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28 Data Analyst Interview Questions to Help You Prepare

Data Analyst Interview Questions Answers

Data makes the world go round, and today’s job market is flooded with openings in data-related careers. That’s unsurprising since many positions directly relate to processing and using data, focusing on disciplines such as data science, data mining, data analysis, and data analytics. Today, we’re discussing data analysts.

This article highlights the current top data analyst interview questions. Our list is broken down into general questions, data analyst technical interview questions, and other questions relating to a data analyst job, covering tools such as Excel and SQL. We’ll also share a way for you to get data analytics training which would be instrumental in preparing you for an interview.

But before we get into the questions, let’s examine what an experienced data analyst does and the difference between the disciplines of data analysis, data analytics, and data science.

What’s a Data Analyst?

Data analysts pull meaningful insights from data, finding answers to specific questions. Data analytics is a broad field that involves using data and related tools to arrive at intelligent business decisions. Data analysis, on the other hand, is a subset of data analytics that covers specific processes, turning information gained from sources such as big data into something useful. This process includes dealing with suspicious or missing data.

To sum it up, data analytics is used by data analysts, data scientists, and data engineers to discover, interpret, visualize, and tell a story about data patterns to drive business strategy and outcomes. Data analytics focuses on finding trends, uncovering opportunities, making decisions, and predicting events, actions, and triggers. These responsibilities include data profiling and data validation.

Meanwhile, data analysis involves cleaning, inspecting, modeling, transforming, and questioning data to locate helpful information. Data analysis covers giving other people access to the data they need, using storytelling or visualization to present the data, and making recommendations based on the data. So data analysis gets the data set up and hands it off to whoever performs data analytics.

Now that we have that squared away let’s dive into our data analyst interview questions. We begin with a dozen general knowledge interview questions for data analysts.

Also Read: How Much is the Typical Data Analytics Salary in 2023? A Complete Guide

General Data Analyst Interview Questions

Q: What do you see as the most critical responsibilities of a data analyst?

A: Data analysts perform these tasks:

  • Collecting and interpreting data from multiple sources and analyzing the results
  • Filtering and cleaning data gathered from these multiple sources
  • Implementing data visualization tools and skills to provide comprehensive results
  • Analyzing complex datasets and identifying their hidden patterns
  • Data preparation
  • Keeping databases secure

Q: What essential requirements make a good data analyst?

A: The ideal data analyst meets the following requirements:

  • Be familiar with programming languages (e.g., JavaScript, XML, or ETL frameworks), databases (SQL, SQLite, Db2, etc.), and have extensive knowledge of reporting packages such as business objects
  • Possess strong knowledge of statistical packages designed to analyze massive data sets (e.g., SAS, Excel, and SPSS)
  • Have robust technical knowledge in database design, data mining, and segmentation techniques
  • Be proficient in using data visualization tools
  • Know how to extract and prepare data
  • Generate and prepare reports

Q: What does data cleansing mean?

A: Data cleansing mainly involves detecting and removing inconsistencies and errors from unstructured data to improve data quality.

Q: What functions are associated with data cleansing?

A: Data cleansing operations include:

  • Segregating and organizing data according to its respective attributes
  • Breaking large groups of data into smaller bits and cleaning them
  • Analyzing each data column’s statistics
  • Creating scripts or utility functions to streamline everyday data cleaning tasks
  • Recording every data cleansing operation to allow more straightforward addition or subtraction from data sets

Q: What is data wrangling?

A: Data wrangling is the process that cleans, structures, and enriches raw data into the needed usable format for making better decisions. Wrangling involves discovering, cleaning, structuring, enriching, validating, and subsequently analyzing data.

Q: How do the methods of data cleaning and data wrangling differ?

A: Trick question! Data cleaning and data wrangling are the same things! But here are the accepted methods of data cleaning (or wrangling).

  • Removing a whole data block
  • Replacing data with its appropriate median or mean values
  • Utilizing placeholders for empty spaces
  • Looking for ways to fill in black data without creating redundancies

Q: What are some of the roadblocks often associated with data analysis?

A: Typical data analyst issues include:

  • Handling duplication and spelling errors
  • Handling data purging and data storage challenges
  • Knowing how to collect the correct, meaningful data at the right time
  • Ensuring data security and compliance
  • Dealing with incomplete data
  • Poor data sources, which result in wasted time
  • Dealing with the unrealistic timelines and expectations of stakeholders and management

Q: List some of the most common tools for data analysis and what they’re used for.

A: Common tools include:

  • MS SQL Server, MySQL, for working with data stored in relational databases
  • MS Excel, and Tableau, to create reports and dashboards
  • Python, R, and SPSS to perform statistical analysis, data modeling, and exploratory analysis
  • MS PowerPoint to present and display the final results and essential conclusions.

Q: What’s the difference between data analysis and data mining?

A: Data analysis involves cleaning, organizing, and using the data to produce meaningful insights, while data mining is used to find hidden patterns in the data.

Q: List the most common validation methods used by data analysts.

A: There are four chief data validation methods:

  • Field level validation. This method validates the data when and as it enters the field, and errors are corrected as they appear.
  • Form level validation. Analysts use this method are users submit the form. The data entry form is immediately checked, each field is validated, and errors are highlighted for the user to fix.
  • Data saving validation. This validation method is used when files or database records are saved and typically when there are multiple data entry forms to validate.
  • Search criteria validation. This method validates the user’s search criteria to provide the user with accurate and related results. Search criteria validation’s primary purpose is to ensure that search results returned by the user’s query are highly relevant.

Q: Explain outliers in the context of data analysis, and how to find them.

A: In data sets, outliers are values that differ significantly from the mean of a data set’s characteristic features. Analysts can determine either the variability in measurement or experimental errors by using an outlier. There are two kinds of outliers: univariate and multivariate.

Q: Okay, so you’re a data analyst, and you discover some missing values or suspect data. What steps do you take?

A: Take the following steps:

  • Detect missing data using data analysis strategies such as deletion, single imputation, and model-based methods
  • Generate a comprehensive validation report detailing the suspected or missing data
  • Closely examine the suspicious data to assess its validity
  • Replace any invalid data with a proper validation code
  • Perform model preparation for the missing data
  • Predict any missing values

Now let’s move on to specific areas, beginning with statistical methods.

Also Read: What is Data Analytics: Types, Roles, and Techniques

Data Analyst Interview Questions on Statistics

Q: Explain how you handle missing dataset values.

A: There are four methods to handle missing values.

  • Average Imputation. Fill in the missing value with the average value of other participants’ responses.
  • Listwise Deletion. The whole record is excluded from the analysis.
  • Multiple Imputations. Create plausible values for the missing data based on the correlations, then average the simulated datasets by factoring in random errors in the predictions.
  • Regression Substitution. Use multiple-regression analyses to estimate the missing value.

Q: We already covered how to find outliers, but now, explain how you handle them.

A: There are four ways you can deal with outliers.

  • Assign a new value
  • Cap your outlier’s data
  • Drop the outlier records
  • Try a new transformation

Q: What are Type I and Type II errors in the context of statistics?

A: A Type I error, also known as a false positive, occurs when the null hypothesis gets rejected even if it’s true. A Type II error, alternately called a false negative, occurs when the null hypothesis isn’t rejected, even if it’s false.

Q: What are the two primary forms of hypothesis testing?

A: The two primary forms are:

  • The Alternative Hypothesis. This hypothesis, denoted by H1, states that there’s some relation between the population’s predictor and outcome variables.
  • The Null Hypothesis. The Null Hypothesis, denoted by H0, says there is no relation between the population’s predictor and outcome variables.

Time to leave statistics and check out Excel interview questions for data analysts.

Excel Data Analyst Interview Questions

Q: What’s the difference between COUNT, COUNTA, COUNTBLANK, and COUNTIF?

A: These are the differences:

  • The COUNT function returns the numeric cell count in a range
  • The COUNTA function counts the non-blank cells in a range
  • The COUNTBLANK function gives the count of blank cells in a range
  • The COUNTIF function returns the value count by checking a given condition

Q: A numeric value can be treated as a text value if preceded by what?

A: An apostrophe.

Q: How do you make a dropdown list?

A: Here’s how to make a dropdown list using Excel.

  • First, click on the Data tab in the ribbon
  • Next, select Data Validation under the Data Tools group
  • Then, navigate to Settings > Allow > List
  • Finally, select the source you wish to provide as the list array

Q: Can you provide a dynamic range in “Data Source” for a Pivot table, and if so, how?

A: The answer is yes. To provide a dynamic range, create a named range using the offset function, then base the pivot table employing a named range constructed in the initial step.

Now let’s review some SQL interview questions for data analysts.

SQL Interview Questions for Data Analysts

Q: Explain the difference between a WHERE clause and a HAVING clause.

A: There are three notable differences:

  • The WHERE clause operates on row data, whereas the HAVING clause works on aggregated data
  • You can’t use aggregated functions in WHERE clauses, but you can in HAVING clauses
  • Filters occur before any groupings are made in the WHERE clause while HAVING clauses are used to filter values from the group

Q: Write out the syntax of WHERE and HAVING clauses.

A: Here’s the syntax:

  • Syntax of WHERE clause:
  • SELECT column1, column2, …
  • FROM table_name
  • WHERE condition;
  • Syntax of HAVING clause;
  • SELECT column_name(s)
  • FROM table_name
  • WHERE condition
  • GROUP BY column_name(s)
  • HAVING condition
  • ORDER BY column_name(s);

Q: What is a subquery?

A: In the context of SQL, a subquery is a query situated in another query. Also known as an inner or nested query, they are used to enhance data queried by the main query. It comes in two types: Correlated and Non-Correlated Queries. Here’s an example where you get the name, e-mail address, and telephone number of an employee in Massachusetts:

  • SELECT name, e-mail, phone
  • FROM employee
  • WHERE emp_id IN (
  • SELECT emp_id
  • FROM employee
  • WHERE city = ‘Massachusetts’);

Q: Describe the steps for writing a stored procedure.

A: There are five steps:

  • Create a procedure by assigning it a name
  • Declare the variables
  • Use a set statement to write the formula
  • Print the computed variable’s values
  • Finally, use the EXEC command to run the stored procedure

Let’s move along to some tableau data analyst interview questions.

Also Read: The UI UX Design Process: Everything You Need to Know

Tableau Data Analyst Interview Questions

Q: What’s a Gantt chart in Tableau?

A: A Gantt chart consists of bars with a time axis and is primarily a project management tool. Each bar is a measure of a task within a project. The chart depicts the progress of value over a given period. For instance, it could show the duration of specific events. It consists of bars along with the time axis.

Q: What’s LOD in Tableau?

A: LOD stands for Level of Detail, an expression used to execute complex queries that involve many dimensions at the data sourcing level. You can find duplicate values using LOD expression, synchronizing chart axes, and creating bins on the aggregated data.

Q: Explain the feature selection process and why it’s essential for data analysis.

A: Feature selection selects a subset of relevant features derived from a larger set of dataset variables or predictors. The process tries to improve model performance, enhance interpretability, reduce overfitting, and optimize computational efficiency. Here are the reasons why feature selection matters for data analysis:

  • It improves the model’s performance by selecting the most relevant features
  • It prevents overfitting by reducing the risk of redundant or irrelevant features
  • It offers interpretability and insights by providing a smaller set of selected features
  • It boosts computational efficiency by working with a smaller feature set

Q: Explain the difference between data joining and blending.

A: You can conduct data joining only when the data comes from the same source, while data blending uses data from two or more sources. In addition, all the combined tables and sheets used in data joining have a common set of measures and dimensions, while each data source uses its own set of measures and dimensions when you use data blending.

Do You Want Solid Training in Data Analytics?

Data analytics is a fascinating and challenging field that offers plenty of opportunities for people who want to work in the field. However, before you get to the interview stage, you should boost your data analyst skill set by attending a data analytics bootcamp. This highly interactive, instructor-led bootcamp gives you six months of applied learning, multiple labs, and hands-on projects designed to give you a solid understanding of data analytics.

According to the job website, data analysts in the United States can earn an annual average of $75,027, going as high as over $114,000. So, get that exciting new data analyst career off the ground today and sign up for this valuable bootcamp, and take your place as a qualified data professional.

You might also like to read:

Top Data Scientist Skills You Must Have

How to Become a Data Scientist in 2023?

Data Scientist vs. Data Analyst – The Differences Explained

What Does a Full Stack Web Developer Salary Look Like?

What are Today’s Top Ten AI Technologies?

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