Entry Level Data Analyst is becoming a popular job for youngsters these days and almost everyone is looking out for this job as well. But it’s not going to be that easy to get the job as you have to provide yourself worthy and qualified and go through an interview for the job.
You can’t possibly take the entry-level data analyst interview for granted because your job fully depends on it. All you need is to prepare for the job and make sure that you do everything right and get the job. To prepare for the job interview, you might wanna check out the popularly asked questions for the respective job and see what kind of questions the interviewers ask. Since you are here, we can guess that you are also looking for entry-level data analyst interview questions.
Well, you have come to the right place because we happen to have a good number of questions for you that would help you prepare for the job. So go ahead and take a look at the following questions and see if you can answer them or need to put more effort.
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Data Analyst Interview Questions
1.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 staffsResolve business associated issues for clients and performing audit on dataAnalyze results and interpret data using statistical techniques and provide ongoing reportsPrioritize business needs and work closely with management and information needsIdentify new process or areas for improvement opportunitiesAnalyze, identify and interpret trends or patterns in complex data setsAcquire data from primary or secondary data sources and maintain databases/data systemsFilter and “clean” data, and review computer reportsDetermine performance indicators to locate and correct code problemsSecuring database by developing access system by determining user level of access
3.Mention what are the various steps in an analytics project?Ans-Various steps in an analytics project include–Problem definitionData explorationData preparationModellingValidation of dataImplementation and tracking
4.Mention what is data cleansing?Ans-Data cleaning also referred as data cleansing, deals with identifying and removing errors and inconsistencies from data in order to enhance the quality of data.
5.List out some of the best practices for data cleaning?Ans-Some of the best practices for data cleaning includes,–Sort data by different attributesFor large datasets cleanse it stepwise and improve the data with each step until you achieve a good data qualityFor large datasets, break them into small data. Working with less data will increase your iteration speedTo handle common cleansing task create a set of utility functions/tools/scripts. It might include, remapping values based on a CSV file or SQL database or, regex search-and-replace, blanking out all values that don’t match a regexIf you have an issue with data cleanliness, arrange them by estimated frequency and attack the most common problemsAnalyze the summary statistics for each column ( standard deviation, mean, number of missing values,)Keep track of every date cleaning operation, so you can alter changes or remove operations if required
6.Explain what is logistic regression?Ans-Logistic regression is a statistical method for examining a dataset in which there are one or more independent variables that defines an outcome.
7.List of some best tools that can be useful for data-analysis?Ans-TableauRapidMinerOpenRefineKNIMEGoogle Search OperatorsSolverNodeXLioWolfram Alpha’sGoogle Fusion tables
8.Mention what is the difference between data mining and data profiling?Ans-The difference between data mining and data profiling is that–Data profiling: It targets on the instance analysis of individual attributes. It gives information on various attributes like value range, discrete value and their frequency, occurrence of null values, data type, length, etc.–Data mining: It focuses on cluster analysis, detection of unusual records, dependencies, sequence discovery, relation holding between several attributes, etc.
9.List out some common problems faced by data analyst?Ans-Some of the common problems faced by data analyst are–Common misspellingDuplicate entriesMissing valuesIllegal valuesVarying value representationsIdentifying overlapping data.
10.Mention the name of the framework developed by Apache for processing large data set for an application in a distributed computing environment?Ans-Hadoop and MapReduce is the programming framework developed by Apache for processing large data set for an application in a distributed computing environment.
11.Mention what are the missing patterns that are generally observed?Ans-The missing patterns that are generally observed are–Missing completely at randomMissing at randomMissing that depends on the missing value itselfMissing that depends on unobserved input variable
12.Explain what is KNN imputation method?Ans-In KNN imputation, the missing attribute values are imputed by using the attributes value that are most similar to the attribute whose values are missing. By using a distance function, the similarity of two attributes is determined.
Conclusion –So these are some of the common entry-level data analyst interview questions that you need to know before your interview. We hope that these questions helped you out a bit. Best for your upcoming interview and we hope you get the job!