interview questions

13 most common Data Analysts interview questions you must prepare for
Ayesha Saleem
| October 24, 2022

Get hired as a Data Analyst by confidently responding to the most asked interview questions. No matter how qualified or experienced you are, if you stumble over your thoughts while answering the interviewer, it might take away some of your chances of getting onboard. 

 

data analyst interview question
Data analyst interview question – Data Science Dojo

In this blog, you will find the top data analysts interview questions covering both technical and non-technical areas of expertise.  

List of Data Analysts interview questions 

1. Share about your most successful/most challenging data analysis project? 

In this question, you can also share your strengths and weaknesses with the interviewer.   

When answering questions like these, data analysts must attempt to share both their strengths and weaknesses. How do you deal with challenges and how do you measure the success of a data project? You can discuss how you succeeded with your project and what made it successful.  

Take a look at the original job description to see if you can incorporate some of the requirements and skills listed. If you were asked the negative version of the question, be honest about what went wrong and what you would do differently in the future to fix the problem. Despite our human nature, mistakes are a part of life. What’s critical is your ability to learn from them. 

Further talk about any SAAS platforms, programming languages, and libraries. Why did you use them and how did you use them to accomplish yours?

Discuss the entire pipeline of your projects from collecting data, to turning it into valuable insights. Describe the ETL pipeline including data cleaning, data preprocessing, and exploratory data analysis. What were your learnings and what issues did you encounter and how did you deal with them. 

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2. Tell us about the largest data set you’ve worked with? Or What type of data you have worked with in the past? 

What they’re really asking: Can you handle large data sets?  

Data sets of varying sizes and compositions are becoming increasingly common in many businesses. Answering questions about data size and variety requires a thorough understanding of the type of data and its nature. What data sets did you handle? What types of data were present? 

It is not necessary that you should only mention a dataset you worked with at your job. But you can also share about varying sizes specifically large datasets you worked with as a part of a data analysis course, Bootcamp, certificate program, or degree. As you put together a portfolio, you may also complete some independent projects where you find and analyze a data set. All of this is valid material to build your answer.  

The more versatile your experience with datasets will be, the greater the chances there are of getting hired.  

Read more about several types of datasets here:

32 datasets to uplift your skills in data science

 

3. What is your process for cleaning data? 

The expected answer to this question will include details about: How you handle missing data, outliers, duplicate data, etc.?c.? 

Data analysts are widely responsible for data preparation, data cleansing, or data cleaning. Organizations expect data analysts to spend a significant amount of time preparing data for an employer. As you answer this question, share in detail with the employer why data cleaning is so important. 

In your answer, give a short description of what data cleaning is and why it’s important to the overall process. Then walk through the steps you typically take to clean a data set. 

 

4. Name some data analytics software you are familiar with. OR What data software have you used in the past? OR What data analytics software are you trained in? 

What they need to know: Do you have basic competency with common tools? How much training will you need? 

Before you appear for the interview, it’s a good time to look at the job listing to see what software was mentioned. As you answer this question, describe how you have used that software or something similar in the past. Show your knowledge of the tool by employing associated words.  

Mention software solutions you have used for a variety of data analysis phases. You don’t need to provide a lengthy explanation. What data analytics tools you used and for which purpose will satisfy the interviewer. 

  

5. What statistical methods have you used in data analysis? OR what is your knowledge of statistics? OR how have you used statistics in your work as a Data Analyst? 

What they’re really asking: Do you have basic statistical knowledge? 

Data analysts should have at least a rudimentary grasp of statistics and know-how that statistical analysis helps business goals. Organizations look for a sound knowledge of statistics in Data analysts to handle complex projects conveniently. If you used any statistical calculations in the past, be sure to mention it. If you haven’t yet, familiarize yourself with the following statistical concepts: 

  • Mean 
  • Standard deviation 
  • Variance
  • Regression 
  • Sample size 
  • Descriptive and inferential statistics 

While speaking of these, share information that you can derive from them. What knowledge can you gain about your dataset? 

Read these amazing 12 Data Analytics books to strengthen your knowledge

12 excellent Data Analytics books you should read in 2022

 

 

6. What scripting languages are you trained in? 

In order to be a data analyst, you will almost certainly need both SQL and a statistical programming language like R or Python. If you are already proficient in the programming language of your choice at the job interview, that’s fine. If not, you can demonstrate your enthusiasm for learning it.  

In addition to your current languages’ expertise, mention how you are developing your expertise in other languages. If there are any plans for completing a programming language course, highlight its details during the interview. 

To gain some extra points, do not hesitate to mention why and in which situations SQL is used, and why R and python are used. 

 

7. How can you handle missing values in a dataset? 

This is one of the most frequently asked data analyst interview questions, and the interviewer expects you to give a detailed answer here, and not just the name of the methods. There are four methods to handle missing values in a dataset. 

  • Listwise Deletion 

In the listwise deletion method, an entire record is excluded from analysis if any single value is missing. 

  • Average Imputation  

Take the average value of the other participants’ responses and fill in the missing value. 

  • Regression Substitution 

You can use multiple-regression analyses to estimate a missing value. 

  • Multiple Imputations 

It creates plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your predictions. 

 

8. What is Time Series analysis? 

Data analysts are responsible for analyzing data points collected at different intervals. While answering this question you also need to talk about the correlation between the data evident in time-series data. 

Watch this short video to learn in detail:

 

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

Profiling data attributes such as data type, frequency, and length, as well as their discrete values and value ranges, can provide valuable information on data attributes. It also assesses source data to understand its structure and quality through data collection and quality checks. 

On the other hand, data mining is a type of analytical process that identifies meaningful trends and relationships in raw data. This is typically done to predict future data. 

 

10. Explain the difference between R-Squared and Adjusted R-Squared.

The most vital difference between adjusted R-squared and R-squared is simply that adjusted R-squared considers and tests different independent variables against the model and R-squared does not. 

An R-squared value is an important statistic for comparing two variables. However, when examining the relationship between a single stock and the rest of the S&P500, it is important to use adjusted R-squared to determine any discrepancies in correlation. 

 

11. Explain univariate, bivariate, and multivariate analysis.

Bivariate analysis, which is simpler than univariate analysis, is used when the data set only has one variable and it does not involve causes or effects.  

Univariate analysis, which is more complicated than bivariate analysis, is used when the data set has two variables and researchers are looking to compare them.  

When the data set has two variables and researchers are investigating similarities between them, multivariate analysis is the right type of statistical approach. 

 

12. How would you go about measuring the business performance of our company, and what information do you think would be most important to consider?

Before appearing for an interview, make sure you study the company thoroughly and gain enough knowledge about it. It will leave an impression on the employer regarding your interest and enthusiasm to work with them. Also, in your answer you talk about the added value you will bring to the company by improving its business performance. 

 

13. What do you think are the three best qualities that great data analysts share?

List down some of the most critical qualities of a Data Analyst. This may include problem-solving, research, and attention to detail. Apart from these qualities, do not forget to mention soft skills which are necessary to communicate with team members and across the department.    

 

Did we miss any Data Analysts interview questions? 

Share with us in the comments below and help each other to ace the next data analyst job. 

  

Data Science Dojo
| September 28, 2019

This list of 101 top data science interview questions, answers, and key concepts was built to help you prepare and ace your interview.

In October 2012, the Harvard Business Review described “Data Scientist” as the “sexiest” job of the 21st century. Well, as we approach 2020 the description still holds true! The world needs more data scientists than are available for hire. All companies – from the smallest to the biggest – want to hire for a job role that has something “Data” in its name: “Data Scientists”, “Data Analysts”, “Data Engineers” etc.

On the other hand, there’s a large number of people who are trying to get a break in the Data Science industry, including people with considerable experience in other functional domains such as marketing, finance, insurance, and software engineering. You might have already invested in learning data science (maybe even at a Data Science Bootcamp), but how confident are you for your next Data Science interview?

This blog is intended to give you a nice tour of the questions asked in a Data Science interview. After thorough research, we have compiled a list of 101 actual data science interview questions that have been asked between 2016-2019 at some of the largest recruiters in the data science industry – Amazon, Microsoft, Facebook, Google, Netflix, and Expedia, etc.

If you want to know more regarding the tips and tricks for facing a data science interview, watch the AMA with some of our own Data Scientists.

Top categories for best data science interview questions

Data Science is an interdisciplinary field and sits at the intersection of computer science, statistics/mathematics, and domain knowledge. To be able to perform well, one needs to have a good foundation in not one but multiple fields, and it is reflected in the interview. We’ve divided the questions into 6 categories:

  • Machine Learning
  • Data Analysis
  • Statistics, Probability, and Mathematics
  • Programming
  • SQL
  • Experiential/Behavioral Questions

We’ve also provided brief answers and key concepts for each question. Once you’ve gone through all the questions, you’ll have a good understanding of how well you’re prepared for your next data science interview!

Machine learning and data science

As one will expect, data science interviews focus heavily on questions that help the company test your concepts, applications, and experience in machine learning. Each question included in this category has been recently asked in one or more actual data science interviews at companies such as Amazon, Google, Microsoft, etc. These questions will give you a good sense of what sub-topics appear more often than others. You should also pay close attention to the way these questions are phrased in an interview.

Data analysis

Machine learning concepts are not the only area in which you’ll be tested in the interview. Data pre-processing and data exploration are other areas where you can always expect a few questions. We’re grouping all such questions under this category. Data analysis is the process of evaluating data using analytical and statistical tools to discover useful insights. Once again, all these questions have been recently asked in one or more actual data science interviews at the companies listed above.

Statistics, Probability, and Mathematics

As we’ve already mentioned, data science builds its foundation on statistics and probability concepts. Having a strong foundation in statistics and probability concepts is a requirement for data science, and these topics are always brought up in data science interview questions. Here is a list of statistics and probability questions that have been asked in actual data science interviews.

 

Programming

When you appear for a data science interview, your interviewers are not expecting you to come up with a highly efficient code that takes the lowest resources on computer hardware and executes it quickly. However, they do expect you to be able to use R, Python, or SQL programming languages so that you can access the data sources and at least build prototypes for solutions.

You should expect a few programming/coding questions in your data science interviews. Your interviewer might want you to write a short piece of code on a whiteboard to assess how comfortable you are with coding, as well as get a feel for how many lines of codes, you typically write in a given week.

Here are some programming and coding questions that companies like Amazon, Google, and Microsoft have asked in their data science interviews.

Structured Query Language (SQL)

Real-world data is stored in databases and it ‘travels’ via queries. If there’s one language a data science professional must know, it’s SQL – or “Structured Query Language”. SQL is widely used across all job roles in data science and is often a deal-breaker. SQL questions are placed early on in the hiring process and used for screening. Here are some SQL questions that top companies have asked in their data science interviews.

Situational/Behavioral questions

Capabilities don’t necessarily guarantee performance. It’s for this reason employers ask you situational or behavioral questions in order to assess how you would perform in a given situation. In some cases, a situational or behavioral question would force you to reflect on how you behaved and performed in a past situation. A situational question can help interviewers in assessing your role in a project you might have included in your resume, can reveal whether or not you’re a team player, or how you deal with pressure and failure. Situational questions are no less important than any of the technical questions, and it will always help to do some homework beforehand. Recall your experience and be prepared!
Here are some situational/behavioral questions that large tech companies typically ask:

Thanks for reading! We hope this list is able to help you prepare and eventually ace the interview! If you’re still on the job hunt, check out our friends over at Jooble.

If you need help understanding the concepts above, check out Data Science Dojo’s online data science bootcamp!

Like the 101 machine learning algorithms blog post, the accordion drop-down lists are available for you to embed on your own site/blog post. Simply click the ’embed’ button in the lower left-hand corner, copy the iframe, and paste it within the page.

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