{"id":67037,"date":"2018-08-21T06:42:40","date_gmt":"2018-08-21T06:42:40","guid":{"rendered":"https:\/\/www.whizlabs.com\/blog\/?p=67037"},"modified":"2018-08-21T06:42:40","modified_gmt":"2018-08-21T06:42:40","slug":"data-analyst-interview-questions-answers","status":"publish","type":"post","link":"https:\/\/www.whizlabs.com\/blog\/data-analyst-interview-questions-answers\/","title":{"rendered":"Data Analyst Interview Questions and Answers"},"content":{"rendered":"<p class=\"p1\" style=\"text-align: justify;\"><em><strong><span class=\"s1\">Looking for Data Analyst interview questions for freshers and experienced? You have reached the right place!<\/span><\/strong><\/em><\/p>\n<p class=\"p1\" style=\"text-align: justify;\"><span class=\"s1\">One of the promising and lucrative big data career today is the position of a data analyst. Data analytics industry is dynamic, and it has widened the scope for data analysts with a high packaged job and a steep career growth. It is forecasted that tech giants are going to recruit more than 700,000 data analysis professionals by 2020. And if you are on the same track and preparing for the data analyst job interview, then you must be well aware of the core areas that a recruiter must check to ensure whether you have the proper knowledge on those areas.<\/span><\/p>\n<blockquote>\n<p style=\"text-align: justify;\">Also Check: Top 50 <a href=\"https:\/\/www.whizlabs.com\/blog\/big-data-interview-questions\/\" target=\"_blank\" rel=\"noopener\">Big Data Interview Questions<\/a>\u00a0with the detailed answers<\/p>\n<\/blockquote>\n<p><span class=\"s1\">Hence, you should be focused on the related data analyst interview questions which are focused on those areas related to your job position level. And if you are not sure how to categorize them, then you are in the right place! In this blog, we are going to discuss some of the best <\/span><span class=\"s2\">data analyst interview questions and answers.<\/span><\/p>\n<h2 class=\"p5\" style=\"text-align: justify;\"><span class=\"s1\">Data Analyst Interview Questions for Freshers<\/span><\/h2>\n<p>While going for a data analyst interview as a fresher, you need to prepare yourself with the basic and fundamental data analyst interview questions. Here we&#8217;re enlisting data analyst interview questions for freshers with detailed answers.<\/p>\n<h4 class=\"p5\" style=\"text-align: justify;\">1. How do you define the primary responsibilities of a data analyst?<\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer: <\/b>A data analyst is responsible for <\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li1\"><span class=\"s1\">Analyzing all data related information<\/span><\/li>\n<li class=\"li1\"><span class=\"s1\">Taking active participation during the data auditing<\/span><\/li>\n<li class=\"li1\"><span class=\"s1\">Suggesting and forecasting based on statistical analysis of data. <\/span><\/li>\n<li class=\"li1\"><span class=\"s1\">Helps to improve the business process and process optimization<\/span><\/li>\n<li class=\"li1\"><span class=\"s1\">To generate business reports using the raw data.<\/span><\/li>\n<li class=\"li1\"><span class=\"s1\">Sourcing data from different data sources and harvest that in the database.<\/span><\/li>\n<li class=\"li1\"><span class=\"s1\">Coordinating with the clients and stakeholders.<\/span><\/li>\n<li class=\"li1\"><span class=\"s1\">Identifying new areas of improvement.<\/span><b><\/b><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s2\">2. What are the required skills for a data analyst?<\/span><\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer: <\/b>A data analyst must possess the below skills:<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li10\"><span class=\"s2\">Strong analytical skills in big data.<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Strong hands-on experience in reporting tools, ETL frameworks, programming languages like XML, relational and non-relational databases like SQL, HBase, etc.<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Technical knowledge of data modeling, data mining, and related database design <\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Robust understanding of statistical tools like SAS, SPSS to analyze large datasets.<\/span><b><\/b><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s2\">3. What are the steps followed in a standard data analyst project?<\/span><\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer: <\/b>The steps followed in a data analyst project are:<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li10\"><span class=\"s2\">Defining the problem <\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Data exploration<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Preparing data <\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Data Modelling<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Data validation<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Tracking and implementation<\/span><b><\/b><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s2\">4. What are the different types of tools data analysts use during a complete project life cycle?<\/span><\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer:<\/b> Based on the responsibilities below mentioned types of tools a data analyst comes across during a complete project life cycle \u2013<\/span><\/p>\n<table class=\"t1\" cellspacing=\"0\" cellpadding=\"0\">\n<tbody>\n<tr>\n<td class=\"td1\" valign=\"top\">\n<p class=\"p1\"><span class=\"s1\"><b>The task of a Data scientist<\/b><\/span><\/p>\n<\/td>\n<td class=\"td2\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\"><b>Commonly Used Tools<\/b><\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td1\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Data sourcing<\/span><\/p>\n<\/td>\n<td class=\"td2\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">MongoDB, Hadoop HDFS, Riak, SAP, Cassandra, Redis<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td1\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Data storing<\/span><\/p>\n<\/td>\n<td class=\"td2\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Oracle, SAP Sybase, MySql, Apache HBase, Neo4j<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td3\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Data conversion and ETL<\/span><\/p>\n<\/td>\n<td class=\"td4\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Sqoop<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td1\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Data transformation<\/span><\/p>\n<\/td>\n<td class=\"td2\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Hive<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td1\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Exploratory analysis<\/span><\/p>\n<\/td>\n<td class=\"td2\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Elasticsearch, Knime<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td5\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Model building and insight generation<\/span><\/p>\n<\/td>\n<td class=\"td6\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">R, SAS, pandas, Python, Julia, Rapid Miner, SPSS, Mahout, SAP HANA, Clojure<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td5\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Visualization<\/span><\/p>\n<\/td>\n<td class=\"td6\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Ggplot2, SAP Business Objects, Tableau, Cognos, JMP, JasperSoft<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td1\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Model execution<\/span><\/p>\n<\/td>\n<td class=\"td2\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Hadoop, Java, Spark, Scala, C#, Storm<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td1\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Versioning<\/span><\/p>\n<\/td>\n<td class=\"td2\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Git<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td1\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">IDE<\/span><\/p>\n<\/td>\n<td class=\"td2\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">RStudio, Sublime<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td class=\"td1\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Text for coding<\/span><\/p>\n<\/td>\n<td class=\"td2\" valign=\"top\">\n<p class=\"p10\"><span class=\"s1\">Jupyter Notebook, R Shiny<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4 class=\"p5\"><span class=\"s2\">5. Why is data mining a useful technique in big data analysis?<\/span><\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer:<\/b><\/span> <span class=\"s1\">Big data Hadoop is a clustered architecture where we need to analyze a large set of data to identify the unique patterns. The patterns help to understand the problem areas of business and establish a solution. The data mining is a useful process to do this job. Hence, it is widely used in big data analysis.<\/span><\/p>\n<h4 class=\"p5\">6. What is Data Cleansing?<\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer: <\/b>Data cleansing is the process to identify and remove inconsistencies and errors from data to enhance data quality.<\/span><\/p>\n<h4 class=\"p5\">7. Explain Logistic Regression?<\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer:<\/b> Logistic regression is one of the statistical methods used by data analysts to examine a dataset where a single and multiple independent variables define an outcome.<\/span><\/p>\n<h4 class=\"p5\">8. What is Data Profiling?<\/h4>\n<p class=\"p14\" style=\"text-align: justify;\"><span class=\"s12\"><b>Answer:<\/b><\/span> <span class=\"s1\">The data profiling is a process to validate the data already available in an existing data source and to understand whether it is readily used for some other purposes.<\/span><\/p>\n<blockquote>\n<p class=\"p4\"><span class=\"s4\">To become a Data Analyst, you need to have a good knowledge of Data Analyst Tools. For this, you can go through our previous blog on<\/span> <span class=\"s6\"><a href=\"https:\/\/www.whizlabs.com\/blog\/data-scientists-tools-to-improve-productivity\/\" target=\"_blank\" rel=\"noopener\">Data Scientists Tools To Improve Productivity<\/a><\/span><\/p>\n<\/blockquote>\n<h4 class=\"p5\">9. What are the different data validation methods which are used by data analysts?<\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer: <\/b>There are two methods used for data validation in data analysis:<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li1\"><span class=\"s2\">Data screening<\/span><\/li>\n<li class=\"li1\"><span class=\"s2\">Data verification<\/span><b><\/b><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s2\">10. What is Data Screening Process?<\/span><\/h4>\n<p class=\"p17\" style=\"text-align: justify;\"><span class=\"s13\"><b>Answer:<\/b><\/span><span class=\"s1\"> The data screening is a part of the data validation process where the entire set of data is processed by using various data validation algorithms to verify whether the data has any business related issues.<\/span><\/p>\n<h4 class=\"p5\">11. Explain your understanding of the K-mean algorithm?<\/h4>\n<p class=\"p19\" style=\"text-align: justify;\"><span class=\"s13\"><b>Answer:<\/b><\/span><span class=\"s1\"> The K-mean algorithm is used for the data partitioning in a clustered architecture. In this process data sets are classified through a\u00a0certain number of\u00a0clusters (for example k clusters). Here objects are divided into several k groups.<\/span><\/p>\n<p class=\"p19\" style=\"text-align: justify;\"><span class=\"s1\">Within the k-mean algorithm:<\/span><\/p>\n<p class=\"p19\" style=\"text-align: justify;\"><span class=\"s1\">1. As the clusters are in a shape of a sphere, so data points within the clusters are centered in the cluster<\/span><\/p>\n<p class=\"p19\" style=\"text-align: justify;\"><span class=\"s1\">2. The spread or the variance of the cluster is almost similar.<\/span><\/p>\n<h4 class=\"p5\">12. Explain Outlier.<\/h4>\n<p class=\"p21\" style=\"text-align: justify;\"><span class=\"s14\"><b>Answer:<\/b><\/span> <span class=\"s1\">The outlier is a term used by analysts to refer to a value that appears distant and diverges from the overall pattern of a sample. These are of two types:<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li22\"><span class=\"s2\">Univariate<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Multivariate<\/span><b><\/b><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s1\">13. Explain the Hierarchical Clustering Algorithm.<\/span><\/h4>\n<p class=\"p24\" style=\"text-align: justify;\"><span class=\"s16\"><b>Answer:<\/b><\/span> <span class=\"s2\">Hierarchical clustering algorithm is the process to combine and divide existing data groups to create a hierarchical structure out of that to represent the order in which the groups are merged or divided.<\/span><\/p>\n<h4 class=\"p5\">14. What is Time\u00a0Series Analysis?<\/h4>\n<p class=\"p17\" style=\"text-align: justify;\"><span class=\"s13\"><b>Answer:<\/b><\/span> <span class=\"s1\">Time series analysis is a process to forecast the output of a process through the analysis of the previous data using various statistical methods like log-linear regression method, exponential smoothening, etc. It can be performed in two domains \u2013 time domain and frequency domain.<\/span><\/p>\n<h4 class=\"p5\">15. Explain Collaborative Filtering.<\/h4>\n<p class=\"p17\" style=\"text-align: justify;\"><span class=\"s13\"><b>Answer:<\/b><\/span><span class=\"s1\"> Collaborative filtering is an algorithm that helps the user with a recommendation based responses based on the behavioral data analysis.<\/span><\/p>\n<h4 class=\"p5\">16. What is clustering in data analysis?<\/h4>\n<p class=\"p17\" style=\"text-align: justify;\"><span class=\"s13\"><b>Answer:<\/b><\/span><span class=\"s1\"> The clustering in data analysis defines the process of grouping a set of objects based on specific predefined parameters. This is one of the industry recognized data analysis technique especially used in big data analysis.<\/span><\/p>\n<h4 class=\"p5\">17. What is the imputation process? What are the different types of imputation techniques available?<\/h4>\n<p class=\"p19\" style=\"text-align: justify;\"><span class=\"s14\"><b>Answer:<\/b><\/span><span class=\"s1\"> The Imputation process is the process to replace missing data elements with substituted values. There are two major types of imputation processes with subtypes:<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li24\"><span class=\"s2\">Single Imputation<\/span><\/li>\n<li class=\"li24\"><span class=\"s2\">Hot-deck imputation<\/span><\/li>\n<li class=\"li24\"><span class=\"s2\">Cold deck imputation<\/span><\/li>\n<li class=\"li24\"><span class=\"s2\">Mean imputation<\/span><\/li>\n<li class=\"li24\"><span class=\"s2\">Regression imputation<\/span><\/li>\n<li class=\"li24\"><span class=\"s2\">Stochastic regression<\/span><\/li>\n<li class=\"li24\"><span class=\"s2\">Multiple Imputation<\/span><b><\/b><\/li>\n<\/ul>\n<blockquote><p>With the generation of Big Data, the more opportunities are arising in the field of Data Analytics. Read our previous blog to learn more about the <a href=\"https:\/\/www.whizlabs.com\/blog\/big-data-analytics-importance\/\" target=\"_blank\" rel=\"noopener\">Big Data Analytics importance<\/a>.<\/p><\/blockquote>\n<h4 class=\"p5\"><span class=\"s2\">18. What is n-gram?<\/span><\/h4>\n<p class=\"p17\" style=\"text-align: justify;\"><span class=\"s13\"><b>Answer:<\/b><\/span> <span class=\"s1\">An n-gram is an adjoining sequence of n items from a sequence of speech or text or. It is a kind of probabilistic language model to predict the next item in the sequence following the form of (n-1).<\/span><\/p>\n<h4 class=\"p5\">19. Mention few of the statistical methods which are widely used for data analysis?<\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer: <\/b>Some of the useful and widely used statistical methods:<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li10\"><span class=\"s2\">Simplex algorithm<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Bayesian method<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Cluster and Spatial processes<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Markov process<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Mathematical optimization<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Rank statistics, Outliers detection, Percentile<\/span><\/li>\n<\/ul>\n<h2 class=\"p5\">Data Analyst Interview Questions and Answers for Experienced<\/h2>\n<p>If you have gained some experience in Big Data Analytics and preparing for your next interview, this section of Data Analyst Interview Questions for experienced will help you in your preparation. Let&#8217;s go through these data analyst interview questions.<\/p>\n<h4 class=\"p5\">20. What is your perception of a good data model?<\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer: <\/b>A good data model should have below criteria<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li22\"><span class=\"s2\">It must be consumed easily <\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">It should be scalable for large data changes<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">It should be performed in a predictable manner<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">It should be adaptable if the requirements are changed.<\/span><b><\/b><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s2\">21. Tell me the common problems you face as a data analyst?<\/span><\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer:<\/b><\/span> <span class=\"s1\">Few of the common problems we face as data analyst are:<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li10\"><span class=\"s2\">Duplicate entries<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Common misspelling<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Illegal values<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Missing values<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Identifying overlapping data<\/span><\/li>\n<li class=\"li10\"><span class=\"s2\">Varying representations of values <\/span><b><\/b><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s2\">22. What are the best practices for data cleaning?<\/span><\/h4>\n<p class=\"p24\" style=\"text-align: justify;\"><span class=\"s16\"><b>Answer:<\/b><\/span> <span class=\"s2\">Some of the best practices for data cleaning<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li22\"><span class=\"s2\">Sorting the data based on different attributes<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">To clean large datasets stepwise.<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Improving the data by cleansing in each step until it achieves a good data quality<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">To break the large data sets into small data to increase the iteration speed<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Using scripts\/tools\/functions to handle the common cleansing task.<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Alternatively, arrange the data by estimated frequency and address the most common problems<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Analysis of the summary statistics for each column <\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Tracking of every date cleaning operation to<span class=\"Apple-converted-space\">\u00a0 <\/span>alter or remove operations if necessary<\/span><b><\/b><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s2\">23. What are the missing patterns which are generally observed in data analysis?<\/span><\/h4>\n<p class=\"p21\" style=\"text-align: justify;\"><span class=\"s14\"><b>Answer:<\/b><\/span> <span class=\"s1\">The common missing patterns that are observed during data analysis are<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li22\"><span class=\"s2\">Completely missing at random<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Random missing<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Missing based on the missing value <\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Missing based on the unobserved input variable<\/span><b><\/b><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s2\">24. What should you do with suspected or missing data?<\/span><\/h4>\n<p class=\"p8\" style=\"text-align: justify;\"><span class=\"s1\"><b>Answer: <\/b>We can do below operations with missing data:<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li22\"><span class=\"s2\">We can prepare a validation report that will provide information on all missing or suspected data. In the report, we must provide detail information like which validation fails with date time stamp.<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Suspected data can be further examined to validate their credibility<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Invalid data should be replaced and assigned with a validation code<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Using best data analysis techniques like single imputation, deletion method, model-based methods, etc. to work on missing data strategy.<\/span><b><\/b><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s2\">25. How do you deal with the multi-source problems?<\/span><\/h4>\n<p class=\"p21\" style=\"text-align: justify;\"><span class=\"s14\"><b>Answer:<\/b><\/span> <span class=\"s1\">We can do the following to deal with the multi-source problems:<\/span><\/p>\n<ul class=\"ul1\" style=\"text-align: justify;\">\n<li class=\"li22\"><span class=\"s2\">Performing a schema integration through the restructuring of schemas<\/span><\/li>\n<li class=\"li22\"><span class=\"s2\">Identifying and merging similar records into a single record which will contain all relevant attributes without redundancy<\/span><\/li>\n<\/ul>\n<h4 class=\"p5\"><span class=\"s12\">Bottom Line<\/span><\/h4>\n<p class=\"p1\" style=\"text-align: justify;\"><span class=\"s28\">Hope the data <\/span><span class=\"s2\">analyst interview questions <\/span><span class=\"s28\">mentioned<b> <\/b><\/span><span class=\"s2\">above <\/span><span class=\"s28\">will help you to prepare for the data analyst job interview. However,<\/span><span class=\"s1\"> if you are an aspiring data analyst<b> <\/b>get yourself acquainted with at least one of the popular tools for data scientists. You can proceed with <a href=\"https:\/\/www.whizlabs.com\/spark-developer-certification\/\" target=\"_blank\" rel=\"noopener\"><span class=\"s29\">Spark Developer Certification (HDPCD)<\/span><\/a> and <a href=\"https:\/\/www.whizlabs.com\/hdpca-certification\/\" target=\"_blank\" rel=\"noopener\"><span class=\"s29\">HDP Certified Administrator (HDPCA)<\/span><\/a> <span class=\"s29\">Certification<\/span> based on Hortonworks Data platform. Whizlabs is successfully assisting aspiring candidates with the certification training that will give you comprehensive guidance, both theoretical and hands-on to pass the big data certifications.<\/span><\/p>\n<p class=\"p1\" style=\"text-align: justify;\"><span class=\"s1\">So, combine your study with our <\/span><span class=\"s2\">data analyst interview questions and training and build your Big Data Career!<\/span><\/p>\n<p style=\"text-align: justify;\"><strong><em>Have any questions\/concerns? Just write in the comment section below or submit at\u00a0<a href=\"https:\/\/help.whizlabs.com\/hc\/en-us\/requests\/new\" target=\"_blank\" rel=\"noopener\">Whizlabs helpdesk<\/a>, we&#8217;ll respond you in no time.<\/em><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Looking for Data Analyst interview questions for freshers and experienced? You have reached the right place! One of the promising and lucrative big data career today is the position of a data analyst. Data analytics industry is dynamic, and it has widened the scope for data analysts with a high packaged job and a steep career growth. It is forecasted that tech giants are going to recruit more than 700,000 data analysis professionals by 2020. And if you are on the same track and preparing for the data analyst job interview, then you must be well aware of the core 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Malhotra","author_link":"https:\/\/www.whizlabs.com\/blog\/author\/aditi\/"},"uagb_comment_info":14,"uagb_excerpt":"Looking for Data Analyst interview questions for freshers and experienced? You have reached the right place! One of the promising and lucrative big data career today is the position of a data analyst. Data analytics industry is dynamic, and it has widened the scope for data analysts with a high packaged job and a steep&hellip;","_links":{"self":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/67037","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/users\/220"}],"replies":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/comments?post=67037"}],"version-history":[{"count":0,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/posts\/67037\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media\/67166"}],"wp:attachment":[{"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/media?parent=67037"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/categories?post=67037"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.whizlabs.com\/blog\/wp-json\/wp\/v2\/tags?post=67037"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}