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10 Frequently Asked Data Analytics Interview Questions

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Are you applying for a position as a data analyst? Or are you a hiring manager for one of these positions? Either way, this is a post that could be important for you. This is because we offer here ten questions interviewers frequently ask during data analytics interviews. Additionally, we provide insights on each question.


The era we live in is characterized by a massive generation and flow of data. Data, after being transformed, is given meaning and context to become information. Information, in turn, becomes useful knowledge for decision-making.

Data is critical to the operation of any organization. In fact, it has become indispensable in solving business problems and exploring opportunities. Therefore, data analytics, the process of analyzing raw data to extract valuable information, is important to businesses in several ways. For example:

  • It helps managers and business owners make better, more informed strategic decisions.
  • Data analytics helps identify problems.
  • It helps to establish and/or improve business processes for more efficient operations and better productivity.
  • Businesses use data analysis to build customer relations. It helps marketers and business owners better understand consumer trends and behavior. In turn, this helps them to design products that meet consumers’ needs.
  • Data analytics also helps businesses better understand the market and their competition.


However, to make the most of data, a business needs a qualified data analyst. This is someone who has studied a data analytics course. Further, this person has gained skills and practical experience by working on data analytics projects.

Basically, given the place of data in organizations, the demand for seasoned data analysts, engineers, and scientists continues to rise.

Frequently Asked Data Analytics Interview Questions

Part of getting your dream job in this field is acing the interview. But interviews can make you anxious. Nonetheless, with good preparation, you should have nothing to worry about.

Be sure to prepare a comprehensive portfolio about projects you have handled before you go to the interview. Also, make sure you have the skills a data analyst should have. In addition, be sure you have a good understanding of the more popular analytics tools.

To help you prepare for your next interview, here are 10 data analytics questions an interviewer might ask you, along with information about each one.

1. What is data sampling and what are the steps sampling involves?

Data sampling is a technique data analysts use in statistical analysis. They use this technique to select, manipulate, and analyze a representative subset of a population. This allows them to get information about the larger population. To select a data sample, you need to:

  • Identify and define your target population.
  • Develop a sampling frame.
  • Select a sampling method.
  • Decide which sample size you will use.
  • Collect data for your sample.

2. What is data cleansing? What are the best data cleansing practices?

When data is collected and put together from different sources, the data set is bound to have duplicate, incorrect, incomplete, mislabeled, or corrupted data. Data cleansing is the process by which analysts identify such data and either fix or remove it. This process improves the quality of the data set.

Data cleaning follows five best practices, including: 

  • Come up with a data quality plan to help you set expectations. Include the KPI’s and standard operating procedures you will use to track, measure, and ensure data quality. This helps you identify and address the causes of inconsistencies in your data set.
  • Convert all data into the same standard format before feeding it into the system. Standardizing data at the point of entry makes the data cleaning process more manageable. This is because you will have fewer errors to deal with. For instance, ensure that all numerical values have a standard measurement unit, such as meters or kilograms. 
  • Verify/validate data accuracy. This is an important step to re-check the accuracy of data. Sometimes you can do this in real time. This step ensures that data conforms to the rules and constraints you have put in place.
  • Identify and eliminate duplicates from your data set. This is because duplicate data prevents effective data analysis. Importantly, it is one of the main causes of bad reporting. The presence of duplicate data in a data set means that the data set is inaccurate and unhealthy.
  • Completeness of a data set means that you have all the data you require for analysis and business intelligence. You ensure completeness of a data set by reviewing the source. Then you can find and fill in the missing data.


3. Explain the data validation methods used in data analytics

  • Field level validation, as the term implies, happens in each field as the user makes entries. In essence, the user corrects errors as they make entries. For instance, the user is careful not to enter a numeric value in the name field.
  • Form level validation ensures that all mandatory fields in an online form are filled before the user can save and submit a form. If any errors are highlighted in the form, the user will not be able to submit until they are corrected. 
  • Data saving validation is commonly used where there are multiple data entry forms. In this method, the user is allowed to first save the current records before leaving the page. 
  • The search criteria validation technique is used where the user enters multiple search criteria in a form. Then, it allows the user to save the search. Next, relevant results that match the search terms are returned. 
  • Range validation is mostly used for numeric values where the values entered fall within the specified range.

4. What is the difference between data mining and data profiling?

Data mining, also known as knowledge discovery, refers to the process of analyzing clusters, discovering hidden patterns, correlations, and sequences in data sets. Analysts use data mining to predict outcomes.

Unlike data mining, which focuses on data clusters, data profiling focuses on analyzing individual attributes of data sets. Analysts use data profiling to establish data quality and consistency. Profiling also helps users to understand the data they are using in their projects. Additionally, it helps them to extract useful knowledge from the attributes they are analyzing. Then, if there are quality problems during profiling, they know to conduct more data cleansing.

5. Explain univariate, bivariate, and multivariate analysis

These are statistical analysis techniques.

Univariate analysis is a statistical analysis technique in which the data has only one variable. The purpose of univariate analysis is to describe data and find hidden patterns in it.

Bivariate analysis analyzes two variables, such as X and Y, to determine the empirical relationship between them. It will find out if there is an association between two variables. Additionally, it allows the analyst to determine how strong the association is.

Multivariate analysis determines the relationship between more than two variables, usually a dependent variable and multiple independent variables.

6. What is KNN imputation and why is it used to determine missing numbers? 

KNN, refers to the K-nearest neighbor algorithm. This is a data mining algorithm analysts use to match a point they call “k” with its closest neighbors. They use this technique for pattern recognition and statistical estimation in classification and regression analyses.

KNN works on the principle that similar data points will be closer to each other. It determines missing numbers and calculates them using the distance function.

7. How does a data analyst address missing or corrupt values? 

An analyst has several options in dealing with such values. For instance, these include:

  • Detect the missing/corrupt values using techniques like single imputation and deletion.
  • Develop a validation report to give information about the missing values.
  • Examine the corrupt values and determine their validity.
  • Replace the missing or corrupt values with a validation code.

8. Name popular statistical techniques used for data analysis

  • Simplex algorithm
  • Imputation
  • Bayesian method
  • Spatial and cluster techniques
  • Outliers detection

9. What is the difference between variance and covariance?

Variance and covariance are two statistical analysis techniques. Variance measures the dispersion of data around its mean value. Covariance, on the other hand, is a measure of the relationship between two random variables as they change together.

10. What is “normal distribution”?  

Also known as Bell or Gaussian curve, normal distribution is a probability function. It describes how values of a variable are distributed. The distribution is usually symmetric and will indicate how values differ in means and standard deviations.

Use This Guide to Help You Study for Your Data Analytics Interview

There are many questions an interviewer might ask in your interview for a data analyst position. However, the questions they ask will depend on several factors. For instance, they will ask different questions depending on the job level you’re applying for, whether entry, intermediate, or advanced level. Their questions will also vary with the industry and your experience.

Still, during the interview, an interviewer is likely to ask you one, two, or several of the questions we have listed in this article. So take your time, use this article, and prepare well. This is because your answers to these questions will be the first thing prospective employers will evaluate as they conduct the interview.