30 Important Data Analyst Interview Questions And Answers


A profession in data analytics is not only enjoyable but also educational and lucrative. Companies all over the world have spent billions of dollars in researching and developing this sector. As a result, we find several highly paid-workers in this field worldwide. However, this brings rivalry. Hence, we’ve compiled a list of the Top 30 Data Analyst interview questions to help you get an edge over your competition. These questions will provide you with a detailed understanding of the type of Data Analyst interview questions often asked in Data Analyst interviews, allowing you to ace them

  1. Basic Data Analyst Interview Questions
  2. Advanced Data Analyst Role Interview Questions
  3. Excel Data Analyst Interview Questions and Answers

1. Basic Data Analyst Interview Questions

Basic data analyst interview questions are the most sought-after questions among interviewers. Here are the top questions you should understand. You will find the 15 most important data analyst interview questions in this section.

  • How can the terms Data Analysis and Data Mining be distinguished?
Data Mining Data Analysis
A hypothesis is not normally needed for this process. The process is often initiated by a query or assumption.
The process is predicated on well-maintained and organized evidence. This process entails data cleaning or data structuring in a proper format.
The results of the data mining process are difficult to understand. A data analyst can analyze findings and communicate them to stakeholders easily.
Equations can be derived easily using data mining algorithms. Only data analysts are responsible for generating equations.
  • Define “Data Wrangling” in the context of data analytics.

Data Wrangling is the method of cleaning, structuring, and enriching raw data to make it available for improved decision making. It entails data discovery, structuring, cleaning, enrichment, validation, and analysis. This method can transform and map vast volumes of data derived from different sources into a more usable format. To evaluate the results, techniques such as combining, grouping, concatenating, joining, and sorting are used. It can then be used for another dataset.

  • What are some of the best data cleaning practices?

Some of the best data cleaning practices are as follows:

  1. Sorting data based on various attributes.
  2. For big datasets, cleanse them step by step and boost the data for each step until it is of high quality.
  3. Divide massive databases into tiny data sets. Working with fewer data can accelerate the pace of the iterations.
  4. Build a series of utility functions/tools/scripts to perform basic cleaning tasks. It could be remapping values based on a CSV file or SQL table, or it could be regex search-and-replace, blanking out any values that do not fit a regex.
  5. If you have a concern about data cleanliness, organize them by estimated frequency and focus on the most popular issues.
  6. Examine each column’s summary statistics (standard deviation, mean, number of missing values)
  7. Keep a record of any data cleaning procedure so that you can make modifications or delete operations if required.
  • What are the requirements for a good data model?

Given below are the conditions for a good data model:

  1. A strong data model is simple to absorb.
  2. It results in predictable success.
  3. It is capable of adapting to improvements in its specifications.
  4. Massive improvements in data must be robust with a successful model.
  • What are the advantages of using version control?

There are several advantages of using version control:

  1. Creates a simple method for comparing data, identifying gaps, and merging if any improvements are made.
  2. Uses a clear method for tracking the life cycle of an application construct, covering all stages such as construction, manufacturing, testing, and so on.
  3. Creates a healthy environment for establishing a shared work culture.
  4. Ensures that all code versions and variants are kept safe and secure.

This is one of the top data analyst interview questions for freshers that you should be familiar with.

  • How does PROC SQL operate?

PROC SQL is a process that runs all of the observations at the same time. When a PROC SQL is run, the following events occur:

  1. SAS scans every statement and tests for syntax errors.
  2. If any tables are specified in the FROM declaration, they are put into the data engine and can be accessed in memory.
  3. The SQL optimizer examines the question contained inside the sentence. It determines how to perform the SQL query to limit runtime.
  4. Calculation and codes are carried out.
  5. The Final Table is stored in memory and routed to the SQL statement’s output table.
  • What tools are used for data analysis?

The following tools are used for data analysis:

  1. Tableau
  2. Google Search Operators
  3. RapidMiner
  4. KNIME
  5. Solver
  6. Open Refine
  7. NodeXL
  8. Wolfram Alpha’s
  9. io
  10. Google Fusion tables
  • What exactly is imputation? What are the various styles of imputation techniques?

We use substituted values to overwrite missing data during imputation. The following imputation methods are used:

Single Imputation

Hot-deck imputation: Using a punch card, a missing value is imputed from a randomly chosen identical record.

Cold deck imputation: It is similar to hot deck imputation, but it is more advanced and extracts donors from other datasets.

Mean imputation: In all such instances, the absent meaning is replaced with the variable’s mean.

Imputation regression: This is the process of replacing missing values with expected values of a variable dependent on other variables.

Stochastic regression: It is similar to regression imputation, but it includes the average regression variance.

Multiple Imputation

Multiple imputations: As opposed to single imputation, multiple imputations estimate the values multiple times.

  • What is A/B testing?

A/B testing is the statistical theory testing for randomized research with two variables, A and B. It is also known as split testing. It is an observational approach that uses sampling statistics to determine population parameters. This test links two web pages by showing two variations, A and B, to an equal amount of users, and the model with the higher conversion rate wins. The aim of A/B Testing is to determine whether the web page has changed.

Note: This is one of the most frequently asked data analyst interview questions.

  • What distinguishes Overfitting from Underfitting?
Overfitting Underfitting
Using the training set, the model effectively trains the data. In this case, the model neither trains well nor generalizes new data.
Over the course of the test series, consistency suffers significantly. Performs badly on both the train and test sets.
This occurs as the model thoroughly learns the spontaneous variations and noise in the training dataset. This occurs where there is insufficient data to construct a reliable model and when attempting to create a linear model from non-linear data.
  • What exactly is correlogram analysis?

In geography, the most popular form of spatial analysis is correlogram analysis. It is made up of a collection of approximate autocorrelation coefficients measured for each spatial association. When the raw data is represented as distance rather than values at individual points, it can be used to create a correlogram for distance-based data.

  • What do you know about stories in Tableau?

Stories describe a series of incidents or provide a company use case. The dashboard offers many choices to create a story. All the story points may be focused on a different perspective or dashboard, or the whole story could be based on the same idea.

To build a story in Tableau:

  1. Select the New Story option.
  2. Select a size for your story. You can select one of the predefined sizes or enter a custom size in pixels.
  3. Double-click on the title to edit it. You may also adjust the size, colour, and orientation of your title. To see the updates, click Apply.
  4. Drag a sheet from the Story tab on the left into the middle of the view to begin constructing your story.
  5. Click add a caption to summarize the main aspect of the story.
  6. To emphasize the key point, change a filter or sort, then save by pressing the Update button near the navigator box.
  • What is the fundamental distinction between Principal Component Analysis (PCA) and Factor Analysis (FA)?

The main distinction between PCA and FA is that factor analysis is used to specify and deal with the variation between factors, while PCA is used to describe the covariance between the existing components or variables.

  • Explain KPI, the 80/20 rule, and experiment design in a nutshell.

KPI stands for Key Performance Indicator. It is made up of several combinations of reports, spreadsheets, and charts about the whole business operation.

The 80/20 Rule states that you earn 80% of your money from 20% of your customers.

The creation of experiments – This is the first step in splitting your data, setting up the experiment, and collecting a sample of data for statistical analysis.

  • What is the distinction between SQL’s NVL and NVL2 functions?

NVL(exp1, exp2) and NVL2(exp1, exp2, exp3) are functions that determine whether the value of exp1 is null.

If we use the NVL(exp1,exp2) function, the value of exp1 will be returned if it is not null; otherwise, the value of exp2 will be returned. However, exp2 would have the same data type as exp1.

Similarly, if we use the NVL2(exp1, exp2, exp3) function, and if exp1 is not empty, the value of exp2 is returned; if not, the value of exp3 is returned.

Next on this list of top Data Analyst interview questions and answers, let’s look at some of the more advanced data analyst interview questions.

2. Advanced Data Analyst Role Interview Questions

In this section, the top 10 advanced data analytics questions are answered. You can get an understanding of these data analyst interview questions for your interview preparation.

  • Why do you feel you are the best candidate for the Data Analyst position?

The interviewer is attempting to gauge your level of comprehension of the work description and where you are coming from in terms of Data Analysis experience with this question. Be sure to respond in a succinct and informative manner by outlining your priorities, objectives, and dreams and how they align with the business substructure.

  • What are the typical problems that a data analyst faces?

Among the data analyst interview questions, obstacles encountered is a sure-fire challenge posed by the interviewer. Here are a few examples of challenges:

  1. Illegal values
  2. Redundant entries
  3. Identifying data that is overlapping
  4. Regular misspelling
  5. Irregular value misrepresentation

Note: data analyst interview questions may be asked in a variety of ways. There are data analyst interview questions for newcomers and data analyst interview questions for seasoned candidates. Make sure you are thoroughly trained for the ones that pertain to your current scenario.

  • How can you handle suspicious or missing data in a dataset while conducting data analysis?

If there are any inconsistencies in the data, the user may use any of the following methods:

  1. Creating a validation report with information regarding the data under consideration.
  2. Escalating the same to a seasoned Data Analyst to take a call.
  3. Replacing invalid data with matching correct and up-to-date data.
  4. Using several methods to locate missing values and relying on approximation if necessary.
  • What exactly is the ACID property in a database?

The acronym ACID stands for Atomicity, Consistency, Isolation, and Durability. This property is used in databases to determine whether data transfers are reliably stored in the system. 

  1. Atomicity: Refers to transactions that are either entirely efficient or completely unsuccessful. A transaction in this context corresponds to a particular process. As a result, if a specific operation fails, the whole transaction fails, leaving the database state unchanged.
  1. Consistency: This attribute ensures that the data follows all the validation rules. As a result, this ensures that the transaction never exits the database without finishing its state.
  1. Isolation: Isolation leaves transactions apart before they are completed. So, in essence, each transaction is distinct.
  1. Durability: Durability assures that your committed transaction is never lost. As a result, this ensures that the database can maintain track of pending updates in such a manner that the server will rebound from an abnormal termination or if there is a power outage, malfunction, or some other kind of mistake.
  • What are the different forms of Hypothesis Testing used today?

There are many kinds of hypothesis testing. Here are a few examples:

ANOVA: In this case, the comparison is performed between the mean values of different classes.

T-test: Where the standard variance is unknown, and the sample size is small, this type of research is used.

Chi-square test: This form of hypothesis testing is used to determine the degree of correlation between categorical variables in a sample.

Note: This is one of the most frequently asked data analysis questions.

3. Excel Data Analyst Interview Questions and Answers

Here are some of the most commonly asked data analytics questions in Excel, considered useful data analyst interview questions for freshers. 

  • What do you understand by Solver?

Solver is an Excel add-in that allows you to find the best solution when there are many variables and constraints. You can think of it as a more advanced version of Goal Seek. You can specify the constraints and the goal you need to achieve with Solver. It performs the calculation in the background to provide you with a possible solution.

  • What is an Excel macro, and why do we need one?

A macro in Excel is a program or set of written or recorded instructions to perform a specific task. It enables the user to automate some repetitive and time-consuming tasks performed regularly. 

  • What are the various data formats available in Excel?

Excel supports the following formats:

Text Format – This includes both text and alphanumeric strings (such as ABC123). Punctuation and symbols can also be included in a text string.

Number Format – Different formats exist even within numbers. You can have decimals, fractions, thousand separators, and so on. Numbers can be used in calculations such as addition, subtraction, division, and multiplication regardless of the format used.

Date Format – The most important thing to remember about dates is that they are stored in Excel as numbers. You can, however, format it so that it appears as a date. E.g., 01-01-2019 will be saved in Excel as 43466, which is the date’s number. Dates in Excel can be shown in several ways, including long dates (01 January 2019), short date color (01-01-2019), and so on.

Accounting/Currency Format – When you use the accounting/currency format on an amount, Excel applies the currency symbol and two decimal points.

Percentage Format – You can format figures such that they appear as a percentage. When you add the percentage format to 0.1, for example, it will appear as 10%.

  • What is a Scenario Manager?

If you have several variables and want to see the impact on the end outcome when these variables shift, Scenario Manager in Excel may be the method of preference.

You may construct a one-variable or two-variable data table if either one or two variables are changing. However, if you have three or more variables that may shift, a scenario planner is the way to go.

For instance, if you are a regional sales manager with four areas reporting to you, you can use scenario manager to build various scenarios (such as):

  • There is no revenue increase in either of the areas.
  • Area A rises 10%, whereas the other three do not.
  • Areas A and B rise by 10%, while the other two do not.
  • What are comments, and how can you apply them to a cell?

Comments may be added to any cell to include extra details for a number of purposes. You may add comments to a cell to explain its function, clarify a formula in the cell, or leave notes for other users about a cell.

To insert a statement into a cell, right-click on it and choose Insert Comment from the cell menu. Fill out the comment form with your opinions. A red triangle in the top right corner of a cell means that there is a statement associated with that cell. To erase a statement from a cell, right-click it and choose Delete Comment from the cell menu.

  • How can you highlight cells that contain errors?

There are many kinds of errors in Excel, including #N/A, #DIV/0!, #VALUE!, #REF!, #NAME, and #NUM.

Using conditional formatting, you will show any of the cells that comprise these errors.

The following are the ways to highlight cells with errors:

Choose the data set in which you want to highlight the cells that contain errors.

Go to the Home tab and choose Conditional Formatting.

Select ‘New Rule.’

Select the ‘Use a method to decide which cells to format’ choice in the New Formatting Rule dialogue box.

Enter =ISERROR(A1) in the formula field, where A1 is the active cell in the selection.

Select the color you want to highlight the cells with by clicking the Format icon.

Click the OK button.

  • What is a pivot table, and why is it important?

A pivot table allows for the simple summarization of vast amounts of data. We could quickly and efficiently compile huge numbers and conduct research. Pivot tables handle several cases more quickly and can be configured easily to meet our needs. The pivot table provides us with precision and analytical capacity. Making a complex pivot chart in Excel is a time-saver source that can also be connected to external data, giving versatility.

  • In Excel, how can you replace one value with another?

Using Excel’s FIND & REPLACE function, you can replace one attribute with another.

To do so, pick the data collection and click the CONTROL H keyboard shortcut (hold the control key and then press H). The Find & Replace dialogue box would appear. In this dialogue box, you can indicate the value to be changed as well as the substitution value.

  • What exactly is a dashboard?

A dashboard is a graphical description of essential knowledge used to present it. It is good for viewing large amounts of data on a single computer screen such that it can be monitored at a glance and useful information can be derived from it.

  • What is the Microsoft excel 2013 data filter shortcut?

Ctrl-Shift-L is the shortcut key to insert a filter in the results.

Note: The knowledge of Excel is very important for Data Analysts. Brush up your skills with such Excel data analytics interview questions.


It is important to respond correctly to the data analyst interview questions usually posed by interviewers during work interviews. Recruiting managers and interviewers would expect you to be able to address basic data analyst questions quickly and without delay.

You don’t need to memorize the data analyst interview questions for freshers to an extent where you talk like a robot, but you can always plan ahead of time, so you aren’t caught off guard during the interview.

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 



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