Data Analyst Interview Questions and Answers

Data Analyst Interview Questions and Answers

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Updated on Oct 13, 2023 14:18 IST

We live in an information-driven age where data plays an integral role in the functioning of any organization. Thus, organizations are always on the lookout for skilled data analysts who can turn their data into valuable information and help organizations achieve better business growth.


If you plan to be a part of this high-potential industry and prepare for your next data analyst interview, you are in the right place! Here are the top data analyst interview questions & answers that will help you clear your next data analyst interview. These data analyst interview questions cover all the essential topics, ranging from data cleaning and validation to SAS.

Let’s begin!

Explore Top 13 Guesstimate Questions And Answers For Interview

Top Data Analyst Interview Questions & Answers

The article is categorized into two parts –

Core Data Analyst Interview Questions

Q1. What are the best practices for data cleaning?

Ans. There are 5 basic best practices for data cleaning:

  • Make a data cleaning plan by understanding where the common errors occur and keeping communications open.
  • Standardize the data at the point of entry. This way, it is less chaotic and you will be able to ensure that all information is standardized, leading to fewer errors on entry.
  • Focus on the accuracy of the data. Maintain the data value types, provide mandatory constraints, and set cross-field validation.
  • Identify and remove duplicates before working with the data. This will lead to an effective data analysis process.
  • Create a set of utility tools/functions/scripts to handle common data cleaning tasks.

Explore free data analysis courses

Q2. What is the difference between data mining and data profiling?

Ans. Below are some of the major differences between data mining and data profiling.

Data Mining

Data Profiling

Refers to the process of identifying patterns in a pre-built database Analyses raw data from existing datasets
Turns raw data into useful information by evaluating the existing database and large datasets Collects statistics or informative summaries of the data
Identifies the hidden patterns, searches for new, valuable, and non-trivial knowledge to generate useful information Helps evaluate data sets for consistency, uniqueness, and logic
Cannot identify incorrect or inaccurate data values Identifies the wrong data at the initial stage of data

Q3. Which data validation methods are used in data analytics?

Ans. The various types of data validation methods used are:

  • Field Level Validation – Field level validation is done in each field as the user enters the data to avoid errors caused by human interaction.
  • Form Level Validation – In this method, validation is done once the user completes the form before a save of the information is needed.
  • Data Saving Validation – This type of validation is performed during the saving process of the actual file or database record. This is usually done when there are multiple data entry forms.
  • Search Criteria Validation – This type of validation is relevant to the user to match what the user is looking for to a certain degree. It is to ensure that the results are actually returned.

Must Read – How to Become a Data Analyst from Scratch?

Q4. Which challenges are usually faced by data analysts? Share your opinion.

Ans. There are various ways you can answer the question. It might be very badly formatted data when the data isn’t enough to work with, clients provide data they have supposedly cleaned it but it has been made worse, not getting updated data or there might be factual/data entry errors.

Q5. How often should a data model be retained?

Ans. A good data analyst would be able to understand the market dynamics and act accordingly to retain a working data model so as to adjust to the new environment.

Q6. Explain what you do with suspicious or missing data.

Ans. When there is a doubt in data or there is missing data, then:

  • Make a validation report to provide information on the suspected data.
  • Have experienced personnel look at it so that its acceptability can be determined.
  • Invalid data should be updated with a validation code.
  • Use the best analysis strategy to work on the missing data like simple imputation, deletion method, or case-wise imputation.

Q7. What is the difference between the true positive rate and recall?

Ans. There is no difference, they are the same, with the formula:

(true positive)/(true positive + false negative)

Q8. What is a good data model?

Ans. The criteria that define a good data model are:

  • It is intuitive.
  • Its data can be easily consumed.
  • The data changes in it are scalable.
  • It can evolve and support new business cases.

Q9. Estimate the number of weddings that take place in a year in India.

Ans. To answer this type of guesstimation question, one should always follow four steps:

Step 1:

Start with the right proxy – here the right proxy will be the total population. You know that India has more than 1 billion population; more precisely, it’s around 1.2 billion.

Step 2:

Segment and filter – the next step is to find the right segments and filter out the ones which are not. You will have a tree-like structure, with branches for each segment and sub-branches that further filter out each segment. In this question, we will filter out the population above 35 years of age and below 15 for rural/below 20 for urban.

Step 3:

Always round the proxy to one or zero decimal points to make your calculation easy. Instead of doing a calculation like 1488/5, you can go for 1500/5.

Step 4:

Validate each number using your common sense to understand if it’s right. Add all the numbers that you have come up with after filtering. You will get the required guesstimate. E.g. we will validate the guesstimate to include one-time marriages only at the end.

Let’s do it:

Total population – 1.2 billion

Two main population segments – Rural (70%) and Urban (30%)

Now, filtering age group and sex ratio:

The average marriage age in rural – 15 to 35 years

The average marriage age in urban – 20 to 35 years

Assuming 65% of the total population is within 0-35 years,

Percentage of the population which has the probability of getting married in the rural area ≈ (35-15)/35*65 ≈ 40%

Percentage of the population which has the probability of getting married in the urban area ≈ (35-20)/35*65 ≈ 30%

Assuming the sex ratio to be 50% male and 50% female,

Total number of marriages in rural area ≈ .70*.40*1.2 billion/2 ≈ 170 million

Considering only first-time marriages in the rural area ≈ 170 million/20 ≈ 8.5 million

Total number of marriages in urban area ≈ .30*.30*1.2 billion/2 ≈ 50 million

Considering only first-time marriages in the rural area ≈ 50 million/15 ≈ 3million

Thus, the total marriage in India in a year ≈ 11 – 12 million

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Q10. What are the steps involved in a data analytics project?

Ans. It is one of the most commonly asked data analyst interview questions, go prepared for this one.

The fundamental steps involved in a data analysis project are –

  • Understand the Business
  • Get the data
  • Explore and clean the data
  • Validate the data
  • Implement and track the data sets
  • Make predictions
  • Iterate

Q11. What do you do for data preparation?

Ans. Since data preparation is a critical approach to data analytics, the interviewer might be interested in knowing what path you will take up to clean and transform raw data before processing and analysis. As an answer to such data analyst interview questions, you should discuss the model you will be using, along with logical reasoning for it. In addition, you should also discuss how your steps would help you to ensure superior scalability and accelerated data usage.

Also Read – How to Become a Data Analyst

Q12. What are some of the most popular tools used in data analytics?

Ans. The most popular tools used in data analytics are:

  • Tableau
  • Google Fusion Tables
  • Google Search Operators
  • Konstanz Information Miner (KNIME)
  • RapidMiner
  • Solver
  • OpenRefine
  • NodeXL
  • Io
  • Pentaho
  • SQL Server Reporting Services (SSRS)
  • Microsoft data management stack

Explore – Data Analytics Courses & Certification

Q13. What are the benefits of using version control?

Ans. The primary benefits of version control are –

  • Enables comparing files, identifying differences, and merging the changes
  • Allows keeping track of application builds by identifying which version is under development, QA, and production
  • Helps to improve the collaborative work culture
  • Keeps different versions and variants of code files secure
  • Allows seeing the changes made in the file’s content
  • Keeps a complete history of the project files in case of central server breakdown

Q14. Do you have any idea about the job profile of a data analyst?

Ans. Yes, I have a fair idea of the job responsibilities of a data analyst. Their primary responsibilities are –

  • To work in collaboration with IT, management, and/or data scientist teams to determine organizational goals
  • Dig data from primary and secondary sources
  • Clean the data and discard irrelevant information
  • Perform data analysis and interpret results using standard statistical methodologies
  • Highlight changing trends, correlations, and patterns in complicated data sets
  • Strategize process improvement
  • Ensure clear data visualizations for management

To learn more about data science, read our blog  – What is data science?

Q15. What is a data collection plan?

Ans. A data collection plan collects all the critical data in a system. It covers –

  • Type of data that needs to be collected or gathered
  • Different data sources for analyzing a data set

Q16. What is an Affinity Diagram?

Ans. An Affinity Diagram is an analytical tool to cluster or organize data into subgroups based on their relationships. These data or ideas are mostly generated from discussions or brainstorming sessions and used to analyse complex issues.

Q17. Name some of the essential tools useful for Big Data analytics.

Ans. The important Big Data analytics tools are –

  • NodeXL
  • Tableau
  • Solver
  • OpenRefine
  • Rattle GUI
  • Qlikview

Q18. What is data visualization?

Ans. In simpler terms, data visualization is a graphical representation of information and data. It enables the users to view and analyze data more smartly and use technology to draw them into diagrams and charts.

Read more about Data Visualization – Getting Started with Data Visualization: from Analysis to Aesthetics

Q19. Why should you choose data visualization?

Ans. Since it is easier to view and understand complex data in the form of charts or graphs, the trend of data visualization has picked up rapidly.

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Q20. What is the Metadata?

Ans. Metadata refers to detailed information about the data system and its contents. It helps to define the type of data or information that will be sorted.

Q21. What are some Python libraries used in Data Analysis?

Ans. Some of the vital Python libraries used in Data Analysis include –

  • Bokeh
  • Matplotlib
  • NumPy
  • Pandas
  • SciKit
  • SciPy
  • Seaborn
  • TensorFlow
  • Keras

Read about – What Is Python And Why Should You Get a Certification?

Q22. Explain the essential steps in the data validation process. 

Ans. Two steps are involved in the data validation process: data screening and data verification.

Data Screening: In this step, different algorithms screen the data to find inaccurate values. It ensures that the data is clean and ready for analysis.

Data Verification: In the data verification step, the accuracy and quality of source data are checked before using it. Every suspected value is evaluated on various use cases, and a final decision is made on whether the value must be included in the data. Data validation is a form of data cleansing.

Q23. Mention some problems that data analysts face while performing the analysis.

Ans. The problems that data analysts face while performing data analysis are:

  • Inconsistent and incomplete data
  • Duplicate entries and spelling mistakes
  • Poorly formatted data file
  • Different value representations and misclassified data
  • Overlapping data

Q24. Explain how to deal with multi-source problems.

Ans. To deal with multi-source problems, one can:

  • Restructure the schemas to accomplish a schema integration
  • Identify similar records and merge them into a single record containing all relevant attributes

Also Read>>How are Data Scientist and Data Analyst different?

Q25. Explain KPI, the design of experiments, and the 80/20 rule.

Ans. KPI stands for Key Performance Indicator. It is a metric or feature comprising any combination of spreadsheets, reports, or charts about the business process.

Also known as experimental design, the design of experiments is the initial process used before data is collected. It is used to split the data, sample, and set up a data set for statistical analysis.

The 80/20 rule means that 80% of your income (or results) comes from 20% of your clients (or efforts).

Q26. What do you mean by Hadoop Ecosystem?

Ans. Hadoop Ecosystem is the framework developed by Apache. It processes large datasets for an application in a distributed computing environment. It consists of the following Hadoop components.

  • HDFS
  • YARN
  • MapReduce
  • Spark
  • PIG
  • HIVE
  • HBase
  • Oozie
  • Mahout
  • Spark MLlib
  • Apache Drill
  • Zookeeper
  • Flume
  • Sqoop
  • Ambari
  • Solr
  • Lucene

Explore free Hadoop Online Courses & Certifications

Q27. What is MapReduce?

Ans. MapReduce is a framework that enables you to write applications to process large data sets, splitting them into subsets, processing each subset on a different server, and then blending results obtained on each. It consists of two tasks, namely Map and Reduce. The map performs filtering and sorting while reduce performs a summary operation. As the name suggests, the Reduce process occurs after the map task.

Explore Top MapReduce Interview Questions and Answers

Q28. What is imputation? Explain different types of imputation techniques. 

Ans. Imputation is the process of replacing the missing data with substituted values. While there are many ways to approach missing data, the most common imputation techniques are:

There are two types of imputation–single or multiple.

  • Single Imputation: In this, you find a single estimate of the missing value. The following are the single imputation techniques:
  • Mean imputation: Replace the missing value with the mean of that variable for all other cases.
  • Hot deck imputation: Identify all the sample subjects who are similar on other variables, then randomly choose one of their values on the missing variable.
  • Cold deck imputation: It works just like the hot deck but in a systematic manner. A systematically chosen value from an individual with similar values on other variables.
  • Regression imputation: The predicted value obtained by regressing the missing variable on other variables.
  • Stochastic regression: It works like the regression imputation and adds the average regression variance to the regression imputation.
  • Substitution: Impute the value from a new variable that was not selected to be in the sample.
  • Multiple Imputation: The values are estimated multiple times in the Multiple Imputation techniques.

Q29. What is the basic syntax style of writing code in SAS?

Ans. The basic syntax style of writing code in SAS is:

  • Write the DATA statement to name the dataset.
  • Write the INPUT statement to name the variables in the data set.
  • End all statements with a semi-colon.
  • Every SAS program must end with a RUN statement.
  • Use of proper space to separate the components in a SAS program statement.

Go through the Top SAS Interview Questions and Answers

Q30. What is the ANYDIGIT function in SAS?

Ans. The ANYDIGIT function searches a string for the first occurrence of any digit character. After searching for any digit in the character variable, it gives the first position of a digit from a character string. If the character is there, the ANYDIGIT function will return the position in the string of that character. If there is no such character, it will return a value of 0.

Q31. What is interleaving in SAS?

Ans. In SAS, Interleaving means combining individual sorted SAS data sets into one big sorted data set. Data sets can be interleaved by using a SET statement and a BY statement.

Q32. Which questions should you ask the user/client before you create a dashboard?

Though this depends on the user’s requirements, still some of the common questions that I would ask the client before creating a dashboard are –

  • What is the purpose of the dashboard?
  • Should the dashboard be retrospective or real-time?
  • How detailed should the dashboard be?
  • How tech and data-savvy is the end-user?
  • Does the data need to be segmented?
  • Should I explain the dashboard design to you?
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Q33. What is Clustering? What are the properties of clustering algorithms?

Ans. Clustering is a classification method that is applied to data. Clustering or cluster analysis is the process of grouping a set of objects so that the objects in the same cluster are more similar to each other than those in other clusters.

The properties of the clustering algorithm are as follows:

  • Hierarchical or flat
  • Hard and soft
  • Iterative
  • Disjunctive

Q34. What is the KNN imputation method?

Ans. KNN (K-nearest neighbor) is an algorithm that matches a point with its closest k neighbors in a multi-dimensional space.

Q35. Why is KNN used to determine missing numbers?

Ans. KNN is used for missing values under the assumption that a point value can be approximated by the values of the points that are closest to it based on other variables.

Q36. What is the K-means algorithm?

Ans. Kmeans algorithm partitions a data set into clusters such that a cluster formed is homogeneous and the points in each cluster are close. The algorithm tries to maintain enough separation between these clusters. Due to the unsupervised nature, the clusters have no labels.

Q37. What is the difference between linear regression and logistic regression?

Ans. The differences between linear regression and logistic regression are:

Linear Regression Logistic Regression
It requires independent variables to be continuous It can have dependent variables with more than two categories
Based on the least-square estimation Based on maximum likelihood estimation
Requires 5 cases per independent variable Requires at least 10 events per independent variable
Aimed at finding the best fitting straight line where the distance between the points and the regression line are errors As it is used to predict a binary outcome, the resultant graph is an S-curved one.

Q38. What is the condition for using a t-test or a z-test?

Ans. The T-test is usually used when we have a sample size of less than 30 and a z-test is when we have a sample test greater than 30.

Q39. What is the difference between standardized and unstandardized coefficients?

Ans. The standardized coefficient is interpreted in terms of standard deviation, while the unstandardized coefficient is measured in actual values.

Q40. What is the difference between R-squared and adjusted R-squared?

Ans. R-squared measures the proportion of variation in the dependent variables explained by the independent variables. Adjusted R-squared gives the percentage of variation explained by those independent variables that affect the dependent variable.

Understand the difference between R-squared and adjusted R-squared

Q41. What are the most popular statistical methods used when analyzing data?

Ans. The most popular statistical methods used in data analytics are –

  • Linear Regression
  • Classification
  • Resampling Methods
  • Subset Selection
  • Shrinkage
  • Dimension Reduction
  • Nonlinear Models
  • Tree-Based Methods
  • Support Vector Machines
  • Unsupervised Learning

Q42. What is Collaborative Filtering?

Ans. Collaborative filtering is a technique recommender systems use to make automatic predictions or filter about a user’s interests. This is achieved by collecting information from many users.

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Q43. What is a Pivot Table?

Ans. A Pivot Table is a Microsoft Excel feature that summarises huge datasets quickly. It sorts, reorganizes, counts, or groups data stored in a database. This data summarization includes sums, averages, or other statistics.

Q44. Name different sections of a Pivot Table.

Ans. A Pivot table has four different sections, which include –

  • Values Area
  • Rows Area
  • Column Area
  • Filter Area

Q45. What is Standard Deviation?

Ans. Standard deviation is a very popular method to measure any degree of variation in a data set. It measures the average spread of data around the mean most accurately.

Q46. What is the Truth Table?

Ans. A truth Table is a collection of facts that determine the truth or falsity of a proposition. It works as a complete theorem-prover and is of three types –

  • Accumulative Truth Table
  • Photograph Truth Table
  • Truthless Fact Table

Q47. What is the main difference between overfitting and underfitting?

Ans. Overfitting – A statistical model describes any random error or noise that occurs when a model is super complicated. An overfit model has poor predictive performance as it overreacts to minor fluctuations in training data.

Underfitting – In underfitting, a statistical model cannot capture the underlying data trend. This type of model also shows poor predictive performance.

Q48. Explain the time series analysis. 

Ans. Time series analysis is a statistical technique that analyzes time-series data to extract meaningful statistics and other data characteristics. There are two ways to do it, namely, the frequency domain and the time domain. Various methods like exponential smoothening and log-linear regression methods help in forecasting the output of a particular process by analyzing the previous data.

Explore Statistics for Data Science Online Courses & Certifications

Q49. What do you mean by Hash Table?

Ans. A hash Table is a data structure that stores data associatively. It is a map of keys to values. It stores the data in an array format where each data value has its unique index value. A hash table uses a hash technique to generate an index into an array of slots from which the desired value can be fetched.

Q50. What are collisions in hash tables? How to deal with them?

Ans. A hash table collision occurs when two different keys are hashed to the same index in a hash table. In simple terms, it happens when two different keys hash to the same value. Collisions, thus, create a problem as two elements cannot be stored in the same slot in an array.

There are many techniques to avoid hash table collision:

  1. Separate Chaining technique
  2. Open Addressing

Q51. Explain data wrangling.

Ans. Data wrangling is the process through which the raw data is cleaned, structured, analyzed and enriched into a desirable usable format. This is important for making better decisions. To do so, data analysts use techniques such as concatenating, joining, grouping, and merging for data analysis so that it can be used for another dataset.

Q52. What is n-gram?

Ans. An n-gram is a contiguous sequence of n items from a given sequence of text or speech. It combines adjacent words or letters of length n in the source text. A probabilistic language model predicts the next item in a sequence as a (n-1).

Q53. How do you highlight cells with negative values in Excel?

Ans. We can highlight cells with negative values through conditional formatting; the steps include –

  • Select the required cells
  • Go to the Home tab and click on Conditional Formatting
  • Go to the Highlighted Cell Rules and click on the Less Than option
  • In the dialog box of Less Than, specify the value as 0

Q54. How will you handle slow Excel workbooks?

Ans. To fix the slow Excel workbooks, we should –

  • Avoid Volatile Functions
  • Use manual calculation mode
  • Avoid Array Formulas
  • Use Helper Columns
  • Maintain all the referenced data in a single sheet
  • Use Helper columns instead of array formulas
  • Convert all the unused formulas to values
  • Use Conditional Formatting with Caution
  • Use Excel Tables and Named Ranges
  • Convert Unused Formulas to Values
  • Keep All Referenced Data in One Sheet

Q55. What is Normal Distribution?

Ans. The normal distribution is a probability function that explains how variable values are distributed. It is a symmetric distribution where –

  • Maximum observations cluster around the central peak
  • Probabilities for values further away from the mean taper off equally in both directions

e.g. – The Bell Curve is a normal distribution.

Key Properties of the Normal distribution | CFA Level 1 - AnalystPrep

Fig – Normal distribution

As in the above image, you can see that the data is distributed around a central value without any bias to the left or right.

Q56. What is an outlier?

Ans. Any observation that lies at an abnormal distance from other observations is known as an outlier. It indicates either variability in the measurement or an experimental error.

Q57. What are the two main methods two detect outliers?

Ans. Box plot method: if the value is higher or lesser than 1.5*IQR (interquartile range) above the upper quartile (Q3) or below the lower quartile (Q1), respectively, then it is considered an outlier.

Standard deviation method: if the value is higher or lower than mean ± (3*standard deviation), it is considered an outlier.

Q58. Why is ‘naïve Bayes’ naïve?

Ans. It is naïve because it assumes that all datasets are equally important and independent, which is not true in a real-world scenario.

Q59. What is the difference between factor analysis and principal component analysis?

Ans. The principal component analysis explains the covariance between variables, while factor analysis explains the variance between variables.

You May Like – An Introduction to Principal Component Analysis

Q60. Define prescriptive analytics.

Ans. This is a type of data analytics that is used to find ways to achieve something. It factors information related to available resources, possible situations, past performance, and available resources. Here, raw data is analyzed, and technologies are used for making decisions.

Q61. What is imputation?

Ans. Missing data may lead to some critical issues; hence, imputation is the methodology that can help to avoid pitfalls. It is the process of replacing missing data with substituted values. Imputation helps in preventing the list-wise deletion of cases with missing values.

Q62. What is A/B Testing?

Ans. A/B testing is a random experiment with two variants. It is mainly a comparative study where these page variants are presented before random users, and their feedback is statistically analyzed to see which performs better. A/B testing is also known as split testing.

Q63. What is a ‘p-value’?

Ans. The p-value or probability value is a number that explains the chances of your data occurring by random chance.

These were the interview questions for the Data Analyst job profile. We hope that you have found these interview questions & answers article useful. The questions covered in this post are the most sought-after data analyst interview questions that will help you ace your next interview!


Why is data analytics needed?

Data analytics is important because it helps businesses and organizations in optimizing their performance. Data analytics is used by companies to make informed business decisions. This can help organizations to achieve better business growth as they can gain a better understanding of the market, consumers, their product or services, and much more.

What are the essential skills for data analysts?

The essential skills for data analysts are SQL, R or Python, Microsoft Excel, Data Visualization, Machine Learning, Critical Thinking, etc.

Is Data Analytics a good career?

Technology professionals with data analytics skills are finding themselves in high demand as businesses look to harness the power of data. A career in data analytics is one of the most sought-after professions and high-paying jobs. Since the demand for data analysts is high, the supply of skilled professionals is limited. So, starting a career in data analytics can be a great option.

Does data analytics require coding?

Data analysts do not need to have advanced coding or programming skills. But familiarity with programming languages can help you gain a competitive edge in the field. Data analysts use various programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.

What is data analytics for beginners?

Data Analytics is one of the most trending terminologies nowadays. It is the process of analyzing and examining raw data to make conclusions by identifying and analyzing behavioral patterns. This process helps the businesses to grow and expand their operations in the market.

What are some examples of data analytics in real world?

Some interesting examples of data analytics are - e-commerce sites like Amazon and entertainment giants like Netflix, use data analytics to categorize the choices of millions of users Top banks and other financial institutions use data analytics to effectively introduce risk management systems, preventing loss of consumer data Packaged good companies like Pepsico, Cadbury's use data analytics to simplify complex supplier networks and boost their supplier networks

What is the eligibility to become a data analyst?

To work as a data analyst, you must have an undergraduate or a postgraduate degree in a relevant discipline, such as: Computer science; Economics; Information management; Mathematics; Statistics; Marketing; Finance; Business information systems

How do I become a data analyst?

To sharpen data analytics skills, you should learn - The basics of natural language processing, information extraction, computer vision, bioinformatics, speech processing, etc. Multivariable calculus, numerical linear algebra, computational linear algebra, matrix algebra, and probability Build and manage predictive analytic applications, like classification, recommendation, and personalization systems Analyze the data using various statistical analysis models, such as association rules, clustering, classification, prediction models, etc.

What are the role and responsibilities of a Data Analyst?

Roles and responsibilities of a data analyst include - To work in collaboration with IT, management and/or data scientist teams to determine organizational goals; To dig data from primary and secondary sources; To clean the data and discard irrelevant information; To perform data analysis and interpret results using standard statistical methodologies; To highlight changing trends, correlations, and patterns in complicated data sets; To strategize process improvement; To ensure clear data visualizations for management; To design, create and maintain relevant and useful databases and data systems; To prioritize tasks and solve code problems and other data-related issues; To identify trends and make forecasts; To create data dashboards, graphs, and visualizations; To produce and track key performance indicators

Who can become a Data Analyst?

Software Developers; Business Analysts; System/Database Administrators; Database Developers; Database Managers; Data Architects; Data Modelers; Report Writers; Consultants

How much is a data analyst paid?

Alike other professions, salaries for a data analyst vary and depend on their skills and experience. at the entry levels, data analysts earn somewhere around Rs. 3 - 4 LPA, while professionals with 5 - 10 years of experience earn close to Rs. 7 u2013 10 LPA. Experienced professionals with over 15+ years of experience earn over Rs. 20 LPA.

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