In today’s modern era of high tech environment, the demands of the big industries for data analysts have subsequently increased.

Nowadays classrooms are filled for the study and analysis of data, as the new generation understands its value and knows that studying data analysis or improving their skill in this field will lead them to high paying jobs.

If you are a fresher and skilled in data analysis and you have been called for an interview, then you need to prepare well for the data analyst interview questions, so that the interviewer who is taking your interview notices your spark and selects you for the deserving post.

For the preparation of the interview for this post you need to start with the basics. Let’s take a look how to start with the preparation of the interview.

data analyst interview questions

Image Source: Diverbi

Analysis Skills and Techniques Every Data Analyst Should Know:

The following mentioned are few data analyst skills and techniques that any data analyst should possess and also top data analyst roles and responsibilities.

1. You should be well versed with the Data Analyst responsibilities:

  • Proper coordination with the customers and the working staff.
  • Supporting the data analysts if in problem. Work as a team.
  • Resolve the issues related to audit on data analysis.
  • Interoperating the data and analyzing the results by using skilled techniques.
  • Provide ongoing daily reports.
  • Work in coordination of the management and information needs.
  • Interpret, analyze and identify different patterns in the complex data sheets.
  • Maintain the data base on the data system.
  • Acquire data from primary or secondary data sources.
  • Filter and clean the data for balanced computer reports.
  • Resolve code related problems by keeping a regular check on the data reports.
  • Securing the data base and maintaining a user access data base for high security purpose.

2. You should have the proper knowledge of all the technical aspects for the post of Data Analyst:

  • Knowledge on Business packages and Reporting Packages.
  • Different programming languages.
  • Different types of data base.
  • Very strong skill of analyzing, collecting and organizing big data sheets.
  • The work should be done with accuracy.
  • Technical knowledge of :
    a) Database Design
    b) Different Data Models
    c) Data Mining
    d) Different segmentation techniques
  • Good knowledge of statistical packaging to handle big database sheets.

So, we have discussed about what you should keep in mind before preparing for the interview for the post of Data Analyst.

Now the question arises that being a fresher and having no knowledge of the questions that can be asked by the interviewer, how to become a data analyst?

10 Common Data Analyst Interview Questions and Answers:

The following mentioned are few top interview questions on data analysis.

1) Outline the different steps for a data analytics Project?

The different steps involved for a Data Analytics Project are:

Problem Definition:

Firstly you need to know the business problem, what is the business context related to it.

Generally in this industry your client gives you a whole data sheet and asks you to do something significant with it.

In these types of cases you need to thoroughly go through the data and look out for the problem. Although you can ask to the client for the specific problem he is suffering from that needs to be tackled.

This is the first step to look for the problem and rectify it. Then you need to convert the business problem into analytical one.

Data Exploration:

After understanding the problem of the client, now you need to find the root cause of the problem by going through the data provided by the client.

It is every time important to go through the data thoroughly to search the minor problems in it.

Remember that with every set of data provided by the client, you need to start the analysis from the beginning.

Preparation of DATA:

When you have all the problems lined up in a sequence with all the details, now you have to start molding it in the right direction. You need to rectify the:
a) Missing values
b) Detect outliers
c) Variable transformation
d) Creating binary transformation if required


When you have prepared the corrected data, you need to start with the modeling process.

Modeling is a type of repetitive process where you need to run the made model data and search for mistakes, and imply the improvement and then again find the mistake and again imply for the correction, like this the process goes on till you don’t get the perfect result.

This process of modeling helps you to gather the best possible result for the given problematic data by the client.

Validation process:

The validation process generally includes the processing of both the models i.e. the first data model that was provided by the client and the second model which is the corrected one.

By doing this the process assures that the necessary correction works well enough and will satisfy the clients need.

Tracking and Implementation:

This is the final round after the validation process to complete the project. Here you start by implementing the changes and then tracking for the results.

Also the accuracy of the project is tested here. All the final results will be made here and the project will be ready for client.

2) What do you understand by the term Data Cleansing?

Data cleansing is referred to data cleaning, this process involves the deletion of the unwanted, incorrect, inconsistent and error full data.

This process is done to enhance the quality of the data provided.

3) In how many ways can we perform Data Cleansing?

Data cleansing process can be done in the following ways:

  • Shorting the data by various attributes.
  • In large and big data sheets, the cleaning should be done step wise in order to achieve result for the given data.
  • For big projects, break down the data sheets into parts and work on it in a sequence manner which will help you to come with the perfect data faster as compared to working on the whole lot at once.
  • For the cleansing process make a set of utility tools which will help you to maximize the speed of the process and reduce the duration for completion of the process.
  • Arrange the data by estimated frequency and start by clearing the most common problems first.
  • For faster cleaning, analyze the summary of the data.
  • By keeping a check over daily data cleansing, you can improvise the set of utility tools as per requirements.

4) What is a logistic regression?

The logistic regression works on a statistical manner for examining a data sheet having one or more than one variable in it, for defining an outcome.

5) Name the best tools which are useful for analyzing data provided?

The best data analyst tools for analyzing the given data are:

  • Google fusion table
  • Wolfram alpha’s
  • IO
  • NodeXL
  • Solver
  • Search operator by Google
  • Open Refine
  • Rapid Miner
  • Tableau

6) Differentiate between the term Data Mining and Data profiling?

Data Mining:

It mainly focuses on the bulk analysis, rectification of unusual record, sequenced discoveries, dependencies, different type of attributes, etc.

Data Profiling:

It basically works on the independent attributes with high analysis. Also, it gives detailed information about various attributes which are present in the program.

7) State the general problems in the work of Data Analyst?

The common problems which occur in the work of a Data Analyst are as follows:

  • Rectifying the overlapped data
  • Different value representation
  • Improper and illegal values
  • Finding missing values
  • Over copied entries
  • General misspelling

8) What do you understand by the term Hive?

Hive is a framework for programming which is developed by Google and it is used for processing a bulk of data set for a specific application on a distributed computing environment.

9) What are the missing patterns that are generally observed while working on a data sheet?

The missing patterns that are generally observed are as follows:

  • Unobserved input variable
  • Missing value itself
  • Random missing
  • Random missing completely

10) Explain the KNN imputation method?

The KNN method is generally used to rectify the two of the similar attributes/terms in a data sheet.

These are the common questions that can be asked during the interview. Now your interview will turn in to general questions and their answers may be summarized as.


The hiring person may inquire about your previous practice to see whether you have enough knowledge and skills in the field of data analyst or not.

When you have formerly worked as a Data Analyst its good enough, then you need to go forward and talk about all the skills and achievements of the previous job.

When you do not have the experience of the same field then do not sit quietly in front of the interviewer person, it will definitely make a negative mark in your interview.

Just cool yourself and think a bit about your skills, which way it relates to the title that you have applied for and go forward on those topics by making them relative to the present job title.

When you talk about your past work responsibility and achievements of the job and relating them to the benefits you can give now, will definitely make a strong positive effect on the interviewers.

Generally give the interviewer many reasons for keeping you, show them the benefit that the company will have in recruiting you as a data analyst. When you show the company their benefits the chances of your selection automatically increases.

Important key point:

Also, if you completed the above described homework then, you can add up by giving the interviewer the full work out tactic of your effort for the Data Analyst position you signed for.

Tell them the whole toil in a very comprehensive and well-dressed way and let them make out that by keeping you, it will undeniably make a distinction and also be beneficial for the company.

Moreover make them taste a bit with your multiple skills with accuracy and let them know your last job work achievements in the starting six months, this surely will help you make an optimistic picture in the mind of the employer and maybe he thinks to hire you for the best of company as a data analyst.

The trickiest question of all:

Tell me one good reason for selecting you?

With full confidence, focus on your skills and strengths. Instead of telling one good reason to admire you, give them the below mentioned four key reasons to select you:

i. Flexibility
ii. Eagerness
iii. Expertness
iv. Punctuality

These four points possess like the one and only key to open the close doors of the job that you need, it typically describes you.

When you have completed your home work there’s no space for right or wrong answers, the only thing that matters the most is that the perfect answer that you give to the question asked.

The interview is a well organized situation which is set by the company to test your capabilities under extreme condition yet the interview only gives you a part of the actual environment that you will experience if selected.

This brings us to an end and we really hope that you have prepared yourself while reading it.

Not only we have discussed the most important question and answer which can be asked during the interview of a data analyst but have also discussed in details on how you should proceed with the preparation part and follow them in order to get the job of your dreams. Go prepared for the interview and all the tough once will be easy to face.