SQL Interview Questions for Data Analyst | Top 20 SQL Interview Questions and Answers | Simplilearn

welcome back to our Channel where we dive deep into the world of data analysis in today's video we are going to help you prepare for your SQL data analyst job interview SQL or structured query language is a critical tool for data professionals and mastering it is key to Landing that dream job we'll be covering some common SQL data analyst interview questions and providing detailed answers to help you succeed whether you are just starting your career in data analysis or looking to take the next step this video is packed with valuable insights to boost your confidence and Excel in your interviews so let's get started and before we begin if you are someone who is interested in building a career in data analysis by graduating from the best universities or a professional who elicits to switch careers with data analyst by learning from the experts and try giving a show to Simply learns Purdue University post graduate program in data analytics in collaboration with IBM the course link is mentioned in the description box below that will navigate you to the course page with the program and the complete overview of the program is being word and remember to hit the like button subscribe button and tap the notification Bell so you are always in the know which video will come up next so let's Dive Right In we'll start with the interview questions and our first question is mention the differences between Data Mining and data profiling so data mining it's the process of discovering relevant information that has not yet been identified before and if we see about data profiling data profiling is done to evaluate a data set for its uniqueness logic and consistency and in data mining raw data is converted into valuable information and in data profiling it cannot identify inaccurate or correct data values so this is the main differences between Data Mining and data profiling moving on to the next question and the next question is Define the term data wrangling in data analytics so data wrangling it's the process wherein row data is cleaned structured and enriched into a desired usable format for better decision making it involves discovering structuring cleaning and reaching validating and analyzing data the process can turn and map out large amounts of data extracted from various sources into a more useful format techniques such as merging grouping concatenating joining and sorting are used to analyze the data thereafter it gets ready to be used with another data set so this was about data wrangling in data analytics moving on to the next question that is what are the common problems that data analyst encountered during analysis so now we'll discuss about the common problems and the common problems include in data analytics project are the first is handling duplicates and the next is collecting the meaningful write data and the right time so the first is how to handle the duplicate data the second is get the right data and the right time and there are more problems that are handling data purging and storage problems and then we have making data secure and dealing with compliance issues so these are the common problems that data analysts face or encounter so moving on to the next question that are what are the best methods for data cleaning so here we will discuss three or four methods and starting with the first is created data cleaning plan by understanding with the common errors take place and keep all the communications open before working with data identify and remove the duplicates this will lead to an easy and effective data analysis process and the next is focus on the accuracy of the data set Cross Field validation maintain the value types of data and provide mandatory constraints number four normalize the data at the entry point so that it is less chaotic you will be able to ensure that all information is standardized leading to fewer errors on Entry so this was the best methods for data cleaning so you should follow these steps and now moving on to the fifth question that is what are the various steps involved in any analytics project so this is one of the most basic data analyst interview questions the various steps involved in any common analytics projects are as follows the number one understanding the problem understand the business problem Define the organizational goals and plan for a lucrative solution next is collecting data gather the right data from various sources and other information based on your priorities and then cleaning data clean the data to remove unwanted redundant and missing values and make it ready for analysis and then we have exploring and analyzing data that is pure data visualization and business intelligence tools data mining techniques and predictive modeling to analyze data and then interpreting the results interpret the results to find out hidden patterns future Trends and gain insights so this was all about the various steps involved in any analytics project and moving on to the next question that is what is the significance of exploratory data analysis IDI so moving on to the answer we'll find out significance that is exploratory data analysis Eda helps to understand the data better it helps to obtain confidence in your data to a point where you are ready to engage a machine learning algorithm it allows you to refine your selection of feature variables that will be used later for model building you can discover hidden Trends and insights from the data so these are the significance of Eda that is exploratory data analysis moving on to the next question that is explain descriptive predictive and prescriptive analytics so we'll start with descriptive so it provides insights into the past to answer what has happened and whereas in predictive it understands the future to answer what could happen and if we see about prescriptive it suggests various courses of action to answer what should you do and another point for descriptive is it uses data aggregation and data mining techniques and for predictive it uses statistical models and forecasting techniques and for prescriptive it uses simulation algorithms and optimization techniques to advise possible outcomes and now just listen to an example that is for descriptive an ice cream company can analyze how much ice cream was sold which flavors were sold and where the more or less ice cream was sold than the day before and for predictive an ice cream company can analyze how much ice cream was sold which flavors were sold and whether more or less ice cream was sold than the day before and for prescriptive lower prices to increase the sale of ice cream produce more or fewer quantities of a specific flavor of ice cream so this was all about descriptive predictive and prescriptive analytics moving on to the next question that is what are the different types of sampling techniques used by data analyst so we will talk about the sampling first sampling is a statistical method to select a subset of data from an entire data set to estimate the characteristics of the whole population and there are majorly five types of sampling methods the number one is simple random sampling number two systematic sampling number three cluster sampling number four stratified sampling number five judgmental or purposive sampling so this was all about the sampling techniques used by data analyst moving on to the next question that is describe univariate by variate and multivariate Analysis so we'll just describe all the three and we'll start with univariate so we'll start with an example or just we will have a little definition about it let us univariate analysis is the simplest and easiest form of data analysis with the data being analyzed contains only one variable example studying the heights of players in the NBA and univariate Analysis can be described using Central tenancy dispersion quartiles bar charts histograms pie charts and frequency distribution tables and moving on to the bivariate the bivariate analysis involves the analysis of two variables to find causes relationships and correlations between the variables for example analyzing the sale of ice creams based on the temperature outside the bivariate analysis can be explained using correlation coefficients linear regression logistic regression Scatter Plots and box plots now talking about the multivariate analysis the multivariate analysis involves analysis of three or more variables to understand the relationship of each variable with the other variables example analyzing Revenue based on expenditure and multivariate Analysis can be performed using multiple regression factor and Analysis classification and regression trees cluster analysis principle component analysis dual access charts Etc so this was all about the three univariate bivariate and multivariate Analysis and move on to the next question that is how can you handle missing values in a data set so this is one of the most frequently Asked data analyst interview questions and the interviewer expects you to give a detailed answer here and not just the name of the method there are four methods to handle missing values in a data set so we'll see one by one the first is list wise deletion in the listwise deletion method an entire record is excluded from analysis if any single value is missing the next is average imputation take the average value of the other participants responses and fill in the missing value the next is regression substitution you can use multiple regression analysis to estimate a missing value now before multiple imputations it creates plausible values based on the correlations for the missing data and then averages the simulated data sets by incorporating random errors in your predictions so these are the four methods to handle missing values in a data set now moving on to the next question that is explain the term normal distribution normal distribution refers to a continuous probability distribution that is systematic about the mean in a graph normal distribution will appear as a bell curve so the mean median and mode are equal all of them are located in the center of the distribution and moving on to the next that is 68 of the data Falls within one standard deviation of the mean the next is 95 of the data lies between two standard deviations of the mean and then we have 99.7 percent of the data that lies between three standard deviations of the mean so this was all about the term normal distribution now moving on to the next question that is what is time series analysis so time series analysis is a statistical procedure that deals with the ordered sequence of values of a variable and equally spaced time intervals time series data are collected at adjacent periods so there is a correlation between the observations this feature distinguishes time series data from cross-sectional data so this was about the time series analysis now moving on to the next question that is how is overfitting different from underfitting so this is another frequently Asked data analyst interview questions and you are expected to cover all the given differences so we'll start with the differences between overfitting and under fitting so in overfitting the model trains the data value using the training set and while in underfitting the model neither trains the data well nor can generalize to new data in overfitting the performance stops considerably over the test set and in under fitting it performs poorly both on the train and the test set in overfitting it happens when the model learns the random fluctuations and noise in the training data set in detail in under fitting this happens when there is lesser data to build an accurate model and when we try to develop a linear model using non-linear data and now I have an announcement for you guys that is if you are someone who is interested in building a career in data analysis while graduating from the best universities for a professional who elicits to switch careers with data analysis by learning from the experts then try giving a show to Simply learn skeleton data analytics bootcamp in collaboration with IBM The Bootcamp link is mentioned in the description box that will navigate you to the course page where you can find a complete audio of the program being offered and now moving on to the next question that is 14 question and our question is what are the different types of hypothesis is testing so this is also a very frequently Asked question and hypothesis testing is the procedure used by statisticians and scientists to accept or reject statistical hypothesis there are mainly two types of hypothesis testing number one is null hypothesis number two is alternative hypothesis so in null hypothesis it states that there is no relation between the predictor and outcome variables in the population and h0 denotes it for example there is no association between a patient's BMI body mass index and diabetes and about talking about alternative hypothesis it states that there is some relation between the predictor and outcome variables in the population it is denoted by H1 and for example there could be an association between a patient's BMI and diabetes so this was about the two hypothesis testing move on to the next question that is explain the type 1 and type 2 errors in statistics so moving on in hypothesis testing uh type 1 error occurs when the null hypothesis is rejected even if it is true it is also known as a false positive so in type 2 it occurs when the null hypothesis is not rejected even if it is false it is also known as a false negative moving on to the next question that is what is the difference between count count a count blank and countif in Excel so these are the functions in Excel and we'll see the differences so count function Returns the count of numeric cells in a Range whereas count a function counts the non-blank cells in a range and count blank function gives the count of blank cells in a range and countif functions return the count of values by checking a given condition so these are the differences between all the four functions and moving on to the next question that is can you provided dynamic range in data source for a pivot table so the answer is yes you can provide and you can provide a dynamic range in data source of pivot tables so to do that you need to create a named range using the offset function and base the pivot table using a named range constructed in the first step so this was about how you can provide a dynamic range in data source and we'll go to the next question that is what is the difference between a very close and a having close in SQL so these are the two Clauses that is where close and having close and now we'll see the differences between them so where close operates on Raw data whereas the having close operates on aggregated data and in the where close the filter occurs before any groupings are made and in having close it is used to filter values from a group and in where close aggregate functions cannot be used whereas in having close aggregate functions can be used and now we'll see the syntax for both that is where close and having close so syntax of where Clause that is you could just start with select the name of the columns column one column 2 and then from table name and then the condition where condition and talking about the having close you could just select column names write the column names from the table name and where condition and then Group by column names having that condition and order by from which you want to sort or filter that is order by so these are the syntax and we have seen the difference between a where clause and a having close and now move on to the next question that is explain how vlookup Works in Excel so we look up it's also a very general question so vlookup is used when you need to find things in a table or a range viral so we look up accepts the following four parameters the number one is lookup underscore value so the value to look for in the First Column of a table for that we use lookup underscore value and we use the table from where you can extract value and column index col underscore index the column from which to extract value and then we have range underscore lookup so here true equal to approximate match that is default that is approximate values are matched and if it is false that means it is a exact match so this is all about the vlookup and moving on to the next question that is 20th question and that is how can you handle missing values in a data set so this is one of the most frequently Asked data analysis interview questions and the interview expects you to give a d date answer here and note just the name of the methods there are four methods to random missing values the number one is list wise deletion in the listwise deletion method an entire record is excluded from analysis if any single value is missing and then we have average inputation take the average value of the other participants responses and fill in the missing value then we have regression substitution you can use multiple regression analysis to estimate a missing value and then we have multiple imputations it creates plausible values based on the correlations of the missing data and then averages the simulated data sets by incorporating random errors in your predictions so this was all about how you can handle missing values in a data set and thanks for joining us today as we talked and tackled some essential SQL data analyst into equations we hope you find this video informative and valuable in your job preparation Journey and now let's take a minute to hear from our Learners who have experienced massive success in their careers upskilling is extremely essential for any working professional nowadays after completing the professional certificate program in data analytics from Simply learn in partnership with Purdue University my career took a significant turn I was able to change my career domain from teaching kids to providing Supportive Services to adults at the Department of Rehabilitation in San Francisco the experience that I have would simply learn was amazing The Faculty was very encouraging excited about my future and I encourage all professionals and if you have any more questions or want to see more content like this please let us know in the comment section below don't forget to like share and subscribe for more insightful videos on data analysis and best of luck with your upcoming interviews and remember with the right knowledge and practice you have got this see you in the next video get simplified get ahead staying ahead in your career requires continuous learning and upskilling whether you're a student aiming to learn today's top skills or a working professional looking to advance your career we've got you covered explore our impressive catalog of certification programs in Cutting Edge domains including data 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Id: Il82d2bAmr0

Length: 21min 0sec (1260 seconds)

Published: Fri Aug 25 2023