Data Analyst Interview Questions and Answers for 2025

Here you need to list down all possible tools and frameworks you would have used to perform end to end data analysis. This should include programming languages you might have used, tools used for data cleaning and EDA, data visualization tools, query languages etc. 

Being a data analyst, you are expected to have knowledge of the below tools for analysis and presentation purposes. Attend this KnowledgeHut DevOps online classes to master DevOps skills. 

• Python 
• Tableau 
• Microsoft Excel 
• MySQL 
• Microsoft PowerPoint or equivalent 
• Microsoft SQL Server 
• IBM SPSS 

As we know EDA is one of the very important step in data analysis, answer we give to this question depicts our overall in-depth understanding of EDA and it’s contribution towards data analysis process. 

Exploratory Data Analysis or EDA is one of the important steps in the data processing life cycle and it is nothing but a data exploration technique to understand the various aspects of the data. It is basically used to filter the data from redundancies. 

• Exploratory Data Analysis helps to understand the data better 
• It helps you to gain confidence in your data to a point where you are ready to apply a machine learning algorithm 
• It allows you to refine your selection of feature variables that will be used later for building the model 
• You can discover hidden patterns, trends and insights from the data

Here interviewer wants to know whether you know standard practices followed for data cleaning and also some of the preventive mechanisms. Some of the best practices used in data cleaning are: 

• Preparation of data cleaning plan by understanding where the common errors take place and we need to keep communications open 
• We need to identify and remove duplicates before we work with the data. This will ensure data analysis process is effective 
• As a data analyst it’s our responsibility to focus on the accuracy of the data. Also maintain the value types of data, provide mandatory constraints and set cross-field validation 
• We need to standardize the data at entry point so that it is less chaotic and you will be able ensure that all the information is standardized, leading to fewer errors on entry 

Here we need to explain our understanding of data validation including the steps or processed involved. 

We need to start with formal definition and then talk about processed involved. 

Data validation is the process that involves validating or determining the accuracy of the data at hand and also the quality of the sources. 

There are many processes in data validation step, and the main ones include data screening and data verification. 

• Data Screening: This process is all about making use of a variety of models to ensure that the data under consideration is accurate and there are no redundancies present. 
• Data Verification: If there is a redundancy found at the screening process, then it is evaluated based on multiple steps and later a call is taken to ensure the presence of the data item. 

This question is asked to assess our knowledge of corrective mechanisms to address missing values in dataset. 

Missing values in a dataset is one of the big problems in real life scenarios. This situation will arise when no information is provided for one or more items or for a whole unit. 

Some of the ways to handle missing values in a dataset are: 

• Listwise Deletion: In this method, an entire record is excluded from analysis if any single value is missing 
• Average Imputation: Use the average value of the responses from the other participants to fill in the missing value 
• Regression Substitution: One can use multiple-regression analysis to estimate a missing value 
• Multiple Imputation: It creates plausible values based on the correlations for the missing data and then averages the simulated datasets by incorporating random errors in your prediction 

This question asked to see our overall understanding of data profiling, not just brief bookish definition. Hence we need to proceed accordingly. 

Data profiling is a methodology which involves analyzing entire set of entities present across data to a greater depth. The main objective of this process is to provide highly accurate information based on the data and its attributes such as the datatype, frequency or occurrence, and more.  

This process involves following key steps: 

• Collect the data from one or more sources 
• Perform initial data cleansing work to consolidate and unify data sets from different sources 
• Eventually run profiling tasks to collect statistics on the data and at last identify characteristics and issues 

This question is asked to take a look at our depth towards awareness of the all  the tools, frameworks, technologies used for big data and relevant processes. It would be ideal to brief about what they’re used for along with listing down the tools. 

• Hadoop – used to efficiently store and process large datasets 
• Spark - unified analytics engine for large-scale data processing 
• Hive - allows users to read, write, and manage petabytes of data using SQL-like query languages 
• Flume – intended for high volume data ingestion to Hadoop of event-based data 
• Mahout – designed to create scalable machine learning algorithms 
• Flink - unified stream-processing and batch-processing framework 
• Tableau - interactive data visualization software 
• Microsoft PowerBI - interactive data visualization software developed by Microsoft 
• QlikView - business analytics platform 

This question is asked not just to check our understanding of Time Series Analysis but also its various components. 

Time series analysis is a statistical method that deals with an ordered sequence of values of a variable at equally spaced time intervals. It is a widely used technique while working with trend analysis and time series data in particular. 

Components of TSA (Time Series Analysis) include: 

• Long-term movement or trend 
• Short-term movements - seasonal variations and cyclic variations 
• Random or irregular movements 

This question is intended to assess our knowledge towards the topic of outliers including the different types. 

An outlier is a value in a dataset which is considered to be far away from the mean of the characteristic feature of the dataset. I.e. a value that is much larger or smaller in a set of data. 

For example – in following set of numbers 2 and 98 are outliers  

2, 38, 40, 41, 44, 46, 98 

There are two types of outliers: 

• Univariate – scenario that consists of an extreme value on one variable 
• Multivariate - combination of unusual scores on at least two variables 

This question is intended to assess our knowledge of outlier detection 

  techniques, so accordingly we should be talking about at least two most widely used and popular methodologies. 

Multiple methodologies can be used for detecting outliers, but two most commonly used methods are as follows: 

1. Standard Deviation Method: Here the value is considered as an outlier if the value is lower or higher than three standard deviations from the mean value 
2. Box Plot Method: Here, a value is considered to be an outlier if it is lesser or higher than 1.5 times the IQR (Interquartile Range)

Along with the definition and how it works ensure to talk about the meaning behind ‘K’ used in K-means algorithm. 

K-means algorithm clusters data into different sets based on how close the data points are to each other. The number of clusters is indicated by ‘K’ in the K-means algorithm. 

It tries to maintain a good amount of separation between each of the clusters. However, since it works in an unsupervised nature, the clusters will not have any sorts of labels to work with.

So main focus behind this question would be not just to see our understanding of types of hypothesis testing, but mainly to see our understanding towards how they’re used in real world scenario. 

Hypothesis testing is the procedure used by statisticians and scientists to accept or reject statistical hypotheses.  

Null Hypothesis: It states that there’s no relation between predictor and outcome variables in the population. It is denoted by HO. For example there’s no association between a patient's BMI and diabetes. 

Alternative Hypothesis: It states that there’s some relation between the predictor and outcome variables in the population. It is denoted by H1. Example to this is there could be no association between a patient's BMI and diabetes. 

Objective behind this question would be see how well we understand dataanalysis process end to end including problems faced on daily basis and some of    the commonly faced problems by data analysts across the world.  

Some of the common problems that data analysts encounter:  

• Handling duplicate and missing values 
• Collecting the meaningful right data and the right time 
• Making data secure and dealing with compliance issues 
• Handling data purging and storage problems

This question is asked to take a sneak peek into our depth of knowledge when it comes to Microsoft Excell sheets. We need to highlight uses, advantages and capabilities of pivot table here. 

Uses of the Pivot table include: 

• Pivot tables are one of the powerful and key features of Excel sheet 
• They are leveraged to see comparisons, patterns, and trends in your data 
• They are employed to be able to view and summarize entirety of large datasets in a simple manner 
• They help with quick creation of reports through simple drag-and-drop operations 

 Here we need to list down all possible ways to filter the data in Excel. 

• As per management requirement 
• Period wise filter - week wise, month and year wise 
• Current period comparison with any period (past/future) - this quarter’s performance Vs Last quarter’s performance 
• Filter for error summary - for example there’s abnormal increase in raw material wastage, then we can filter for error summary and highlight it in the report for management 
• Filter for performance - for example while analyzing company’s growth

Awareness and knowledge about data security and measures taken to ensure that are equally important for data analysts. This question is intended to check for that aspect. 

Data Security safeguards digital data from unwanted access, corruption, or theft. Data security is critical to public and private sector organizations because there’s legal and moral obligation to protect users and a reputational risk of a data breach.  

Protecting the data from internal or external corruption and illegal access helps to protect an organization from reputational harm, financial loss, consumer trust degradation, and brand erosion.

Through this answer we need to convey along with formal definitions of primary key and foreign key, our practical knowledge about them when we speak of SQL. 

• A PRIMARY KEY is a column or a group of columns in a table that uniquely identifies the rows of data in that table. 
• A FOREIGN KEY is a column or group of columns in one table, that refers to the PRIMARY KEY in another table. It maintains referential integrity in the database. 
• Table with the FOREIGN KEY is called the child table, and a table with a PRIMARY KEY is called a reference or parent table. 

Here interviewer would be happy to listen if we explain the differences through examples. 

Data blending allows a combination of data from different data sources to be linked. Whereas, Data Joining works only with data from one and the same source.  

For example: If the data is from an Excel sheet and a SQL database, then Data Blending is the only option to combine the two types of data. However if the data is from two excel sheets, you can use either data blending or data joining. 

Data blending is also the only choice available when ‘joining’ the tables is impractical. This impracticality occurs when the dataset is humongous. When joins might create duplicate data or when using databases such as Salesforce and Cubes which do not support joins. 

At times we might ignore theory of statistics and algebra involved during data analysis process. Through this answer we need to demonstrate how wellacquainted are we in terms of fundamentals of statistics. 

Eigenvectors: Eigenvectors are basically used to understand linear transformations. These are calculated for a correlation or a covariance matrix. 

For definition purposes, you can say that Eigenvectors are the directions along which a specific linear transformation acts either by flipping, compressing or stretching. 

Eigenvalues: Eigenvalues can be referred to as the strength of the transformation or the factor by which the compression occurs in the direction of eigenvectors. 

Let A be a n × n matrix. 

• An eigenvector of  A  is a nonzero vector  v  in  Rn  such that  Av = λ v ,  for some scalar  λ . 
• An eigenvalue of  A  is a scalar  λ  such that the equation  Av = λ v  has a nontrivial solution. 

If  Av = λ v  for  v 

≠≠

 0,  we say that  λ  is the eigenvalue for  v ,and that  v  is  an eigenvector for  λ .

Here along with definition and understanding of clustering, let’s explain why is it done, it’s objective. 

• Hierarchical clustering or hierarchical cluster analysis, is an algorithm that groups similar objects into common groups called clusters. 
• The goal is to create a set of clusters, where each cluster is different from the other and, individually, they contain similar entities.