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Data Analyst Interview Questions and Answers

by Sathish, on Jan 11, 2021 10:48:57 AM

1.Why do you want to be a data analyst?

Ans: There are many roles out there for data analysts within various industries. This question will tell the interviewer about your thought process in choosing this role. Answer this question with the STAR method by explaining the key reasons you want to be a data analyst as well as which key skills you have for the role:

 A data analyst’s job is to take data and use it to help companies make better business decisions. I’m good with numbers, collecting data, and market research. I chose this role because it encompasses the skills I’m good at, and I find data and marketing research interesting.”

2.Mention what is the responsibility of a Data analyst?

Ans: Responsibility of a Data analyst include,

  • Provide support to all data analysis and coordinate with customers and staffs
  • Resolve business associated issues for clients and performing audit on data
  • Analyze results and interpret data using statistical techniques and provide ongoing reports
  • Prioritize business needs and work closely with management and information needs
  • Identify new process or areas for improvement opportunities
  • Analyze, identify and interpret trends or patterns in complex data sets
  • Acquire data from primary or secondary data sources and maintain databases/data systems
  • Filter and “clean” data, and review computer reports
  • Determine performance indicators to locate and correct code problems
  • Securing database by developing access system by determining user level of access

3.What is the difference between Data Mining and Data Analysis?


     Data Mining            Data Analysis
Used to recognize patterns in data stored. Used to order & organize raw data in a meaningful manner.
Mining is performed on clean and well-documented data. The analysis of data involves Data Cleaning.  So, data is not present in a well-documented format.
Results extracted from data mining are not easy to interpret. Results extracted from data analysis are easy to interpret.


So, if you have to summarize, Data Mining is often used to identify patterns in the data stored. It is mostly used for Machine Learning, and analysts have to just recognize the patterns with the help of algorithms. Whereas, Data Analysis is used to gather insights from raw data, which has to be cleaned and organized before performing the analysis.

4. What is required to become a data analyst?

Ans: To become a data analyst,

  • Robust knowledge on reporting packages (Business Objects), programming language (XML, Javascript, or ETL frameworks), databases (SQL, SQLite, etc.)
  • Strong skills with the ability to analyze, organize, collect and disseminate big data with accuracy
  • Technical knowledge in database design, data models, data mining and segmentation techniques
  • Strong knowledge on statistical packages for analyzing large datasets (SAS, Excel, SPSS, etc.)

5.What is the process of Data Analysis?

Ans: Data Analysisis the process of collecting, cleansing, interpreting, transforming and modeling data to gather insights and generate reports to gain business profits. Refer to the image below to know the various steps involved in the process.

  • Collect Data: The data gets collected from various sources and is stored so that it can be cleaned and prepared. In this step, all the missing values and outliers are removed.
  • Analyse Data: Once the data is ready, the next step is to analyze the data. A model is run repeatedly for improvements. Then, the mode is validated to check whether it meets the business requirements.
  • Create Reports: Finally, the model is implemented and then reports thus generated are passed onto the stakeholders.

6.What is the difference between Data Mining and Data Profiling?

Ans: Data Mining: Data Mining refers to the analysis of data with respect to finding relations that have not been discovered earlier. It mainly focuses on the detection of unusual records, dependencies and cluster analysis.

Data Profiling: Data Profiling refers to the process of analyzing individual attributes of data. It mainly focuses on providing valuable information on data attributes such as data type, frequency etc.

7.What are the key requirements for becoming a Data Analyst?

Ans: This data analyst interview question tests your knowledge about the required skill set to become a data scientist.
To become a data analyst, you need to:

  • Be well-versed with programming languages (XML, Javascript, or ETL frameworks), databases (SQL, SQLite, Db2, etc.), and also have extensive knowledge on reporting packages (Business Objects).
  • Be able to analyze, organize, collect and disseminate Big Data efficiently.
  • You must have substantial technical knowledge in fields like database design, data mining, and segmentation techniques.
  • Have a sound knowledge of statistical packages for analyzing massive datasets such as SAS, Excel, and SPSS, to name a few.

8.Name the best tools used for data analysis?

Ans: A question on the most used tool is something you’ll mostly find in any data analytics interview questions.
The most useful tools for data analysis are:

  • Tableau
  • Google Fusion Tables
  • Google Search Operators
  • RapidMiner
  • Solver
  • OpenRefine
  • NodeXL
  • io

9.What should a data analyst do with missing or suspected data?

Ans: In such a case, a data analyst needs to:

  • Use data analysis strategies like deletion method, single imputation methods, and model-based methods to detect missing data.
  • Prepare a validation report containing all information about the suspected or missing data.
  • Scrutinize the suspicious data to assess their validity.
  • Replace all the invalid data (if any) with a proper validation code.

10.Which data analyst software are you trained in?

Ans: This question tells the interviewer if you have the hard skills needed and can provide insight into what areas you might need training in. It’s also another way to ensure basic competency. In your answer, include the software the job ad emphasized, any experience with that software you have, and use familiar terminology.

“I have a breadth of software experience. For example, at my current employer, I do a lot of ELKI data management and data mining algorithms. I can also create databases in Access and make tables in Excel.”

11.What was your most difficult data analysis project?

Ans: With a question like this, the interviewer is gaining insight into how you approach and solve problems. It also provides an idea of the type of work you have already done. Be sure to explain the event, action, and result (EAR), avoid blaming others, and explain why this project was difficult

“My most difficult project was on endangered animals. I had to predict how many animals would survive to 2020, 2050, and 2100. Before this, I’d dealt with data that was already there, with events that had already happened. So, I researched the various habitats, the animal’s predators and other factors, and did my predictions. I have high confidence in the results.”

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12.What are some of the statistical methods that are useful for data-analyst?

Ans: Statistical methods that are useful for data scientist are

  • Bayesian method
  • Markov process
  • Spatial and cluster processes
  • Rank statistics, percentile, outliers detection
  • Imputation techniques, etc.
  • Simplex algorithm
  • Mathematical optimization

13.What is time series analysis?

Ans: Time series analysis can be done in two domains, frequency domain and the time domain.  In Time series analysis the output of a particular process can be forecast by analyzing the previous data by the help of various methods like exponential smoothening, log-linear regression method, etc.

14.Explain what is correlogram analysis?

Ans: A correlogram analysis is the common form of spatial analysis in geography. It consists of a series of estimated autocorrelation coefficients calculated for a different spatial relationship.  It can be used to construct a correlogram for distance-based data, when the raw data is expressed as distance rather than values at individual points.

15.Mention the steps of a Data Analysis project?

Ans: The core steps of a Data Analysis project include:

  • The foremost requirement of a Data Analysis project is an in-depth understanding of the business requirements. 
  • The second step is to identify the most relevant data sources that best fit the business requirements and obtain the data from reliable and verified sources. 
  • The third step involves exploring the datasets, cleaning the data, and organizing the same to gain a better understanding of the data at hand. 
  • In the fourth step, Data Analysts must validate the data.
  • The fifth step involves implementing and tracking the datasets.
  • The final step is to create a list of the most probable outcomes and iterate until the desired results are accomplished.

17.What are the problems that a Data Analyst can encounter while performing data analysis?

Ans: A critical data analyst interview question you need to be aware of. A Data Analyst can confront the following issues while performing data analysis:

  • Presence of duplicate entries and spelling mistakes. These errors can hamper data quality.
  • Poor quality data acquired from unreliable sources. In such a case, a Data Analyst will have to spend a significant amount of time in cleansing the data. 
  • Data extracted from multiple sources may vary in representation. Once the collected data is combined after being cleansed and organized, the variations in data representation may cause a delay in the analysis process.
  • Incomplete data is another major challenge in the data analysis process. It would inevitably lead to erroneous or faulty results. 

18.Explain univariate, bivariate, and multivariate analysis? 

Ans: Univariate analysis refers to a descriptive statistical technique that is applied to datasets containing a single variable. The univariate analysis considers the range of values and also the central tendency of the values. 

Bivariate analysis simultaneously analyzes two variables to explore the possibilities of an empirical relationship between them. It tries to determine if there is an association between the two variables and the strength of the association, or if there are any differences between the variables and what is the importance of these differences.  

Multivariate analysis is an extension of bivariate analysis. Based on the principles of multivariate statistics, the multivariate analysis observes and analyzes multiple variables (two or more independent variables) simultaneously to predict the value of a dependent variable for the individual subjects.

19.Explain the difference between R-Squared and Adjusted R-Squared? 

Ans: The R-Squared technique is a statistical measure of the proportion of variation in the dependent variables, as explained by the independent variables. The Adjusted R-Squared is essentially a modified version of R-squared, adjusted for the number of predictors in a model. It provides the percentage of variation explained by the specific independent variables that have a direct impact on the dependent variables.

20.How can a Data Analyst highlight cells containing negative values in an Excel sheet?

Ans: Final question in our data analyst interview questions and answers guide. A Data Analyst can use conditional formatting to highlight the cells having negative values in an Excel sheet. Here are the steps for conditional formatting:

  • First, select the cells that have negative values.
  • Now, go to the Home tab and choose the Conditional Formatting option.
  • Then, go to the Highlight Cell Rules and select the Less Than option.
  • In the final step, you must go to the dialog box of the Less Than option and enter “0” as the value.

Data Analyst Interview Questions: Tableau

21.What is a dual axis?

Ans: Dual Axis is a phenomenon provided by Tableau. This helps the users to view two scales of two measures in the same graph. Websites such as make use of dual axis to show the comparison between two measures and the growth of these two measures in a septic set of years. Dual axes let you compare multiple measures at once, having two independent axes layered on top of one another. Refer to the below image to see how it looks. 

22.What is the difference between joining and blending in Tableau?

Ans: TheJoiningterm is used when you are combining data from the same source, for example, worksheet in an Excel file or tables in an Oracle database. Whileblendingrequires two completely defined data sources in your report.

23.How to create a calculated field in Tableau?

Ans: To create a calculated field in Tableau, you can follow the below steps:

  • Click the drop down to the right of Dimensions on the Data pane and select “Create > Calculated Field”to open the calculation editor.
  • Name the new field and create a formula.

24.How to view underlying SQL Queries in Tableau?

Ans:n To view the underlying SQL Queries in Tableau, we mainly have two options:

  • Use the Performance Recording Feature: You have to create a Performance Recording to record the information about the main events you interact with the workbook. Users can view the performance metrics in a workbook created by Tableau.
    Help -> Settings and Performance -> Start Performance Recording.
    Help -> Setting and Performance -> Stop Performance Recording.
  • Reviewing the Tableau Desktop Logs: You can review the Tableau Desktop Logs located at C:UsersMy DocumentsMy Tableau Repository. For live connection to the data source, you can check log.txt and tabprotosrv.txt files. For an extract, check tdeserver.txt file.

25.Can you tell how to create stories in Tableau?

Ans:  Stories are used to narrate a sequence of events or make a business use-case. The Tableau Dashboard provides various options to create a story. Each story point can be based on a different view or dashboard, or the entire story can be based on the same visualization, just seen at different stages, with different marks filtered and annotations added.

To create a story in Tableau you can follow the below steps: 

  • Click the New Story tab.
  • In the lower-left corner of the screen, choose a size for your story. Choose from one of the predefined sizes, or set a custom size, in pixels.
  • By default, your story gets its title from its sheet name. To edit it, double-click the title. You can also change your title’s font, color, and alignment. Click Apply to view your changes.
  • To start building your story, drag a sheet from the Story tab on the left and drop it into the center of the view.
  • Click Add a caption to summarize the story point.
  • To highlight a key takeaway for your viewers, drag a text object over to the story worksheet and type your comment.
  • To further highlight the main idea of this story point, you can change a filter or sort on a field in the view, then save your changes by clicking Update above the navigator box.


Topics:Information Technologies (IT)



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