Shadowing Practice: Data Analyst Interview Questions and Answers | Data Analytics Interview Questions | Edureka - Learn English Speaking with Video

the job market for data analysts is highly competitive therefore the

interview process can be challenging during an interview an interviewer will assess your technical skills problem solving abilities

and your experience with data analysis the interviewer may ask questions about specific projects that you have worked on,

how you handle missing or incomplete data, your experience with statistical methods, and how you have used data to inform decision -making in the past.

To excel in the interview, it is essential to be prepared with answers to common data analyst interview questions.

To answer those questions effectively, you should prepare examples and additional details to demonstrate your understanding of the subject matter.

Additionally, it is crucial to be well versed in the latest developments and advancements in the field of data analysis.

Hi everyone, I am Elton from Edureka

and in this video I will take you through some of the most commonly asked data analyst interview questions.

But before we go ahead, make sure to hit the like button and subscribe to our channel.

You can also hit the bell icon to receive regular updates from here.

We also have a lot of training programs and certification courses on our website.

So if you are interested in it, do check out the links given in the description.

So let's not waste any more time and see our agenda for today.

We will start with the most generally asked interview questions for a data analyst interview.

After that we will see interview questions on statistics, interview questions on python and interview questions on SQL.

We will cover around 30 questions in total.

So let's head over to the first part which is the general interview questions.

So the first question for today is to mention the difference between data mining and data profiling.

You can think of data mining as the process of sorting through large datasets to identify patterns

and relationships that can help you solve business problems through data analysis.

The thing over here is that data mining will help you discover relevant information that has not been identified before,

whereas data profiling is done to evaluate a dataset for its uniqueness, logic and consistency.

This basically means that you're checking your dataset to see if it's valid for your use case.

The second difference is that in data mining, raw data is converted into valuable information.

This can either be done through scraping data from the internet or filtering the existing dataset.

But in data profiling, you cannot identify inaccurate or incorrect data values.

Now let's head over to the second question.

Define the term data wrangling in data analytics By definition,

data wrangling is the process of transforming and mapping data from one data form to another format.

Basically, data wrangling is the process wherein raw data is cleaned, structured, and enriched with the intent of making it more valuable for data analytics.

This 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 data.

Thereafter, it gets ready to be used with another data set.

Our third question for today is, what are the various steps involved in a data analytics project?

This is one of the most basic data analyst interview questions.

The various steps involved in any common analytics projects are as follows.

You start with understanding the problem.

Understand the business problem, define the organizational goals, and plan for a lucrative solution.

After that, you will start collecting data.

You will gather the right data from various sources and other information based on your priorities.

The third step is cleaning data.

Over here, you will clean the data to remove unwanted, redundant, and missing values and make it ready for analytics.

After the cleaning process is done, you will start exploring and analyzing the data.

You can do this by using data visualization and business intelligence tools, data mining techniques, and predictive modeling to analyze the data.

The last step is interpreting the results.

You will interpret the results to find out hidden patterns, future trends, and then gain insights.

Our fourth question for today is what are the common problems that data analysts encounter during analysis?

One of the biggest problems that data analysts face is handling the data, right?

It starts with handling redundant data, collecting the right data at the right time, handling data purging and storage problems,

keeping that data secure, and then handling compliance issues.

These are some of the most common problems that data analysts face, and you should have a solution for this when you attend a data analyst interview.

With that, let's head over to the fifth question, that is, list out some of the common tools that you have worked with.

Now, this question can be considered as a pattern of skillset most data analysts should have.

Let's start with database systems.

In database systems, you will need to know MySQL, MongoDB, Apache Cassandra, and CouchDB.

These are some of the most commonly used database tools.

After that, a data analyst also needs to know how to report and create dashboards, right?

For that, you will need Excel, Tableau, and Power BI.

A data analyst also needs to know how to code for

that most data analysts use python r

and spss these are the most commonly used programming languages

and you will need to have a thorough understanding of them finally we have presentations

and reports for this you can use powerpoint

and keynote during the interview you can also give some examples

and use cases of how you've used these tools before this

can help the interviewer understand your knowledge about these tools the next question is what is the significance of exploratory data analysis,

abbreviated as EDA.

The first thing over here is that exploratory data analysis can help you get a better understanding of your data.

You can use different tools and techniques to understand how your data works internally.

The second point is that you can have more confidence in your decisions.

Exploratory data analysis is based on statistics, and the decisions you make will be based on that.

So if your research is correct, then you have more probability of your decision being right.

EDA can also help you refine feature selection during modeling.

You can avoid overfitting or underfitting your model, and this analysis can help you choose the right model for your use case.

EDA can also help you discover hidden trends in your data.

This can help you find unique information that can help you make better decisions

and with that we have the next question which is explained descriptive predictive

and prescriptive analytics the main point about descriptive analytics is

that it provides insights into the past to understand what has happened you should understand

that descriptive analytics is used to understand data

and draw conclusions from it you use techniques such as hypothesis testing to find out whether certain assumptions are true.

Now, predictive analytics is used to understand the future to answer the questions on what could happen.

It uses available data to predict the future based on patterns.

Prescriptive analytics on the other hand suggests various courses of action that answers what you should do.

You can think of this as a doctor's prescription.

If you have a problem, then prescriptive analytics can give you a solution to it.

Now, all three of these are used in different use cases.

So the second difference can be about the techniques that you use in these use cases.

Descriptive analytics uses data aggregation and data mining techniques, whereas predictive analytics uses statistical models and forecasting techniques,

whereas prescriptive analytics uses simulation algorithms and optimization techniques to advise possible outcomes.

Now, the next question is, what are the different types of sampling techniques used by data analysts?

To understand this, let's first understand what sampling is.

Sampling is a statistical method to understand a subset of data

from an entire dataset to estimate the characteristics of the whole population.

What this essentially means is that, in sampling, you take a part of the entire dataset and and you try to analyze that, and based on the results of that particular sample,

you will derive conclusions for the entire dataset.

Now, for the different types of sampling techniques, we have simple random sampling,

systematic sampling, cluster sampling, stratified sampling, and judgmental sampling.

100

Now we have the question, describe univariate, bivariate, and multivariate analysis.

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Univariate analysis is the simplest and easiest form of data analysis with the data being analyzed contains only one variable.

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That is why it's called uni, right?

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So with that we can see what bivariate is.

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Bivariate analysis basically means you are comparing two variables.

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This can be explained using correlation coefficients, linear regression, logistic regression, scatter plots, and box plots.

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Now we have multivariate analysis.

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This kind of analysis involves the analysis of three or more variables to understand the relationship of each variable with other variables.

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Our tenth question and the last question for this section is what are the best methods for data cleaning?

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So the first thing is to create a data cleaning plan by understanding where the common errors are taking place

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and after that you will keep all the communications open.

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You should also remember that before you start working with data, You should identify and remove all the duplicates.

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This will lead to an easy and effective data analysis process.

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You should also focus on the accuracy of data.

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You can set cross -field validation, maintain value types of data, and implement mandatory constraints.

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You can also normalize the data at the entry point so that it is less chaotic.

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You need to ensure that all information is standardized.

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This will lead to fewer errors on entry.

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With that, we are done with the general interview questions that we face in data analyst interviews, and next we can see the interview questions on statistics.

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The first question over here is how you handle missing values in a dataset.

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Now if you've handled data before, then you know that missing values is one of the most commonly found problems in a dataset.

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To handle this, the first method is list -wise deletion, where an entire record is excluded from analysis if any single value is missing.

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The second method is average imputation also called as mean imputation

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where you fill in the missing values with the average of that particular value.

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The third method is regression substitution followed by the fourth method which is multiple imputations.

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Now the second question

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that we have in this section is explain the term normal

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Normal distribution is one of the most basic concepts in graph theory

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and it refers to a continuous probability distribution that is symmetric about the mean.

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Some of the important points over here is that you should remember that the mean,

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median and mode of a normal distribution is equal and all three of them are located in the center of the distribution.

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Now let's head over to the next question which is, what is time series analysis? By definition,

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time series analysis is a statistical procedure that deals with ordered sequence of values of a variable at equally spaced time intervals.

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Basically, time series data is this data that is collected at adjacent time periods.

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That is why you can see a correlation between the observations.

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This feature distinguishes time series data from cross -sectional data.

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I have added two graphs below for you to understand this concept better.

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The next question is explain the difference between overfitting and underfitting.

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Now this question is one of the most basic questions that you can find on building models.

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You say that your model is overfitting

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when it trains well on the training dataset but the performance drops drastically in the testing dataset.

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This happens when your model learns random fluctuations and noise from your training dataset.

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Underfitting occurs when your model does not really train well on the training dataset

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and performs poorly on both training and testing dataset.

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This occurs when your training dataset is so small

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that it can't really derive any conclusions or if you're developing a linear model with a non -linear dataset.

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After this we have the question, how do you treat outliers in dataset?

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Now to answer that question, the first thing that you need to understand is what is an outlier?

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Basically, an outlier is just a data point that is distant from other similar points.

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This may occur due to variability in the measurement or may indicate experimental errors.

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Now for you to treat outliers, the first thing that you can do is drop outliers where you can just delete all the records

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that contain outliers and the second method is capping outliers data.

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The third method is assigning a new value.

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You can assign the mean, median or some other appropriate value over here.

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The fourth thing that you can do is just try a new transformation.

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And with that, we come to the next question which is What are the different types of hypothesis testing?

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A hypothesis is basically just an educated guess about a specific parameter or population.

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And hypothesis testing can be described as the procedure used by statisticians and scientists to accept or reject a statistical hypothesis.

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Now, in hypothesis testing, you have two different kinds of hypothesis.

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The first thing is a null hypothesis, and the second one is an alternative hypothesis.

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Now you can also get another question based on this concept.

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What is the difference between type 1 and type 2 errors?

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The type 1 error occurs when the null hypothesis is rejected even if it is true,

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whereas the type 2 error occurs when the null hypothesis is not rejected even if it is false.

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Now let's end this section over here and head over to the interview questions on python.

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The first question in this section is what is the correct syntax for reshape function in numpy.

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For you to use the reshape function all you need to do is use two parameters.

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The first parameter is the array name and the second parameter is the shape of that array.

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Now, I have attached a snippet that uses the reshape function over here.

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Go through it and type the output in the comment section.

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What are the different ways to create a data frame in pandas?

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For this question, you can list down the two ways to create data frames in pandas.

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The first method is by initializing list and the second one is initializing a dictionary.

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For you to create a pandas data frame using lists, the first thing that you need to do is type pandas .dataframe and then give it two parameters,

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data and columns.

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Now, the data parameter consists of a list containing different lists.

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This will later be converted into your records.

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And the second parameter, columns, will consist of your column names.

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Initializing a pandas data frame using dictionary is even simpler.

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All you need is a dictionary with values which you can convert into a data frame.

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The syntax is just pandas .dataframe, and in the parameters, you will mention the dictionary's name.

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For the next question we have, there are two arrays, A and B.

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Stack the arrays A and B horizontally using the NumPy library in Python.

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Now, for this question, there are two ways you can approach it.

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The first way is the concatenate method, where you will type numpy .concatenate A and B, and then mention the axis.

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The second method is the HedgeStack method.

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This is another NumPy method where in the parameters, you will provide a list with the values A, B.

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Next question is how can you add a column to a panda's dataframe?

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Doing this is extremely simple.

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All you need to do is type the dataframe's variable name followed by the column name.

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This is then assigned values from a list.

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So let's move on to the next question, which is, how will you print 4 random integers between 1 and 15 using numpy?

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This is again a very simple question.

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If you refer to the snippet that I have given you over here, you can see that in the second line I have used numpy .random .randent and I have given a few parameters.

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This randent function can help you generate your numbers.

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Now, the randent function itself has 3 parameters, right?

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In this method, the first value represents the start point.

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You won't find any numbers being generated before this point.

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The second value is the endpoint.

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You won't find any values being generated after this number.

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The third value that I mentioned over here is the number of integers that we want from this method.

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Now let's move on to the next question.

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Suppose there is an array that has values 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 how will you display the following values from the array?

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1, 3, 5, 7, 9.

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Now if we observe the values that need to be extracted, we can see that all of those numbers are just odd numbers, right?

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So this question again refers to some of the basics of programming.

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All you need to do is create a list with the values given at the start of the question

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and then check for the odd numbers.

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You can do this by using the modulus operator.

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Now the next question is, suppose there is an array with the following values.

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Extract the value 8 using 2d indexing.

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To answer this question, all you need to do is start by creating your numby array.

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And over here you will pass the same values as given in the question.

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You can do this as follows.

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Now if you check your array, then you can see that the value 8 lies in the third row and the second column, right?

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But the indexing in Python starts from zero.

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So the third row becomes the second row and the second column becomes the first column.

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So if you type those indexes, you will definitely get your answer.

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And then we have the next question.

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How do you select specific columns from a data frame?

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Now this is also a basic question.

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You can do this with a single line of code.

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You will start by typing the data frame name and you will open two sets of square brackets inside one another.

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Once you do this, you can type the names of the columns that you want inside the inner square brackets.

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With that, we can come to the fourth section of this video which is data analyst interview questions on SQL.

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The first question in this section is what is the difference between where clause and having clause in SQL.

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Let's do this with a table the first point is

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that where clause operates on row data which means that

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when you use a where clause you are only operating on

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individual rows of data the having clause on the other hand

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operates on aggregated data in the where clause the filtering occurs before the groupings are made in the data.

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But the HAVING clause is used to filter values from a group of data.

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In the WHERE clause, you can't use aggregate functions.

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But HAVING clause allows you to do that.

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The second question is a very common sort of question.

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Is the following SQL query correct?

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Now in the interview, the interviewer can ask any kind of SQL query.

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You need to make sure that you have a thorough understanding of SQL before attempting to answer these questions.

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So the query is as follows.

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Select customer ID

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and order date from year as order year from order where order year is greater than or equal to 2023.

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Now is the SQL query correct?

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Pause the video over here and analyze the question.

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You can type your answers in the comment section down below. So, what's your answer?

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If you answered that the query is correct, then you are wrong.

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The query stated above is incorrect, as we cannot use the alias name while filtering data using the WHERE clause.

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It will throw an error.

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The correct form of the SQL query is as follows.

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All you need to do is pass the order date through here, not directly.

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Now with that, let's see the next question which is, what is subquery in SQL?

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A subquery in SQL is just a query within another query.

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It is also known as a nested query or an inner query.

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Subqueries are just used to enhance the data which is queried by the main query.

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These queries come in two types, correlated and non -correlated.

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You can find more information about correlated and non -correlated subqueries in the blog that has been given in the description.

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Next, we have what is the difference between delete and truncate statements.

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The delete command can delete rows in your table.

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You can delete individual rows or a group of rows.

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Whereas the truncate statement is used to delete all the rows from the table.

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You can roll back to your data after using a delete statement.

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You can think of rollback as a save point in SQL.

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With rollback, you can navigate to a previous version of your data.

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This feature is not available with the truncate statements.

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The third difference is the category of commands that these two commands belong to.

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Delete is a DML command whereas truncate is a DDL command.

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The fourth difference between these commands lie within speed.

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The delete command is slower than the truncate command.

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Let's now head over to the next question which is what do you understand by query optimization?

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Query optimization is basically a phase that deals with optimizing the efficiency of your query.

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With query optimization, your outputs are generated faster, a large number of queries can be executed in less time,

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and query optimization reduces the time and space complexity of your queries.

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And with that, we have reached the end of this session.

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This video is an introductory video that gives you a basic overview of the interview questions.

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I highly recommend that you also watch our in -depth videos that talk about each of these sections separately.

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The links to those videos are provided below.

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I really do hope to see you there.

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And until then, happy learning.

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I hope you have enjoyed listening to this video.

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Please be kind enough to like it

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and you can comment any of your doubts and queries and we will reply them at the earliest.

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Do look out for more videos in our playlist and subscribe to Edureka channel to learn more.

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Happy learning!

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Vocabulario y notas de pronunciación para esta lección

Esta lección de conversación se basa en el vídeo “Data Analyst Interview Questions and Answers”. Las palabras que más se repiten: value, interview, analysis, dataset, query. Este vídeo tiene 280 frases y 3805 palabras para practicar shadowing. La parte hablada dura 23:30. El hablante habla a un ritmo natural de unas 162 palabras por minuto, cercano a una conversación cotidiana. El 82 % de las palabras está entre las 3.000 más comunes del inglés; conviene repasar el resto antes de empezar.

Vocabulario clave de este vídeo

15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:

Palabra	Pronunciación	Significado
query sustantivo	/ˈkwɪɹ.i/	consulta, pregunta
analytics sustantivo	/ˌæn.əˈlɪt.ɪks/	analítica
hypothesis sustantivo	/haɪˈpɒθɪsɪs/	hipótesis, hipótesi
parameter sustantivo	/pəˈɹæm.ə.tɚ/	parámetro
delete verbo	/dɪˈliːt/	deletear
array sustantivo	/əˈɹeɪ/	atavío, galas
variable adjetivo	[ˈvæɹ.i.ə.bl̩]	variable
occur verbo	/əˈkɜː/	ocurrir
whereas	/(h)wɛɹˈæz/	mientras que (locution)
column sustantivo	/ˈkɒləm/	columna, pilastra
statistical adjetivo	/stəˈtɪs.tɪ.kəl/	estadístico
optimization sustantivo	/ˌɑptəmaɪˈzeɪʃən/	optimización
commonly adverbio	/ˈkɑmənli/	comúnmente
filter sustantivo	/ˈfɪltɚ/	filtro
generate verbo	/ˈd͡ʒɛn.ə.ɹeɪt/	generar

Phrasal verbs que vas a escuchar

Palabra	Pronunciación	Significado
find out verbo		averiguar, anoticiarse
check out verbo		investigar, chequear
go ahead verbo	/ˌɡoʊ əˈhɛd/	venga, adelante
go through verbo		atravesar

Frases que vale la pena repetir

Frases cortas y completas del vídeo que puedes reutilizar en la conversación diaria:

Let's start with database systems.
You can think of this as a doctor's prescription.
To understand this, let's first understand what sampling is.
How do you select specific columns from a data frame?

Gramática en este vídeo

Las estructuras que más usa el hablante, con las palabras exactas del vídeo:

Estructura	En el vídeo
Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora	have worked · has not been identified · has happened
Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace	has not been identified · is done · is converted
Oraciones de relativo who / which + oración — información extra sobre una persona o cosa	part which is · question which is · method which is

Pronunciación a tener en cuenta

El hablante usa 8 contracciones y formas reducidas, como can't, won't, you're. Dilas en su forma corta, tal como las oyes.

Los sonidos de “th”: hypothesis /haɪˈpɒθɪsɪs/, thorough /ˈθʌɹoʊ/, gather /ˈɡæðɚ/, thereafter /ˌðɛɹˈæf.tɚ/
Los sonidos de “sh” y “zh”: optimization /ˌɑptəmaɪˈzeɪʃən/, conclusion /kənˈkluːʒən/, dictionary /ˈdɪk.ʃəˌnɛ.ɹi/, correlation /ˌkɔɹ.əˈleɪ.ʃən/, essentially /ɪˈsɛnʃəli/
Palabras largas — cuida el acento: analytics /ˌæn.əˈlɪt.ɪks/, hypothesis /haɪˈpɒθɪsɪs/, parameter /pəˈɹæm.ə.tɚ/, statistical /stəˈtɪs.tɪ.kəl/, optimization /ˌɑptəmaɪˈzeɪʃən/

Sonidos difíciles para hispanohablantes:

s + consonante al inicio — sin añadir una “e” delante: statistical /stəˈtɪs.tɪ.kəl/, stack /stæk/, snippet /ˈsnɪpɪt/
/v/ — no es /b/: los dientes tocan el labio inferior: variable [ˈvæɹ.i.ə.bl̩], descriptive /dəˈskɹɪptɪv/, interviewer /ˈɪntɚvjuɚ/, convert /kənˈvɝt/, solve /sɒlv/
/z/ — sonora, no /s/: clause /klɔːz/, whereas /(h)wɛɹˈæz/, optimization /ˌɑptəmaɪˈzeɪʃən/, columns /ˈkɑləmz/, analyze /ˈæn.əˌlaɪz/

Cómo practicar con este vídeo

Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
Grábate y compara con el original, prestando atención a palabras como query, analytics, hypothesis.

¿Qué es la Técnica de Shadowing?

Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.

Técnica de shadowing: lee la guía completa paso a paso →

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