?”, so they must have problem-solving skill sets.

– Data analysis skills, such as a solid understanding of statistics and mathematics as well as data cleansing techniques. Data analysts will be using statistical methods such as linear regression or neural networks in order to analyze their data so they need a good grasp of the math behind these tools. They also need strong programming skills to be able to correctly analyze their data.

– Data visualization skills, which include both the ability to create charts and graphs as well as an understanding of how different types of graphics can help or hurt analysis results.

– Communication skills for presenting findings in a clear manner so that they are easy for people who don’t have strong math backgrounds to understand.

– Data management skills like understanding of how to set up databases and CRMs for a business with large amounts of data as well as the ability to take care of day-to-day tasks like backups or importing new customers into systems.

20. What are the best tools for data analysis?

This question seeks to put the interviewee’s knowledge to the test. You should be familiar with the best tools in data analysis and data visualization. For this question, you should mention tools such as Tableau, Hadoop, NodeXL, RapidMiner, and Google Search Operators.

21. What is machine learning?

Machine learning is the field of study that deals with algorithms. These algorithms are designed to take in data and make predictions about future events based on what they learned from past events. Artificial intelligence (AI) can often be considered a subset of machine learning where we program computers to act in ways that mimic human behavior, like decision-making or speech recognition for example.

22. What does “big data” mean?

Big Data refers to datasets so large and complex it becomes difficult to process them using traditional methods such as relational databases due to limitations on time, storage space, or both because of their size.

The term was coined by Gartner back in 2001 but became more popular when Google used it internally to describe the huge amount of data they were processing to improve their search engine.

Data Analyst Interview Questions: Final Thoughts

Data analysts are in huge demand with the number of jobs in this profession set to grow by 12% annually for many years. Data analyst interview questions are a great way for companies to get an idea of how well applicants can handle difficult data-related challenges.

However, with so many different types of data analyst interview questions out there it’s important that job seekers have a comprehensive understanding of the types of data analyst interview questions they might need to answer.

One important thing is not taking these types of data analyst interview questions lightly because they’re often what sets apart qualified candidates from those who are not.

I hope this blog post has helped you learn more about what is expected of a data analyst and how to prepare for an interview. Good luck!

 

 

Josh Fechter
Josh is the founder of The Product Company.