{"id":6295,"date":"2021-06-26T10:57:08","date_gmt":"2021-06-26T10:57:08","guid":{"rendered":"https:\/\/35.154.138.233\/imarticus\/?p=6295"},"modified":"2024-05-23T09:54:54","modified_gmt":"2024-05-23T09:54:54","slug":"analytics-interview-questions","status":"publish","type":"post","link":"https:\/\/imarticus.org\/blog\/analytics-interview-questions\/","title":{"rendered":"Analytics interview questions"},"content":{"rendered":"<p><strong>1. What is the importance of validation of data?<\/strong><br \/>\nFrom a business perspective, at any stage, data validation is a very important tool since it<br \/>\nensures reliability and accuracy. It is also to ensure that the data stored in your system is<br \/>\naccurate, clean and useful. Improper validation or incorrect data has a direct impact on sales,<br \/>\nrevenue numbers and the overall economy.<\/p>\n<p><strong>2. What are the various approaches to dealing with missing values?<\/strong><br \/>\nMissing values or missing data can be dealt with by taking the following approaches-<br \/>\n\u25cf Encoding NAs- this used to be a very common method initially when working with<br \/>\nmachine learning and algorithms was not very common<br \/>\n\u25cf Deleting missing data casewise- this method works well for large datasets with very few<br \/>\nmissing values<br \/>\n\u25cf Using mean\/median value to replace missing values- this method works very well for<br \/>\nnumerical features<br \/>\n\u25cf Run predictive models to impute missing values- this is highly effective as it works best<br \/>\nwith the final model<br \/>\n\u25cf Linear regression- works well to provide good estimates for missing values<\/p>\n<p><strong>3. How do you know if a developed data model is good or bad?<\/strong><br \/>\nA developed data model should fulfil the following criteria to qualify as a good model-<br \/>\n\u25cf Whether the data is the model can be easily consumed<br \/>\n\u25cf If the model is scalable in spite of good data changes<br \/>\n\u25cf Whether performance can be predicted or not<br \/>\n\u25cf How good and fast can a model adapt to changes<\/p>\n<p><strong>4. What are some of the challenges I can face if I were to perform a data analysis?<\/strong><br \/>\nPerforming data analysis may involve the following challenges-<br \/>\n\u25cf Too much data collection which can often overwhelm data analysts or employees<br \/>\n\u25cf Differentiation between meaningful and useless data<br \/>\n\u25cf Incoherent visual representation of data<br \/>\n\u25cf Collating and analyzing data from multiple sources<br \/>\n\u25cf Storing massive amounts of generated data<br \/>\n\u25cf Ensuring and restoring both security and privacy of stored data as well as generated<br \/>\ndata<br \/>\n\u25cf Inadequate experts or lack of industry professionals who understand big data in depth<br \/>\n\u25cf Exposure to poor quality or inaccurate data<\/p>\n<p><strong>5. Explain the method of KNN imputation.<\/strong><br \/>\nThe term imputation means replacing the missing values in a data set with some other possible<br \/>\nvalues. Using KNN imputation in data analysis helps in dealing with missing data by matching a<br \/>\nparticular point with its nearest K neighbours assuming that it is a multi-dimensional space. This<br \/>\nhas been a highly popular method in pattern recognition and statistical estimation since the<br \/>\nbeginning of the 1970s.<\/p>\n<p><strong>6. What does transforming data mean?<\/strong><br \/>\nData transformation involves the process of converting data or information from a different<br \/>\nformat into the required format in a system. While mostly transforming data involves the<br \/>\nconversion of documents, occasionally it also means conversion of a program from one<br \/>\ncomputer language to another in a format that is readable by the system.<br \/>\nData transformation comprises of two key phases, data mapping to ensure smooth<br \/>\ntransformation, and code generation, for the actual transformation to happen and run on<br \/>\ncomputer systems.<\/p>\n<p><strong>7. State the difference between null and alternative hypothesis.<\/strong><br \/>\nIt is a null hypothesis when there is no key significance or relationship between two variables<br \/>\nand is something that the researcher is trying to disprove. No effects are observed as a result of<br \/>\nnull hypothesis and neither are there any changes in actions or opinions. The observations of<br \/>\nthe researcher are a plain result of chance.<br \/>\nAn alternative hypothesis on the other hand is just the opposite of a null hypothesis and has a<br \/>\nsignificant relationship between two measured and verified phenomena. Some effects are<br \/>\nobserved as a result of an alternative hypothesis; and since this is something the researcher is<br \/>\ntrying to prove, some amount of changes in opinions and actions are involved. An alternative<br \/>\nhypothesis is a result of a real effect.<\/p>\n<p><strong>8. What would you mean by principal component analysis?<\/strong><br \/>\nPrincipal component analysis is a method used to reduce large data sets in dimension by<br \/>\ntransforming larger sets of variables into smaller ones, while retaining the principal information.<br \/>\nThis is majorly done with the intent of improving accuracy since smaller data sets are easier to<br \/>\nexplore, as a result of which data analysis gets faster and quicker for machine learning.<\/p>\n<p><strong>9. Define the term - logistic regression.<\/strong><br \/>\n<strong><a href=\"https:\/\/imarticus.org\/blog\/what-do-you-understand-by-logistic-regression\/\">Logistic regression<\/a> <\/strong>is a form of predictive analysis in machine learning that attempts to identify<br \/>\nrelationships between variables. It is used to explain the relationship between a binary variable<br \/>\nand one or multiple nominal, ordinal, interval or ratio-level variables, while also describing the<br \/>\ndata. Logistic regression is used for categorical dependent variables.<\/p>\n<p><strong>10. How can I deal with multi-source problems?<\/strong><br \/>\nStoring the same data can often cause quality hindrances in analytics. Depending on what the<br \/>\nmagnitude of the issues are, a complete data management system needs to be put in place.<br \/>\nData reconciliation, elaborate and informative databases and pooling in segmented data can<br \/>\nhelp in deal with multi-source problems. Aggregation and data integration is also helpful while<br \/>\ndealing with multi-source data.<\/p>\n<p><strong>11. List the most important types of clustering algorithms.<\/strong><br \/>\nThe most important types of clustering algorithms are-<br \/>\n\u25cf Connectivity models- based on the idea that farther data points from each other exhibit<br \/>\nless similarity when compared to closer data points in data space<br \/>\n\u25cf Centroid models- the closeness of a data point to the cluster centroid derives the notion<br \/>\nof similarity for this model<br \/>\n\u25cf Distribution models- based on the probability that all data points in the same cluster are<br \/>\npart of the same distribution<br \/>\n\u25cf Density models- search for varied density areas of data points in the data space<\/p>\n<p><strong>12. Why do we scale data?<\/strong><br \/>\nScaling is important because sometimes your data set will have a set of features that completely<br \/>\nor partially vary in terms of units, range and magnitude. While certain algorithms have minimum<br \/>\nor zero effects, scaling can actually have positive impacts on the data. It is an important step of<br \/>\ndata preprocessing that also helps to normalise data within a given range. Scaling of data also<br \/>\noften helps in speeding up algorithm calculations.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>1. What is the importance of validation of data? From a business perspective, at any stage, data validation is a...<\/p>\n","protected":false},"author":1,"featured_media":6374,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[23],"tags":[],"pages":[],"coe":[],"class_list":{"0":"post-6295","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-analytics"},"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v24.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Top 12 Data Analyst Interview Questions and Answers<\/title>\n<meta name=\"description\" content=\"Below are a few of the more popular questions related to analytics industry you could get asked and the corresponding answers in a nutshell. 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What is the importance of validation of data? 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