Data Analyst Interview Questions And Answers | Data Analytics Interview Questions and Answers

if you're planning to land a job in the domain of data analytics this video is for you today we are going to talk about top 10 conceptual questions asked in data analytics interviews do not forget to check out our previous videos that we have done in the series I'll leave a link to them in the description part below now let's jump into today's video this question is about what are the various steps involved in a data analytics project guys this is one of the most basic data analyst interview question the various steps involved in any common analytics projects are as follows you start with understanding the business problem here you define the organizational goals and the plan for a lucrative solution after that you start collecting data now you must gather the right data from various sources and other information based on your priorities the third step is cleaning data over here you clean the data by removing unwanted redundant and uh missing values and make it ready for analysis after the cleaning process is done you start exploring and analyzing your data you can do this by using data visualizations and uh business intelligence tools data mining techniques and predictive modeling to analyze your data finally the last step is interpreting the results you interpret the result to find out hidden patterns future Trends and gain insights so these are all the steps now let's move on to the next question our next question is about the key differences between data analysis and data mining talking about data analysis it involves the process of cleaning organizing and using data to draw meaningful insights in other words it's like cleaning up a messy room you tidy up organize stuff and makes sense of it all the end goal of data analysis is to get valuable insights from the data also data analysis producers results that are far more comprehensible by a variety of audience whereas data mining is used to search for hidden patterns in the data in simple words data mining is like being a detective in a treasure hunt now let me explain this in terms of projects a good example of a data analytics project could be predicting the price of diamonds here you can perform exploratory data analysis of a data set using python libraries such as pandas matplot lib and cabor to explore the data set of diamonds understanding how different features of the diamond like carrot cut color Etc determine the price of the diamond is what your problem statement would be specific to data mining let's say you pick up a kaggle users data set to analyze the preferences of Indians in investing their money idea would be to uh identify hidden patterns like uh which gender is likely to pick specific investment options like mutual funds fixed deposits Government Bond Etc as the data set also contains the age of individual you can use it to know the biys of younger and older people for investing their money by the way guys if you want to read more on these projects or you want to go ahead and do these projects I'll put a link to both in the description part of the video now let's move on to the next question all right this question is what is data validation explain different types of data validation techniques for this first let's understand what data validation is it is a process of ensuring that data is accurate consistent and meets the required quality standards in simple words it's like a set of checks and uh tests that uh data goes through to verify its reliability and integrity now there are many types of data validation techniques that are used today one of them being field level validation field level validation is done across each of the fields to ensure that there are no errors in the data entered by the user think of this like us spell checker for individual words another type is form level validation form level validation is done when the user completes working with the form but before the information is saved in the context of form level data validation a form typically refers to a structured input interface or a document that collects and organizes data from users and form level validation is like reviewing the whole form to make sure it's complete and makes sense before submitting it like proofreading a job application next is data saving validation this form of validation takes place uh when the file or the database record is being saved this is like checking for errors right before you save a document or record uh ensuring everything is in the right format finally search criteria validation search criteria validation is used to check whether valid results are returned when the user is looking for something think of this like using a search engine where you are making sure your Search terms are clear and will give you the right result when you look for something online so this is a basic idea about different types of data validation techniques let's move on to the next question this question is what are outliers and how do you detect and treat outliers let's understand what a outlier is first outlier is an observation in a given data set that lies far from the rest of the observations that means an outlier is vastly larger or smaller than the remaining values in the set in simple words outliers are extreme values that might not match with the rest of the data points now how do you detect outliers well to detect outliers there are quite a few ways first one is a box plot so we know uh that while using a box plot we can easily find outliers the second technique is z score and uh then there's interquartile range so these are the techniques uh to detect outliers now to treat outliers the first thing you can do is drop them where you can just delete all the records that contain outliers the second method is capping outliers data the third method is assigning a new value you can assign the mean median or some other appropriate value to it over here the fourth thing you can do is just try a new transformation like normalization so that is all about outliers let's move on to the next question our next next question is what are different types of sampling techniques to understand this let's first understand what sampling is sampling is a statistical method to select a subset of data from an entire data set to estimate the characteristics of the whole population what this essentially means is that in sampling you take a part of the entire data set and try to analyze only that part and based on the results of that particular sample you will derive conclusion for the entire data set set now for the different types of sampling techniques we have simple random sampling systematic sampling cluster sampling stratified sampling and judgmental sampling you can read about uh these various sampling techniques from this slide also you may take a screenshot if you would want to keep it for your later reference now let's move on to the next question so this question is about hypothesis testing hypothesis testing is a form of statistical inference that uses data from a sample to draw conclusions about a population parameter or a population probability distribution to perform hypothesis testing first a tentative assumption is made about the parameter this assumption is called the null hypothesis and is denoted by H not and then there is an alternate hypothesis called H so let me explain this in simple words let's say you have an assumption the average height in the city of Delhi is 5 ft this becomes your assumption or the null hypothesis your alternate hypothesis let's say is no the average height in the city of Delhi is more or less than 5 ft now you can't measure every person in the city because that's way too many people so you measure a small group let's say 1,000 people uh this small group is uh your sample that is representative of the larger population of Delhi now you use the data from this smaller group for testing your hypothesis and come to a conclusion whether the average height of Delhi is 5T or more and in a nutshell this entire process is what is called as hypothesis testing now let's move on to the next question this question asks what a normal distribution is well a normal distribution also known as a goian distribution or bell curve is a fundamental Concept in statistics and data analysis it represents a specific type of probability distribution that is characterized by a symmetric bell-shaped curve let me double click on these characteristics first is symmetry normal distribution curve is perfectly symmetrical with the mean median and mode all being at the center talking about shape the normal distribution forms a bell shaped curve with the majority of data points concentrated near the mean and progressively fewer data points As you move away from the mean normal distribution is is defined by two parameters the mean which represents the central value and the standard deviation which measures the spread of disperson of the data talking about the spread of data approximately 68% of the data Falls within one standard deviation from the mean 95% within two standard deviations and 99.7% of the data lies between three standard deviations normal distributions are vital in data analysis because many natural phenomena and human-made processes tend to follow this pattern understanding and identifying normal distributions are crucial for various statistical tests hypothesis testing and making predictions in fields like Finance quality control and scientific research now let's move on to the next question this question asks the difference between univariate bivariate and multivariate data first let's understand what these mean univariate data is like uh looking at one thing at a time it's when you are only interested in one variable or one aspect of something for example if you are only thinking about people's height and nothing else that's univariate data bivariate is like looking at two things together in bivariate data you are interested in how two different things are related to each other for example if you are trying to figure out if there's a connection between temperature and ice cream sales that's by variate data finally multivariate is like looking at at many things all at once in multivariate data you are not just focused on two things you are studying three or more things together for example if you want to understand how the popularity of four different advertisements on a website depend on various factors like age gender and location that's multivariate data and these are the differences that we have documented for you between univariate bivariate and multivariate data you may take a screenshot of this particular table for your uh later reference now let's move on to the next question this question ask about the differences between underfitting and overfitting as usual we'll first understand what underfitting and overfitting is a statistical model or a machine learning algorithm is set to have underfitting when a model is too simple to capture data complexities in simple words underfitting is like having a tool that's too basic for the job this tool is too simple and doesn't understand the tricky parts of the job it won't work well and a statistical model is set to be overfitted when the model does not make accurate predictions on the test data when a model gets trained with too much data it starts learning from the noise and inaccurate data entries in our data set uh in simple terms overfitting is like studying so much detail that you get confused and uh make mistakes and this table shows the differences between the two overfitting and underfitting screenshot this for your future reference now let's move on to the next question this question asks what are the common problems that data analyst encounter during analysis well the problem can be faced in four different steps first with the collection of data this is because data can be scattered across different sources making it difficult to collect and consolidate this data moreover data may be incomplete or inaccurate requiring cleaning and pre-processing it may be sensitive data requ requiring careful handling and storage next challenge comes at storing this data data can be huge requiring scalable uh Storage Solutions Plus it needs to be backed up and protected from loss or corruption not just that data needs to be accessible to authorized users while protecting it from unauthorized access there are specific uh challenges to process data as well data can be complex and difficult to analyze requiring special I tools and skills along with that data processing can be timec consuming and computationally expensive and the results of data processing need to be interpreted and communicated effectively finally data quality and governance data quality is essential for accurate and reliable analysis and data governance ensures that data is managed and used responsibly so guys that's all we had for you today if you have any more questions let us know in the comments and we'll get back to you subscribe our channel for more such interesting data Tech content good luck to you bye

Channel: Analytics Vidhya

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Keywords: analytics vidhya, data science analytics vidhya, analytics vidhya data science

Id: PI1rTHLQ1ok

Length: 14min 14sec (854 seconds)

Published: Thu Nov 09 2023