Hello guys, if you are preparing for Data Analyst interview and looking for frequently asked Data Analysis questions then you have come to the right place. Earlier, I have shared common Data Science questions, Machine Learning Questions, and AI questions from interviews and in this article, I am going to share common Data Analyst Interview Questions with answers for 1 to 3 years experienced people. If you have worked in the field of Data Analysis then most likely you will know the answers of all of them but if you cannot answer then I suggest you to join a good Data Analysis course like Google's Data Analyst Professional certificate on Coursera to learn and revise essential Data Analysis concepts. These days, we are living in an information-driven age where data plays a very important role in our lives. Companies are always on the lookout for expert data analysts who can add value to their organizations. Skilled data analysts are able to turn data into valuable information and help companies achieve business growth. If you want to become a part of this growing industry, you should diligently prepare for your next interview. 20 Data Analyst Interview Questions with Answers If you want to become an expert data analyst and land your dream job, you have come to the right place. The questions in this article will help you to achieve exactly that. These questions will help you to effectively clear your next data analyst interview. The questions in this article cover almost all of the essential topics like data cleaning and data validation. So what are you waiting for? Let us dive in. 1. What are some of the best practices for data cleaning? You should start by making a data cleaning plan by understanding where common errors take place and keeping the communication lines open. You should also standardize the data at the point of entry. You should identify and remove duplicates before working with the data. 2. What is the basic difference between data profiling and data mining? Data mining basically refers to the process of identifying patterns in an existing database. In contrast, data profiling is the process of analyzing raw data from existing datasets. 3. Name two data validation methods that are used in data analysis. Field-level validation is something that is done in each separate field as the user enters the data to avoid errors caused by human interaction. Form-level validation is done once the user completes the form before a save of the information is needed. 4. What are some of the challenges usually faced by data analysts? Challenges that a data analyst may face vary from badly formatted data to situations where there isn't enough data to work with. You may also not be getting updated data or there might be data entry errors. 5. How often can a data model be retained? An expert data analyst should be able to understand the market dynamics and act accordingly to retain a working data model so that you can adjust to the new environment. 6. What can you do with suspicious or missing data? You should start by making a validation report to provide information on the suspected data. You should have some experienced data analysts look at it so that acceptance can be determined. You should also make sure that invalid data is updated with a validation code. 7. What is the difference between the true positive rate and recall? The important thing to note here is that there is no difference between the true positive rate and recall. They are one and the same. 8. What, according to you, is a good data model? A good data model is intuitive and can evolve and support new business cases. The data can be easily consumed. The data changes are also scalable. 9. What are the different steps involved in a data analysis project? The fundamental steps involved in a data analysis project are understanding the business, getting the data, exploring and cleaning the data, validating the data, implementing and tracking the data sets, making predictions, and finally, iterating. 10. What can you do for data preparation? Data preparation is a critical approach to data analytics. Before processing and analyzing, you should know the path that you are taking for cleaning and transforming the raw data. You should also be sure about which model you will be using. 11. What are some of the most popular tools used in data analytics? The most popular tools used in data analytics are Tableau, Google Fusion Tables, Google Search Operators, RapidMiner, Solver, and OpenRefine. 12. What are the advantages of using version control? Version control allows you to compare files, identify differences, and merge the changes. You will also be able to keep track of applications by identifying which version is under development. 13. What is your idea of the job profile of a data analyst? A data analyst has to dig data from primary and secondary sources. He has to clean the data and discard irrelevant information. He should be able to perform data analysis and interpret the results. 14. What can you tell us about a data collection plan? A data collection plan is useful for collecting all the critical data in a system. 15. What is an Affinity Diagram? AN Affinity Diagram is basically an analytical tool that allows you to cluster or organize data into subgroups based on their relationships. 16. What are some of the important tools used in Big Data Analytics? The most important Big Data Analytics tool are KNIME, NodeXL, Solver, OpenRefine, Tableau, Rattle GUI, and Qlikview. 17. What do you mean by data visualization? Data visualization is basically a graphical representation of data and information. It allows the users to view and analyze the data in a more efficient way and draw them into diagrams and charts. 18. What are the benefits of data visualization? It is very easy to view and understand complex data that is in the form of charts or graphs. This is why the trend of data visualization has picked up rapidly. 19. What do you mean by Metadata? Metadata basically refers to detailed information about the data system and all of its contents. It allows us to define the type of data or the information that will be sorted. 20. What are some of the Python libraries used in data analysis? Some of the most important Python libraries used in data analysis are Numpy, Matplotlib, Bokeh, Pandas, Scikit, Scipy, Seaborn, Tensorflow, and Keras. Conclusion There you have it. These are some of the most important and essential data analysis questions that may be asked in an interview. With the help of the questions in this article, you will be able to become an expert data analyst and land your dream job. 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