Data Analyst Interview Questions

This question helps interviewers assess your understanding of a data analytics project, your ability to plan and execute all the steps involved, and your communication skills. 

While answering this question, remember a data analytics project you had worked on. Then, guide the recruiter through the process, from beginning to end.

Sample answer:

“The very first step of a project that I’m working on is understanding the business objective. For this, I would work closely with various stakeholders and understand what they’re looking for.

Following this, I would gather the necessary data from multiple internal and external sources.

The next step includes ensuring data integrity. To do so, I would clean the data, deal with outliers and null values, and standardize data types.

Succeeding this is the process of Exploratory Data Analysis (EDA). In this step, I would trace patterns and trends using various statistical tools and techniques.

Based on the insights obtained from the step of EDA, I would create testable hypotheses that focus on the problem at hand.

The hypotheses would then undergo rigorous analysis using tools like machine learning, regression, etc. This helps predict any meaningful insights.

After finding the required insights, I would use data visualization tools like Power BI and Tableau to create appealing visualizations.

The last step involved would be communicating these insights to the stakeholders using data-backed evidence to facilitate decision-making.”

The objective of this question is to understand your ability to handle real-world datasets. It helps the interviewer understand your data pre-processing skills and problem-solving abilities.

Sample answer:

“A data analyst provided with a raw dataset has to handle multiple issues. The first is assessing the quality of data.

This involves sorting out missing values, duplicates, outliers, and any other errors. This also includes standardizing data types across, scaling, transformation, and replacing values.

Many times, various datasets have to be integrated/joined, and analysts have to ensure that this is done properly. Following this, they have to conduct the process of EDA, where they have to extract insights to create hypotheses.

Another issue that a data analyst comes across is data privacy and storage-related problems.”

This question tests the candidate’s knowledge of fundamental concepts and ability to differentiate its intricate details.

Sample answer:

“The main purpose of data mining is to unearth insights, patterns, and correlations of a dataset. This is used to support predictive analysis and other advanced modeling.

Data profiling is used to assess the dataset's quality and suitability for analysis. It involves summarizing the different characteristics of the data.”

The interviewer uses this question to gauge your data interpretation and visualization skills, statistical knowledge, problem-solving, and communication abilities.

Sample answer:

“Exploratory Data Analysis (EDA) is one of the most important steps in the entire data analysis process as it involves finding patterns and associations, which are then used to solve the business problem at hand. All further decision-making relies on this step.

This step allows analysts to refine data and identify the quirks of their dataset. They deal with increasing the accuracy and quality of the data. In this step, analysts help save resources and time by identifying the necessary variables and features used for modeling.

They also recognize the drawbacks of the dataset, which helps stakeholders to make informed decisions.”

This question tests your commitment to responsibly handling data. They assess your awareness of the different ethical considerations to keep in mind while processing data.

It also helps them understand if you are up to date with the current trends, habits, and ethical practices within the domain.

Sample answer:

“As a data analyst, it is fundamental to maintain good ethics during my process.

I must respect data ownership rights and not violate any agreements or copyright accords. I must ensure that I have the consent of those providing the data and always try to anonymize individuals. Furthermore, I must ensure that no bias or discrimination comes in the way of my work.

A data analyst must be transparent with their stakeholders regarding the steps, limitations, and results. They must be well aware and careful with whom the data and its results are shared. It is also vital to stay aware of the latest ethical practices of data analysis, AI, and machine learning.

Last but not least, if I ever come across unethical practices, I must report them to the concerned authorities.“

This question aims to understand your ability to identify and handle extreme data points and anomalies. It also tests your problem-solving skills.

Sample answer: 

“Outliers are those values that significantly vary from the rest present in the dataset. They can have a disproportionate effect on the results derived and, hence, must be handled with caution.

There are four methods one can use to handle outliers.

• Remove the outliers from the dataset - This must be done with caution as it can lead to the loss of important data.
• Transform the data to limit the overall influence the outlier has. 
• Imputation - This involves replacing the outlier with an estimated value based on prediction. 
• Use robust models like Huber’s regression, which gives less value to the outliers.”

The objective of this question is to understand your data pre-processing skills primarily.

Sample answer:

“The process of normalization is important as it ensures that all the values in a dataset are consistent.

Some important normalization techniques include robust modeling, log transformation, box Cox transformation, z-score standardization, and min-max scaling.”

In an interview, you may be asked questions to differentiate or explain some of the most basic yet fundamental concepts. Some of these are as listed:

A. Hypothesis testing process

The hypothesis testing process has 5 steps. 

1. Formulating the hypothesis 
2. Selecting the level of significance
3. Choosing the appropriate statistical test
4. Conducting the test
5. Making the decision

B. Quantitative vs. qualitative data

Quantitative data consists of numerical measurements. They represent amounts—for example - income, weight, height, etc.

On the other hand, qualitative data mainly includes characteristics of things—for example - color, survey feedback, etc.

C. Variance vs. standard deviation

Variance is calculated by finding the difference of each data point from the mean, squaring each difference, adding all the squares obtained, and then dividing it by the number of data points present in the sample. It tells us how far apart each value is from the mean.

Standard deviation is the square root of variance. It is a simpler measure that tells us how much each value deviates from the mean of the dataset.

D. Covariance

It is a statistical measure that informs us of the degree of the joint variability of 2 variables. A positive co-variance indicates that the two variables move in the same direction, and a negative co-variance means that they move in opposite directions.

E. Univariate, bivariate, and multivariate analysis

Univariate analysis involves exploring the characteristics of a single variable or dataset. In a bivariate analysis, we understand the features of two variables and the kind of relationship between them.

Multivariate analysis includes analyzing more than 2 variables simultaneously. This allows us to uncover complex patterns and dependencies.

F. How do you understand if a dataset follows a normal distribution?

Here are some ways to understand if a dataset follows a normal distribution:

• Observing the dataset’s characteristics - If the mean and median are approximately equal, the skewness is close to 0, and kurtosis is around 3. We can conclude that it is a normal distribution. 
• Visually - A normal distribution has a histogram that is a bell-shaped curve. Additionally, we can also use the box plot. A normal distribution has whiskers extending out symmetrically and the median line in the center.  
• Conducting statistical tests - We can conduct statistical tests like the Anderson-Darling test, the Shapiro-Wilk test, etc. Both tests use the p-value to indicate normality. 

G. Explain the Central Limit Theorem (CLT).

CLT states that as the sample size increases, the distribution of sample means will approximate a normal distribution regardless of its original population’s distribution. 

In an interview, you may simply be asked to define and explain what a certain function does. The following are the most common functions asked in an interview.

A. SUM, SUMIF, COUNT, COUNTIF, COUNTA, COUNTBLANK

• SUM - The SUM function is an aggregate function that adds up the numerical value present in cells within the mentioned range. It ignores the cells that have any non-numeric value.
• SUMIF - This function returns the sum of numerical values in the cells of a particular range, given that these cells meet the mentioned criteria.
• COUNT - This function yields the number of cells that contain a numerical value within the selected range.
• COUNTIF - This function returns the number of cells containing a numerical value within the selected range, given that it meets the specified criteria.
• COUNTA - This function is similar to the COUNT function. It doesn’t consider the data type while returning the number of cells within a selected range.
• COUNTBLANK - This returns the number of blank cells within a specific range.

B. VLOOKUP and its components

VLOOKUP, or ‘vertical lookup,’ is most useful when the user wants to find a specific value within a column and return the corresponding data from the same row but from another column.

It includes 3 mandatory parameters and 1 optional. 

• Lookup value - This is the value you want to find in a particular column.
• Table array - This is the range of data within which you are searching and returning. It includes the column within which you are searching and the column from where the value is to be returned.
• Column index number - This value specifies the exact column from which the data will be returned.
• Range lookup (optional) - This can be set to 2 options - TRUE or FALSE. TRUE returns an approximate match, and FALSE returns an exact match.

C. XLOOKUP

The XLOOKUP function can perform both vertical and horizontal searching. It allows the user to search for a specific value within a range and returns the data from another mentioned range or table. This function offers greater flexibility as compared to VLOOKUP.

D. CONCATENATE, AND

CONCATENATE - This function is used to merge multiple strings into one single string. It can also insert delimiters.

AND - This function is used to insert several criteria to be met. It returns TRUE if and only if all mentioned conditions are met, it returns FALSE.

E. TODAY, NOW, YEAR, MONTH

These functions are used to mention the data and/or time.

• TODAY - This returns the current date in the MM/DD/YY format. 
• NOW - This returns the current date and time in the MM/DD/YY HH:MM: SS format.
• YEAR - This returns the current year.
• MONTH - This returns the current month.

These questions aim to understand your proficiency in working with spreadsheet software such as Excel and Google Sheets. Furthermore, it tests your data manipulation and analysis skills. Below are 2 key questions and their model answers -

a. How do you create a Pivot Table, and what is its importance?

Sample Answer:

“PivotTables helps us summarize large datasets in a simplified manner, allowing a non-technical audience to interpret and understand them.

There are 5 basic steps involved in creating a Pivot Table. 

• Ensure that your data is well prepared. It must be properly structured, transformed, and cleaned. 
• Select the range of data you want in your table.
• Head over to the Insert tab on the ribbon and select ‘Pivot Table’.
• A dialog box appears where you must confirm the source of the data, and if need be, you can manually adjust it to include or exclude anything. Here, you must also select the destination of your Pivot Table. 
• The last step includes designing and customizing your Pivot Table. You can decide what variables you want as your columns, rows, values, etc. You can customize it by applying filters, adding customized calculated fields, and adding any charts.”

b. How do you filter data in a Pivot Table?

Sample Answer:

“Some ways by which you can filter data in a Pivot Table are using slicers and filters.

• Slicers are typically used when presenting data to a non-technical audience. It provides a more visual interface to select criteria on which you want your data to be filtered. 
• Filters is a more advanced tool that displays only those that meet the conditions set. There are two filter options - Field filter and value filter. As the name suggests, a field filter allows us to filter within a field or range, and a value filter only allows us to filter within the ‘Values’ area.”

This question tests your ability to extract and transform data from the SQL database.

Sample answer:

“The basic components of SQL query are as follows -

• SELECT - This clause allows you to select the columns from which you want data to be retrieved. You can either select all the columns (using *) or you can mention specific columns. 
• FROM - This clause mentions the source of the data you are retrieving from. 
• WHERE - This is a clause that allows the user to filter rows by setting conditions. It only returns those values that satisfy all the mentioned conditions.
• GROUP BY - This clause is used to group rows that have common values.

You can optionally add in other clauses like HAVING, ORDER BY, and JOINs as per the need.”

The interviewer asks questions based on these concepts to understand your basic language understanding and ability to differentiate them. It is also used to understand your attention to detail. Here is a list of concepts and their answers -

a. UNION, UNION ALL 

Both functions combine sets of multiple queries. The difference between UNION and UNION ALL is that the former excludes duplicate rows and only displays the unique rows. UNION ALL includes duplicate rows.

b. DELETE, TRUNCATE. DROP

• DELETE - This is used to delete specific rows within a table based on the mentioned conditions.
• TRUNCATE - This command completely empties a table without mentioning conditions. It is also comparatively faster than DELETE. 
• DROP - Using this command on a view, table, or any object completely and permanently deletes it from the database.

By asking this question, the interviewer assesses your ability to effectively work with a database schema. It also tests your ability to choose the appropriate data types when working on a project.

Sample answer:

“Several data types are used in SQL. Some of them are as follows:

1. Boolean - This data type stores logical conditions like true or false. 
2. Integer - Integer stores whole numbers. It is further classified into INT, BIGINT, SMALLINT, etc.
3. Char, Varchar, Text - All these types store data in text form. CHAR has a fixed length, VARCHAR allows you to vary the length of the text. TEXT is primarily used for texts having large values.
4. Date and Time - This is used to store data that is in the form of date and time. This is further classified into DATETIME, DATA, TIME, etc.
5. GIS - GIS stands for geospatial. This is used to store data on locations and geography.”

An interviewer asks this question to understand your awareness of best security practices and database interactions.

Sample answer:

“As a data analyst, I would use techniques like Least Privilege Principle, Object-Relational Mapping, stored procedures, and parameterized queries. Additionally, I would conduct or participate in audits to identify security issues and strengthen them.”

This question assesses your ability to leverage different data transformations, manipulation, and analysis features.

Sample answer:

“I would use stored procedures to conduct the process of ETL, handle complex data transformations, and use parameterized queries. I would also use stored procedures as a way to strengthen security as a stored procedure limits access to tables and the original datasets.”

An interviewer asks this question intending to assess your domain knowledge and to understand whether their candidate knows the advantages and suitability of Python for different data analysis tasks.

Sample answer:

“Python, amongst many other languages, is preferred due to its flexibility and accessibility. It provides extensive support for different processes relating to data, such as its transformation, manipulation, analysis, and visualization.

Python has a range of sources like Matplotlib, Numpy, and Pandas that are particularly designed for data analysis.”

This question assesses your practical knowledge and skills related to data transformation and analysis.

Sample answer:

“You can read data from a CSV file to a Pandas Dataframe by using the function from the Pandas library, which is read_csv.”

The objective of this question is to understand your data pre-processing skills concerning Python primarily.

Sample answer:

“Cleaning data is a vital step in a data analysis project. I would first deal with duplicates by removing them using the drop_duplicates function. If there are any unnecessary columns, I would get rid of them using the drop function.

I would then look into converting data types as needed using the ‘astype’ function. I would also look into detecting outliers and standardizing data.”

This question evaluates your understanding of statistical analysis and ability to perform hypothesis testing, which is vital in any data analysis project.

Sample answer:

“The steps involved in hypothesis testing are as follows:

• Formulating null and alternative hypotheses
• Selecting a level of significance
• Choosing the appropriate statistical test
• Conducting the test using libraries like SciPy.
• Analyzing the p-value 
• Determining the outcome”

By asking this question, an interviewer understands whether their candidate knows the two essential Python libraries for data manipulation and analysis and their differences.

Sample answer:

“Pandas are primarily used for data analysis and manipulation. Whereas Numpy’s focus is on numerical computations.

Pandas are more suitable for data labeling and indexing. Pandas deals with DataFrame and Series data structures. Whereas NumPy has multi-dimensional arrays.”

This question assesses your proficiency in Python and your awareness of the language’s evolution.

Sample answer:

“Python 3 is the improvised version of Python 2. It includes various library and syntax differences, better support for Unicode, and changes in functions like the print statement.

Python 2 is no longer supported.”

This question tests the candidate’s knowledge and understanding of data visualization libraries commonly used in the Python ecosystem.

Sample answer:

“Both Seaborn and Plotly are used for data visualization purposes.

Seaborn is built on Matplotlib and enhances the latter’s capabilities. It is used to create statistical graphs that are both informative and visually appealing.

Plotly helps develop aesthetically pleasing web-based dashboards, charts, and graphs. This makes it ideal to share and explore data in a user-friendly manner.”

Sample Answer:

“Both Power BI and Tableau are strong data visualization tools and are to be selected based on the requirements of your project. Power BI, being a Microsoft model, combines with other Microsoft tools to offer solid data modeling capabilities.

Tableau, on the other hand, is user-friendly, flexible, and easily accessible. Tableau makes it easy for a non-technical audience to explore the data and its visualizations.”

Sample Answer:

“If I were using Power BI, I would handle missing data and data transformation using Power BI. I would add filters to the columns to see if there are null values or any inconsistent data and either delete it or edit it after consulting with IT professionals and the stakeholders.

I would use the feature of calculated fields to add any variable required for the visualization. Additionally, for data transformation, I would use the process of ETL (Extract, Transform, Load).

In Tableau, I would use appropriate strategies such as exclusion or imputation. I would also use the ETL process to transform data in a way that ensures data integrity.”

Sample Answer:

“The important components of a dashboard are as follows -

• There must be transparency regarding the data source. Stakeholders need to know where the data is coming from. 
• All the relevant metrics and KPIs must be presented and easily visible. 
• The visualization must be interactive and have filters in a user-friendly manner. 
• All visualizations, titles, descriptions, etc. must be clear and should follow a consistent design.”

Sample Answer:

“Calculated fields are a feature present within Power BI and Tableau that allows analysts to create custom variables and calculations based on the needs of the project. It allows us to perform any complex transformation or calculations that would otherwise not be possible.”

Sample Answer:

“I would use color theory to choose appropriate color schemes. Color theory allows me to understand what colors go best with each other. Additionally, I would keep in mind audience considerations such as color blindness.

I would not use too bright colors that would distract from the message of the visualization. I would also carefully choose the colors based on the type of emotional response I want to evoke.”

This question aims to understand your communication skills and ability to reach a wide range of audiences.

Sample Answer:

“In a project I worked on, I had to analyze a dataset to determine the most preferred product among the millennial demographic. I was tasked with comprehending the data and creating visualizations to deliver to the sales team.

Hence, I focused on the business and marketing impact here. I used plain, direct language and data visualizations that highlighted our findings, which were then used to suggest appropriate strategies to target the demographic further.

This resulted in a 20% increase in revenue solely from the millennial age group.”

This question assesses your attention to detail, technical proficiency, resilience, and adaptability.

Sample Answer:

“During a project I was working on, I had to deal with a large dataset of housing information derived from a survey of residents of a state. There were multiple data entry errors in the address and date of birth columns.

I dealt with this by first documenting these errors and communicating them to the IT(data source) professionals. Then, I employed strategies to clean the data and standardize it. After which, to ensure data integrity, I ran a validation process.

This allowed me to analyze the data further and create visualizations with accuracy. It also improved the quality of data if it were to be used for any future analysis.”

This question tests two vital skills - time management and project management.

Sample Answer: 

“During my role at my former company, I had to handle 2 complex projects simultaneously. Both projects had tight deadlines. I used a prioritization system and daily project progress tracking to ensure they were completed within time while ensuring their quality.

 By strictly following these implemented methods and with efficient communication with my team members, we were able to meet both deadlines whilst ensuring their quality.

Both projects helped identify a resource misallocation issue which, when addressed, led to a 45% reduction in manufacturing lead time.”

An interviewer uses this question to understand whether their candidate’s thinking is structured. Furthermore, it tests whether the candidate has specific industry knowledge.

Sample Answer: 

“Firstly, I would obtain the company’s sales data from sources like CRM systems, data warehouses, and reports. I will ensure that this data is clean and accurate.

Then, I will use the process of Exploratory Data Analysis (EDA) and statistical techniques to extract trends and patterns within the data. This includes hypothesis testing to validate our findings.

Furthermore, I would gain insights into industry trends by conducting in-depth market research and competitor analysis.

 Having obtained all the necessary information, I would use data visualization tools like Power BI or Tableau to create informative charts and graphs.

I would then communicate these results, recommendations, and sales strategies based on my analysis to enhance business performance.

Lastly, I would regularly monitor Key Performance Indicators (KPIs) of our implemented strategies to track their efficiency.”

This question understands the candidate’s ability to resolve conflict peacefully. Additionally, it gives insight into the candidate’s adaptability and communication skills.

Sample Answer:

“When I was a fresher at XX company, my superiors and colleagues were wary of my analytical approach. This helped me recognize the necessity of aligning our objectives.

I immediately scheduled a meeting to discuss any of their concerns and to explain my approach in detail, making sure to highlight its benefits and how it would help our company in the long term.

This action helped create a positive, collaborative, and trusty environment, which ultimately led to the improvement of project outcomes throughout my role.”