17 Data Analyst Interview Question You Need to Know

17 Data Analyst Interview Question You Need to Know

At a time when businesses all around the world are leveraging professionals in both data analytics and data science to work through their company data in order to offer actionable insights, more candidates are looking to take advantage of the surge in interest. That means they're working to ace the data analyst interview questions.

Needless to say, while their qualifications and resumés may make them appealing, employers know that where you really get a feel for your candidates is in the interview phase. 

If you had the slightest doubt as to what to ask an applicant who’s got their eyes set on the data analyst post advertised at your company, you can put them aside now. 

Now, I’m going to get into the top seventeen (17) questions as well as the types of responses that ought to seal the deal for you. 

Let’s dive right in. 

1. What are the steps of a data analysis project?

This question is a fundamental piece in your interview arsenal that you should wield the earliest opportunity you get. In essence, this question requires that the candidate describe the outline of how they would go about undertaking a data analysis project. Needless to say, the way in which a candidate answers this question is crucial as it tells you how knowledgeable they are about data management processes. You should give the green light to candidates to answer in this way:

  • The first step is to understand the business’ requirements. 
  • Then, the data analyst ought to extract the most useful data from the best data sources. 
  • Following this, one should explore datasets, sift through, and organize data in logical batches. 
  • Guarantee the validity of the data that has been analyzed. 
  • Put in place mechanisms to track datasets. 
  • Draw up a series of outcomes. 

2. How would you define ‘clustering’?

This is a technical question which a data analyst should not get wrong. Data clustering, as the name suggests, refers to the grouping of data into clusters. Each cluster’s algorithm is defined either as disruptive, iterative, flat, or hard. If the applicant cannot answer this question, then this is a serious red flag, especially if the role that is up for grabs is that of a senior data analyst. 

3. What are the best tools for data analysis?

As is customary, recruiters ought to ask interviewees what their thoughts are on the best tools in data analysis and data visualization. Not only is this a wonderful opportunity to identify what tools possible candidates are familiar with, but it’s also a swell way to figure out if the tools your company uses align with those that the candidate mentions. For this question, you should expect candidates to respond by throwing out names such as Tableau, Hadoop, NodeXL, RapidMiner, and Google Search Operators. 

4. Can you name the statistical methods that benefit data analysts greatly?

Continuing in the same trend of technical questions, this is a question that any decent data analyst ought to be able to answer without much difficulty. Correct responses will include any of the following: 

  • Imputation
  • Mathematical optimization
  • Rank statistics
  • Markov process
  • Bayesian method
  • Simplex algorithm
  • Spatial and cluster processes

Statistical analysis and statistical techniques are crucial in data analysis. Candidates who provide any response that does not vaguely resemble any of these answers ought to be discarded.

5. How would you characterize a solid data model?

This question serves to provide you as a recruiter with the means to identify how the candidate evaluates a batch of data. Undoubtedly, their problem-solving and assessment skills are crucial to the successful completion of their job. Therefore, a recommendable response from an applicant ought to take into account the need for predictive performances, its adaptability, scalability, and its ability to be understood with little to no effort on the part of those who access it. 

6. Can you think of any benefits of version control?

Yet another technical question, this interview inquiry is tough. In fact, this is the type of question I would ask if I were recruiting a senior data analyst. In any event, the principal advantages that you can expect from version control are:

  • Ideal for monitoring application builds. 
  • Help keep an easy-to-follow history of project files in case there is a central server shutdown. 
  • Allows one to save and store multiple code files in a safe and secure way. 
  • Simplifies the challenge of saving, comparing, and editing files on the go. 

7. What does collaborative filtering mean to you?

Collaborative filtering refers to an algorithm that generates a pattern based on how a company’s data has been behaving over a particular period of time. A prime example of this would be the ‘Recommendations’ tab that tends to follow most E-Commerce platforms. Any applicant who doesn’t know what collaborative filtering is ought to be trashed at the earliest. 

8. Is there such a thing as time-series analysis?

The focus of this question centers around the types of domains that one may use when conducting data analysis, namely time domain and frequency domain. The data analyst ought to know this. They also ought to know that time-series analysis can be defined as a procedure through which the resulting forecast of a given process is marked through data collection and analysis. One may even go as far as to add the techniques used to compile data, such as the log-linear regression method and exponential smoothing. 

9. How do you distinguish between data mining and data profiling?

Data profiling refers to the discipline of analyzing a computer’s length, frequency, and other software dialects. On the flip side, data mining makes references to analyzing data clusters, sequence discovery, and spotting uncommon records, to name a few. This is crucial for any data analyst to know, irrespective of how junior or senior their position may be. 

10. What is an N-Gram? 

Once again, this is a tricky technical question that will require a bit of explaining on the part of the applicant. In this case, the response you are looking for is one that defines N-Gram as a sequence containing n items that have been joined in a speech. As a result, data analysts leverage an N-Gram probabilistic language to forecast the next item that may or may not crop up in a batch of data. 

11. What validation methods do data analysts use?

Data analysts have multiple means to practice data validation for batches of data. Here’s a breakdown of them: 

  • Field level navigation: Here, the data analyst conducts a validation check in each field when someone enters data.
  • Form level navigation: With form-level navigation, the data analysts verify the data (a process known as data verification) once it has been checked and validates all the fields that have been populated. 
  • Data saving validation: Data saving validation is a technique used by analysts who want to secure their files and database in the safest way possible. 
  • Search client validation: This seeks to offer validation to the analyst in virtue of the increasing

12.  Can you define an outlier?

If you do not ask this question at some point or another during the data analyst interview, you have done yourself a great disservice. Analysts ought to know that an outlier simply refers to a value that appears uncharacteristic and out-of-place when compared to other values within a batch of data. Applicants may go as far as to state that there are two main types of outlier: univariate and multivariate respectively. While candidates may highlight the methods in which outliers can be defined with the finest precision, unless the role you are advising is a senior data analyst position, you shouldn’t be so strict with regard to a candidate’s knowledge of the answer for this question. 

13. What should a data analyst do when confronted with incomplete data?

Needless to say, this question is one that tests the integrity and moral fiber of your candidate. Here, data analysts need to follow strict guidelines that include the following: 

  • Follow data analysis strategies that include single imputation methods, model-based methods, or deletion methods.
  • Generate a validation report that takes into account the data that you suspect has been miscommunicated or erased. 
  • Examine and analyze the data to verify whether it has in fact been tampered with or modified. 
  • Substitute the coding correspondent to the invalid or void data with admission validation coding.

14. How would you define ‘data cleaning’?

Undoubtedly, this is one of the most common questions that recruiters tend to ask in data analyst interviews. For this reason, it is imperative that the candidate answer this question correctly. 

Otherwise, it shows not only their low-level of preparedness but also their obvious lack of interest in conducting meaningful research that can impact positively on a job interview, something which is supposed to be an important moment in their life. 

The correct answer to this question would be any response that defines data cleansing as a practice that spots and removes any type of error, misrepresentation, inaccuracy, or blunder that may crop up in data with the sole purpose of boosting data quality. 

15. What do you think it takes to become a data analyst?

This is a common question that every data analyst ought to be able to answer confidently. After all, it makes reference, albeit directly, to why the candidate feels that they have the right credentials and profile to apply for a data analyst role. In such cases, candidates may provide a diverse range of responses that include the following: 

  • The need to have mastery over certain programming languages like SQL, DB2, and Python)
  • Understanding of how report generation tools like HubSpot Marketing Analytics Software work
  • Confidence in the ability to sift through, evaluate, and organize Big Data in a fast and efficient manner. 
  • Knowledgeable about statistical packages that analyze massive datasets. These packages include programs such as Microsoft Excel, SPSS, and SAS, among others. 

A combination of these responses above would be a valid way to answer this question. 

16. What would you consider to be three of the most important responsibilities that a data analyst has to bear?

This is a question that forces the candidate to reflect on their own thought process and prioritize the activities that they conduct as data analysts. Usually, the most ideal responses rank the top three responsibilities as report generation, pattern recognition, and data collection and administration. However, if a candidate provides a different response and is able to offer a convincing argument to back it up, then by all means they deserve extra points for their effort. 

17. What does ‘KNN imputation method’ mean to you?

This is yet another technical question that you can add to the list of ones that you ought to ask a data analyst when they have applied for a position at your company. 

Essentially, this question seeks to test the extent to which the candidate can be considered knowledgeable about highly specific areas within data analysis management, namely the manner in which they represent raw data values. This method refers to the way in which missing values for missing data attributes are discovered and assigned. 

Data Analyst Interview Questions: Key Takeaways

In short, guaranteeing the successful recruitment of a data analyst that matches your company's needs and work profile hinges heavily on the quality of the interview that you conduct. 

It is important to know what questions will help you to easily identify how competent an applicant is in a given field and, in turn, give you a better indicator as to how ideal they are for the post.

The questions included in this blog post are carefully selected based on criteria that evaluate not only the technical capabilities of an applicant but also their analytical skills and ability to rationalize. 

All in all, irrespective of the templated questions and answers sections that I have provided, the onus is on you as the interviewer to make a fair and concise assessment of the quality of the candidates you interview for a data analyst job in order to determine whether they have the right skill set, communication skills, and business intelligence for your company.


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