How Would You Estimate How Many Cakes A Famous Bakery Can Sell In Indore During May
Such questions typically test your analytical thought process without the help of computers and a data set. Interviewers prefer candidates with critical thinking skills as it helps them identify connections and relations between variables that are not always clear. For an effective answer, communicate your thought process and the ability to identify segments and variables.
Example:First, I would collect data about the population of Indore, find out the number of bakeries in the city and the average footfall of people in bakeries each month. Next, I would try to find out the total number of anniversaries and birthdays during May from the municipality office. I would also figure out if there are special days like Friendship Day, Mother’s Day or Father’s Day in May. Based on the data collected and conducting analysis on it, I would estimate how many cakes the bakery can potentially sell in May.
Prepare To Speak About A Data Project You Completed
Even if you are not asked, try to find a way to talk about a data project you have completed either at University, online or during work experience. This is an opportunity to show the interviewer that you have experience working on data projects. Talk about the problem formulation phase, the data gathering phase, the cleaning phase, the transformation phase, the tools used for the analysis, the techniques you used for the analysis and the outcome of the project.
Dont be shy here, make sure that the interviewer understands that you know how to run/work on data projects. This step can set you apart from other candidates.
What Are The Key Requirements For Becoming A Data Analyst
To become a data analyst, you should have a combination of the following skills:
 Structured Query Language
 Presentation Skills
 Mathematics and Statistics
Generally, the following responsibilities fall within the data analyst role so you should be comfortable with:
 Utilize data science skills to become experts in the performance of specific businesses and departments.
 Tend to be specific to a single team or department, like Sales, Marketing, or Customer Experience.
 Implement basic scripts and pipeline code, but typically are not expected to develop software.
You May Like: How Honest Should You Be In An Exit Interview
Data Analyst Interview Questions: Statistics
Statistics is a branch of mathematics dealing with data collection and organization, analysis, interpretation, and presentation. Statistics can be divided into two categories: Differential and Inferential Statistics. This field is related to mathematics and thus gives a kickstart to Data Analysis career.
Top 4 Analytics Interview Questions And How To Prepare
When hiring managers schedule interviews for analytics jobs, they assume the candidates theyve chosen have a strong level of technical abilities. While those skills will likely be tested during the interview, managers also want to know more about how you think through problems and view your work.
What matters is initiative, being an independent learner, having a strong work ethic and working whatever hours are necessary, and good teamwork, says Thomas Goulding, a professor in Northeasterns Master of Professional Studies in Analytics program.
Here are some of the top questions you can expect to answer during your interview and how you can best prepare for them.
Also Check: What Do You Wear To A Job Interview
List Of Data Analyst Interview Questions From Top Companies
Data analysis is a rapidly growing field, and companies are increasingly looking to hire the best talent to handle big data. Here are a few examples of interview questions for Data Analysts from some of the top tech companies:
 What are clustered and nonclustered indexes in SQL? Explain the difference between the two.
 You have 10 bags of marbles with 10 marbles in each bag. All but one bag has marbles that weigh 10g each. The exception has marbles that weigh 11g each. How would you determine which bag has 11g marbles using a scale only once?
 Describe data cleansing techniques you have used.
 How do you manage your time with solo projects?
 Which functions in SQL do you like the most?
 What is data normalization and nonnormalization?
 What do you understand by cascading referential integrity?
 Tell me about a time you automated an otherwise manual process.
 Tell me about a time you started an analysis with certain expectations, but then got unexpected results
 If you were to pick a sample for an experiment, how would you choose the size of the sample?
Previous Article
Best Job Strategy For Position Of Data Analyst
Go into a lot of meetups for example conferences, events out there. You should go and attend those places. That’s how you would get to know people. Knowing people right now is highly necessary. Its more important than sending out your resume, like a hundred resume a day.
You should build your network. Building network is for an entrylevel or someone who has just graduated from college. Everyone is looking for networking. Go to conferences, events, and you will get to know people. Reach out to them on LinkedIn look for mentorship look for guidance. After that say, Hey, this is my resume. If there are any opportunities in your company, would you keep me in consideration? That’s a very significant way to get you some solid in an interview especially if you’re already connected with that person and built a relationship with him/her. That person will help you a lot from inside.
Recommended Reading: What Is Coding Test In Interview
First Off Think About What Problems The Company Are Hiring You To Solve
Not every data analysts role is the same, much as not every CEOs role is the same. Different companies at various stages will have a different set of data challenges. There is no such thing as a onewayfitsallsolution to prepare for your interview.
Remember, they’re hiring you to solve their problems. So if you can solve them, you’re hired. But that begs the question: what problems?
If you have a target company in mind youre applying for, then first, please do a thorough research about the company:
 Whats their business model, how do they make money?
 In their industry, what are the main/important metrics to track?
 At their stage, what might be their data problems?
 What are their customers like?
Some of the above questions, you can take it to the company when interviewing with them, too.
What Do You Think Are The Most Important Skills A Data Analyst Needs To Work Efficiently With People That Have Different Roles Knowledge And Duties
As a data analyst, youll be reporting your findings to various people often – and most of the time they dont have a background in technical knowledge. This means you have to be excellent in interpreting your findings using nontechnical language. By answering this question, youre showing that youre capable of working with various people who dont speak your language.
Potential Answer:
I believe patience, understanding, and showing that you care are all very important when working with people of different educational backgrounds. I often work with stakeholders and the most common challenge is trying to answer a question I dont have the answer to yet, due to the limited data I have at the moment. When this situation arises, I use my available data to answer the question as closely as possible and then propose how I can find the information that we dont currently have. This not only shows that Im dedicated to the project but that I also respect their needs.
Don’t Miss: What Are Questions They Ask In An Interview
Q28 What Is Imputation Explain Different Types Of Imputation Techniques
Ans. Imputation is the process of replacing the missing data with substituted values. While there are many ways to approach missing data, the most common imputation techniques are:
There are two types of imputationsingle or multiple.
Single Imputation: In this, you find a single estimate of the missing value. The following are the single imputation techniques:
Mean imputation: Replace the missing value with the mean of that variable for all other cases.
Hot deck imputation: Identify all the sample subjects who are similar on other variables, then randomly choose one of their values on the missing variable.
Cold deck imputation: It works just like the hot deck but in a systematic manner. A systematically chosen value from an individual who has similar values on other variables.
Regression imputation: The predicted value obtained by regressing the missing variable on other variables.
Stochastic regression: It works like the regression imputation and adds the average regression variance to regression imputation.
Substitution: Impute the value from a new variable that was not selected to be in the sample.
Multiple Imputation: In the Multiple Imputation technique, the values are estimated multiple times.
Data Analyst Interview Questions And Answers
1) What is the difference between Data Mining and Data Analysis?
Data Mining vs Data Analysis
Data Mining 

Data mining usually does not require any hypothesis.  Data analysis begins with a question or an assumption. 
Data Mining depends on clean and welldocumented data.  Data analysis involves data cleaning. 
Results of data mining are not always easy to interpret.  Data analysts interpret the results and convey the to the stakeholders. 
Data mining algorithms automatically develop equations.  Data analysts have to develop their own equations based on the hypothesis. 
Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization
2) Explain the typical data analysis process.
Data analysis deals with collecting, inspecting, cleansing, transforming and modelling data to glean valuable insights and support better decision making in an organization. The various steps involved in the data analysis process include
Having identified the business problem, a data analyst has to go through the data provided by the client to analyse the root cause of the problem.
Data Preparation
This is the most crucial step of the data analysis process wherein any data anomalies with the data have to be modelled in the right direction.
Data Modelling
Validation
In this step, the model provided by the client and the model developed by the data analyst are validated against each other to find out if the developed model will meet the business requirements.
You May Like: What Can You Ask In An Interview
Write The Difference Between Variance And Covariance
Variance: In statistics, variance is defined as the deviation of a data set from its mean value or average value. When the variances are greater, the numbers in the data set are farther from the mean. When the variances are smaller, the numbers are nearer the mean. Variance is calculated as follows:
Here, X represents an individual data point, U represents the average of multiple data points, and N represents the total number of data points. Covariance: Covariance is another common concept in statistics, like variance. In statistics, covariance is a measure of how two random variables change when compared with each other. Covariance is calculated as follows:
Here, X represents the independent variable, Y represents the dependent variable, xbar represents the mean of the X, ybar represents the mean of the Y, and N represents the total number of data points in the sample.
Describe A Time When You Had To Advise A Client Toward A Different Course Of Action
As a business analyst, it is your job to make recommendations both in the interest of the client and the organization. Your perspective should be based on the collected data as you interpret it. Should a client pursue a certain course of action you do not feel is in their best interest, you may be required to present the data in new and interesting ways to convince them otherwise.
In your answer, you should explain the ways you can apply your problemsolving skills to navigate potentially difficult situations with clients and other important stakeholders.
Example:“Once, I had a client who was looking to expand a product line for their store. At the same time, they were already struggling to sell many of the products they already carried. I used a detailed sales analysis to show them why they should focus on selling their current products instead of investing in new ones, and offered both suggestions about how they might increase sales along with areas in which they are already succeeding.”
Also Check: How To Prepare For Amazon Coding Interview
What Are The Best Methods For Data Cleaning
 Create a data cleaning plan by understanding where the common errors take place and keep all the communications open.
 Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.
 Focus on the accuracy of the data. Set crossfield validation, maintain the value types of data, and provide mandatory constraints.
 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.
Data Analyst Interview Questions And Answers 2021
The 365 Team
Why you should be familiar with data analyst interview questions?
If youre aiming for a data analyst job, sooner or later, youll reach the final stage of the application process – the data analyst job interview. So, how can you ace the interview with ease? By being wellfamiliar with the data analyst interview questions in advance.
And thats exactly why you should read this article. Here youll learn everything you need to nail the challenging job interview and secure a career as a data analyst:
Bonus content: how to present yourself in your best light and leave a lasting impression on the interviewers. But that will come last. In the meantime
Don’t Miss: What To Ask When Interviewing Someone
Video Conferencing And Phone Interviews
The way in which interviews are conducted is changing. Were heading towards remote video conferencing rather than traditional face to face meetings. Working from home as a data analyst is becoming the new norm. Even tech giants like Google, LinkedIn, and Amazon have announced a switch to online interviews.
Interviews over video calls dont feel much different from inperson interviews. Just make sure your Internet connection is stable and background noise is kept to a minimum.
Voiceonly interviews can prove more challenging. You cant pick up on body language and facial expressions so communication is limited. If you dont understand a question or didnt quite catch it, always ask the interviewer to repeat it. Theyll understand the situation. Its important that youre both on the same page.
Here are some reminders for your phone interviews:
Which Data Analyst Software Are You Trained In
This question tells the interviewer if you have the hard skills needed and can provide insight into what areas you might need training in. Its also another way to ensure basic competency. In your answer, include the software the job ad emphasized, any experience with that software you have, and use familiar terminology.
Heres a sample answer:
I have a breadth of software experience. For example, at my current employer, I do a lot of ELKI data management and data mining algorithms. I can also create databases in Access and make tables in Excel.
Also Check: What Questions Do They Ask In A Cna Interview
How Have You Used Statistics In Your Work
Understanding basic statistics knowledge is important for data analysts. This question helps the interviewer see how much you know about statistics.
Potential Answer:
I have used statistics before – mostly calculating the mean and standard variances, as well as significance testing. Ive also determined the relationship between two variables in a data set while working with correlation coefficients.
Your Future Career In Data Analysis
You should now have a good grasp of the interview process and should be feeling more confident. Remember, data analysis is an indemand skill even if youre not successful on interview day, therell be more opportunities coming your way.
If youre yet to secure an interview and want to boost your chances of getting hired, enroll in our data analytics bootcamp. This structured learning program will teach you everything you need to land a highlypaid data analyst job. Our experienced mentors will even guide you through the interview process, supporting you every step of the way.
For further reading, you might be interested in our data analytics blog. Learn how to become a data analyst to enjoy a long and rewarding career in this field.
You May Like: What Is A Behavioral Interview
Q5 How To View Underlying Sql Queries In Tableau
To view the underlying SQL Queries in Tableau, we mainly have two options:
 Use the Performance Recording Feature: You have to create a Performance Recording to record the information about the main events you interact with the workbook. Users can view the performance metrics in a workbook created by Tableau.Help > Settings and Performance > Start Performance Recording.Help > Setting and Performance > Stop Performance Recording.
 Reviewing the Tableau Desktop Logs: You can review the Tableau Desktop Logs located at C:UsersMy DocumentsMy Tableau Repository. For live connection to the data source, you can check log.txt and tabprotosrv.txt files. For an extract, check tdeserver.txt file.
Probability And Statistics Interview Questions
Statistics and probability questions are common interview fundamentals intended to test a data analyst’s understanding of key concepts. For example, every data analyst should know what a Pvalue is, what confidence intervals are, and how to read and analyze an A/B test.
Q1. Given uniform distributions X and Y and the mean 0 and standard deviation 1 for both, whats the probability of 2X > Y?
Given that X and Y both have a mean of 0 and a standard deviation of 1, what does that indicate for the distributions of X and Y?
Q2. What is an unbiased estimator and can you provide an example for a layman to understand?
To answer this question, start by thinking about how a biased estimator looks. Then, think about how an unbiased estimator differs. Ultimately, an estimator is unbiased if its expected value equals the true value of a parameter, meaning that the estimates are in line with the average.
Q3. Let’s say we have a sample size of N. The margin of error for our sample size is 3. How many more samples would we need to decrease the margin of error to 0.3?
In order to decrease our margin of error, we’ll probably have to increase our sample size. But by how much?
Don’t Miss: What Are Phone Interviews Like