Top 100+ Data Analyst Interview Questions for 2024

Top 100+ Data Analyst Interview Questions for 2024


Over ten years ago, a data analyst interview was very simple. All you needed to know to say was two things:

  1. “Yes, I know Excel!”
  2. “I’m a great communicator.”

Nowadays, the role of a data analyst has changed. Not only have salaries shot up, but data analysts are more in-demand than ever before due to their insight and analytical skillset.

Most data analyst jobs at tech companies require a strong technical skillset combined with good judgment. In this guide, we’ll break down the interview process and the most common data analyst interview questions.

Data Analyst Interview Guide

Technical interviews for data analyst roles are typically multi-stage interviews. They start with initial screens designed to weed out candidates, and quickly progress to more technically demanding screens. To prepare for a data analyst interview, practice questions in each of these category.

Here’s a typical breakdown of the data analyst interview process:

1. Initial Phone Screen

Initial screens are generally calls with recruiters. These screens assess your experience, your interests/specializations, and salary expectations. In some cases, you may be asked basic SQL questions or a simple scenario-based case question.

Sample question: Tell us about a challenging project you have worked on. What were some of the obstacles you had to overcome?

2. Technical Interview

The technical screen assesses your technical skills. In many cases, SQL is a primary focus. SQL questions range from basic definitions, to writing intermediate-to-advanced queries. Depending on the job function, you may be asked technical questions about Python, statistics and probability, algorithms and A/B testing.

Sample question: Given two tables, write a query to find the number of users that accepted Friend requests in the last month.

3. Take-Home Challenge

Take-homes are longer tests that may take several hours to complete. These challenges are designed to evaluate your ability to handle data, perform analysis and present your results effectively. Typically, these tests will ask you to perform and investigation on a dataset and present your findings.

Sample question: Prepare a summary of sales and website data for the Vice President of Marketing. Include an overview of website traffic and sales, as well as areas for improvement.

4. On-site Interview

Data analyst on-site interviews typically consist of 3-5 hour-long interviews. They typically cover traditional technical SQL and statistics questions, as well as data analytics case studies and behavioral questions.

Sample question: Describe an analytics project you worked on. What were some challenges you faced?

Different Types of Data Analyst Interview Questions

Interview Query regularly analyzes the contents of data analyst interviews. By tagging common keywords and mapping them back to question topics for over 10K+ tech companies, we’ve found that SQL questions are asked most frequently.

In fact, in interviews for data analyst roles, SQL and data manipulations questions are asked 85% of the time.

Here are the types of technical interview questions data analysts get asked most frequently:

Additionally, for more traditional data analyst roles, expect interview questions around:

  • Excel
  • Data Visualization

Let’s first dive into how to approach and answer behavioral interview questions

Behavioral Interview Questions for Data Analysts

Behavioral questions in data analyst interviews ask about specific situations you’ve been in, in which you had to apply specific skills or knowledge.

For many data analysts, behavioral questions can be fairly tough.

One tip: Always try to relate the question back to your experience and strengths.

1. Describe a time when you spotted an inconsistency. How did you respond?

Successful data analysts can help businesses identify anomalies and respond quickly.

For data sense questions, think about a time that you were able to spot an inconsistency in data quality, and how you eventually addressed it.

2. Talk about a time where you had to make a decision in a lot of uncertainty.

Interviewers want to see you demonstrate:

  • Decisiveness – Show the interviewer that you can make decisions and communicate your decision-making process.
  • Self-direction – Show that you are able to choose a path forward, deduce information, and create a plan of action.
  • Adaptability – Your response should show that you can adapt your decision-making in a challenging situation.

Here’s an example answer: “In my previous job, I was working on a sales forecasting problem under a strict deadline. However, due to a processing error, I was missing the most recent data, and only had 3-year-old sales figures. The strategy I took was applying the growth factor to the data to establish correct correlation and variances. This strategy helped me deliver a close forecast and meet the deadline.”

3. How would you convey insights and the methods you use to a non-technical audience?

Interviewers ask this question to see if you can make complex subjects accessible and that you have a knack for communicating insights in a way that persuades people. Here’s a marketing analytics example response:

“I was working on a customer segmentation project. The marketing department wanted to better segment users. I worked on a presentation and knew the audience wouldn’t understand some of the more complex segmenting strategies, so I put together a presentation that talked about the benefits and potential trade-offs of segmenting options like K-means clustering.For each option, I created a slide to show how it worked, and after the presentation, we were able to have an informed discussion about which approach to use.”

4. How do you set goals and achieve them? Give us an example.

Interviewers want to see that you can set manageable goals and understand your process for achieving them. Don’t forget to mention the challenges you faced, which will make your response more dynamic and insightful. For example, you might say:

“Data visualization was something I struggled with in college. I didn’t have a strong design eye, and my visualizations were hard to read. In my last job, I made it a goal to improve, and there were two strategies that were most helpful. I took an online data visualization course, and I built a clip file of my most favorite visualizations. The course was great for building my domain knowledge. However, I felt I learned the most by building my clip file and breaking down what made a good visualization on my own.”

5. Describe a time when you solved a conflict at work.

This question assesses your ability to remain objective at work, that you communicate effectively in challenging situations, and that you remain calm under fire. Here’s an example response:

“In my previous job, I was the project manager on a dashboard project. One of the BI engineers wasn’t meeting the deadlines I had laid out, and I brought that up with him. At first, he was defensive and angry with me. But I listened to his concerns about the deadlines and asked what I could do to help. From our conversation, I learned he had a full workload in addition to this project. I talked with the engineering manager, and we were able to reduce some of his workload. He caught up quickly and we were able to finish the project on time.”

6. Give an example of a situation when you have shown effectiveness, empathy, humbleness, and adaptability.

This is a leadership question in disguise. If you can relate a time you were an effective leader, chances are you will easily incorporate all of these traits. For example:

“I was the lead on a marketing analytics project. We had a critical deadline to meet, but due to a data processing error, we were in danger of missing the deadline. The team morale was low, so I held a quick meeting to lay out a schedule, answer questions, and rally the team. That meeting gave the team the jolt it needed. We made the deadline, and I made sure leadership knew how hard each of the contributors had worked.”

7. Give me an example of a time when you failed on a project.

This question tests your resilience, how you respond to adversity, and how you learn from your mistakes. You could say:

“I had to give a presentation about a data analytics project to a client. One mistake I made was assuming the audience had more technical knowledge than they did. The presentation was received by a lot of blank stares. However, I knew the material about our findings was strong. I stopped for questions, and then, I jumped ahead to the visualizations and findings. This helped get the presentation on track, and by the end, the client was impressed. Now, whenever I have a presentation, I take time to understand the audience before I start working on it.”

8. Talk about an occasion when you used logic to solve a problem.

A strong response to this question shows that you can solve problems creativity and that you don’t just jump at the first or easiest solution. One tip: Illustrate your story with data to make it more credible.

Here’s what you could say: “In my previous job, I was responsible for competitor research, and through my analysis, I noticed that our most significant competitors had increased sales 5% during Q1. This deviated significantly from our sales forecasts for these accounts. I found that we needed to update our competitor sales models with more recent market research and historical data. I tested the model adjustments, and ultimately, I was able to improve our forecasting accuracy by 15%.”

9. What do you do if you disagree with your manager?

Interviewers ask this question to gauge your emotional maturity, see that you can remain objective, and gain insights into your communication skills. Avoid subjective examples like my boss was a micromanager. Instead, you could say:

“One time, I disagreed with my manager over the process for building a dashboard, as their approach was to jump straight into the execution. I knew that it would be better to perform some planning in advance, rather than feeling our way through and reacting to roadblocks as they arose, so I documented a plan that could potentially save us time in development. That documentation and planning showed where pitfalls were likely to arise, and by solving for future issues we were able to launch the new dashboard three weeks early.”

10. How comfortable are you with presenting insights to stakeholders?

This question is asked to see how confident you are in your communication skills, and it provides insight into how you communicate complex technical ideas. With this question, talk about the various ways you make data and analytics accessible. Try to answer these questions:

  • Do you create visualizations?
  • What do you do to prepare for a data analytics presentation?
  • What strategies do you use to make data more accessible?
  • What presentation tools do you use?

11. Talk about a time you were surprised by the results of an analytics project.

This question is basically asking: Are you open to new ideas in your work? Analysts can get stuck on trying to prove their hypothesis, even if the data says otherwise. A successful analyst is OK with being wrong and listens to the data. You could say:

“While working on a customer analytics project, I was surprised to find that a subsegment of our customer base wasn’t actually responding to the offers we were providing. We had lumped the subsegment into a larger customer bucket, and had assumed that a broader segmentation wouldn’t make a difference. I relayed the insight to the marketing team, and we were able to reduce churn among this subsegment.”

12. Why are you interested in working for this company?

This question is super common in analyst behavioral interviews. However, it still trips a lot of candidates up. Another variation of this question would be: why did you want to work in data analytics.

In your response, your goal should be to convey your passion for the work and talk about what excites you about the company/role. You might focus on the company’s culture, a mentor who inspired you, recommendation you received, or someone in your network who’s connected with the company. A sample response:

“I’m excited by the possibility of using data to foster stronger social connections amongst friends and peers. I also like to ‘go fast’ and experiment, which fits into Meta’s innovative culture.”

13. Talk about a time when you had trouble communicating with stakeholders. How were you able to overcome it?

Interviewers ask questions like this to assess how you handle adversity and adapt. Don’t be afraid to share what went wrong. B do describe what you learned and how you apply it to future work.

Here’s a sample answer for a data analyst role: “I presented a data analytics project to non-technical stakeholders, but my presentation was far too technical. I realized that the audience wasn’t following the technical aspects, so I stopped and asked for questions. I spent time clarifying the technical details until there were no questions left. One thing I learned was that it’s important to tailor presentations to the audience, so before I start a presentation, I always consider the audience.”

SQL Interview Questions for Data Analysts

SQL Interview Questions for Data Analysts

Data analysts use SQL to query data to solve complex business problems or find answers for other employees. In general, SQL data analyst questions focus on analytics and reporting:

  • Basic SQL Questions - These include the basics, e.g. definitions, as well as beginner SQL queries.
  • Analytics Questions – Analytics based questions, you might have to understand what kind of report or graph to build first, and then write a query to generate that report. So it’s an extra step on top of a regular SQL question.
  • Reporting Questions – SQL reporting questions replicate the work many data or business analysts do on a day-to-day basis, e.g., writing queries.

Reporting interview questions focus on writing a query to generate an already-known output. Such as producing a report or a metric given some example table.

For analytics-based questions, you might have to understand what kind of report or graph to build first and then write a query to generate that report. So it’s an extra step on top of a regular SQL question.

Basic SQL Interview Questions

14. What are the different ways of handling NULL when querying a data set?

To handle such a situation, we can use three different operations:

  • IS NULL − This operator returns true, if the column value is NULL.
  • IS NOT NULL − This operator returns true, if the column value is not NULL.
  • <=> − This operator compares values, which (unlike the = operator) is true even for two NULL values.

15. What’s the difference between UNION and UNION ALL? (Asked by Facebook)

UNION and UNION ALL are SQL operators used to concatenate 2 or more result sets. This allows us to write multiple SELECT statements, retrieve the desired results, then combine them together into a final, unified set.

The main difference between UNION and UNION ALL is that:

  • UNION: only keeps unique records
  • UNION ALL: keeps all records, including duplicates

16. What is the difference between a SQL view and table? (Asked by Kaiser Permanente)

A table is structured with columns and rows. A view is a virtual table extracted from a database by writing a query.

17. What’s the difference between an INNER and OUTER JOIN?

The difference between an inner and outer join is that inner joins result in the intersection of two tables, whereas outer joins result in the union of two tables.

18. What is the difference between WHERE and HAVING?

The WHERE clause is used to filter rows before grouping, and HAVING is used to exclude records after grouping.

19. When do you use the CASE WHEN function?

CASE WHEN lets you write complex conditional statements on the SELECT clause, and also allows you to pivot data from wide to long formats.

20. What is the difference between DELETE TABLE and TRUNCATE TABLE in SQL?

Although they’re both used to delete data, a key difference is that DELETE is a Database Manipulation Language (DML) command, while TRUNCATE is a Data Definition Language (DDL) command.

Therefore, DELETE is used to remove specific data from a table, while TRUNCATE removes all the rows of a table without maintaining the structure of the table.

Another difference: DELETE can be used with the WHERE clause, but TRUNCATE cannot. In this case, DELETE TABLE would remove all the data from the table, while maintaining the structure. TRUNCATE would delete the entire table.

21. How would you pull the date from a timestamp in SQL?

EXTRACT allows us to pull temporal data types like date, time, timestamp, and interval from date and time values.

22. Write a SQL query to select all records of employees with last names between “Bailey” and “Frederick”.

For this question, assume the table is called “Employees” and the last name column is “LastName”.

SELECT * FROM Employees WHERE LastName BETWEEN 'Bailey' AND 'Frederick'

23. What is the ISNULL function? When would you use it?

The ISNULL function returns an alternative value if an expression is NULL. Therefore, if you wanted to add a default value for NULL values, you would use ISNULL. For example in the statement:


NULL price values would be replaced with 50.

Reporting SQL Questions

24. We have a table with an id and name field. The table holds over 100 million rows and we want to sample a random row in the table without throttling the database. Write a query to randomly sample a row from this table.


Column Type

In most SQL databases, there exists a RAND() function which normally we can call:

SELECT * FROM big_table

The function will randomly sort the rows in the table. This function works fine and is fast if you only have, let’s say, around 1,000 rows. It might take a few seconds to run at 10K. And then at 100K maybe you have to go to the bathroom or cook a meal before it finishes.

What happens at 100 million rows?

Someone in DevOps is probably screaming at you.

Random sampling is important in SQL with scale. We don’t want to use the pre-built function because it wasn’t meant for performance. But maybe we can re-purpose it for our own use case.

We know that the RAND() function actually returns a floating point between 0 and 1. So if we were to instead call:


We would get a random decimal point to some Nth degree of precision. RAND() essentially allows us to seed a random value. How can we use this to select a random row quickly?

Let’s try to grab a random number using RAND() from our table that can be mapped to an id. Given we have 100 million rows, we probably want a random number from 1 to 100 million. We can do this by multiplying our random seed from RAND() by the max number of rows in our table.

    SELECT MAX(id) FROM big_table)

We use the CEIL function to round the random value to an integer. Now we have to join back to our existing table to get the value.

What happens if we have missing or skipped id values, though? We can solve for this by running the join on all the ids which are greater or equal than our random value and selecting only the direct neighbor if a direct match is not possible.

As soon as one row is found, we stop (LIMIT 1). And we read the rows according to the index (ORDER BY id ASC). Now our performance is optimal.

FROM big_table AS r1
        SELECT MAX(id)
        FROM big_table)
    ) AS id
) AS r2
    ON >=

25. Given a table of job postings, write a query to breakdown the number of users that have posted their jobs once versus the number of users that have posted at least one job multiple times.

Hint: We want the value of two different metrics, the number of users that have posted their jobs once and the number of users that have posted at least one job multiple times. What does that mean exactly?

26. Write a query to get the current salary for each employee.

More context. Let’s say we have a table representing a company payroll schema.

Due to an ETL error, the employees table instead of updating the salaries every year when doing compensation adjustments, did an insert instead. The head of HR still needs the current salary of each employee.

27. Write a query to get the total amount spent on each item in the ‘purchases’ table by users that registered in 2023.

More context. Let’s say you work at Costco. Costco has a database with two tables. The first is users composed of user information, including their registration date, and the second table is purchases which has the entire item purchase history (if any) for those users.

Here’s a process you can use to solve this question:

  • You can use INNER JOINor JOIN to connect tables users and purchases on the user_id column
  • You can filter the results by using the WHERE clause
  • Use GROUP BY to aggregate items, and apply the SUM() function to calculate the amount spent

28. Write a query to get the cost of all transactions by user ordered by total cost descending.

Here’s a code solution:

 , AS user_id
 ,ROUND(SUM(p.price * t.quantity ) ,2) AS total_cost
FROM users u
INNER JOIN transactions t
    ON = t.user_id
INNER JOIN products p
    ON = t.product_id
ORDER BY total_cost DESC

29. Given a table of transactions and a table of users, write a query to determine if users tend to order more to their primary address versus other addresses.

Hint: This question has been asked in Amazon data analyst interviews, and the first step is getting data from the users table to the transactions table. This can be done using a JOIN, based on a common column between the tables. How do we identify when the addresses match? We can use the CASE WHEN statement to produce a flag to use in further calculations. Finally, we need the percentage of all the transactions made to the primary address rounded to two decimals.

30. Write a query to get the top three users that got the most upvotes on their comments.

You’re provided with three tables representing a forum of users and their comments on posts and are asked to find the top three users with the most upvotes in the year 2020. Additionally, we’re told that upvotes on deleted comments and upvotes that users make on their own comments don’t matter.

Hint: The trickiest part about this question is performing your JOINs on the proper fields. If you join two of our tables on the wrong key, you could make things difficult, or even impossible, for yourself later on.

31. Write a query to identify customers who placed more than three transactions each in both 2019 and 2020.

In this question, you’re given two transactions and users.

Hint: Start by joining the transactions and users tables. Use INNER JOIN or JOIN.

Analytics SQL Questions

32. Given a table of search results, write a query to compute a metric to measure the quality of the search results for each query.

search_results table

Column Type
result_id INTEGER
position INTEGER
rating INTEGER

You want to be able to compute a metric that measures the precision of the ranking system based on position. For example, if the results for dog and cat are….

query result_id position rating notes
dog 1000 1 2 picture of hotdog
dog 998 2 4 dog walking
dog 342 3 1 zebra
cat 123 1 4 picture of cat
cat 435 2 2 cat memes
cat 545 3 1 pizza shops

…we would rank ‘cat’ as having a better search result ranking precision than ‘dog’ based on the correct sorting by rating.

Write a query to create a metric that can validate and rank the queries by their search result precision. Round the metric (avg_rating column) to 2 decimal places.

33. Given the two tables, write a SQL query that creates a cumulative distribution of number of comments per user. Assume bin buckets class intervals of one.

Hint: What is a cumulative distribution exactly? If we were to imagine our output and figure out what we wanted to display on a cumulative distribution graph, what would the dataset look like?

34. We are given a table of bank transactions with three columns: user_id, a deposit or withdrawal value (determined if the value is positive or negative), and created_at time for each transaction.

Write a query to get the total three day rolling average for deposits by day.

Usually, if the problem states to solve for a moving/rolling average, we’re given the dataset in the form of a table with two columns, the date and the value.

This problem, however, is taken one step further with a table of just transactions with values conditioned to filtering for only deposits, and remove records representing withdrawals, denoted by a negative value (e.g. -10).

35. Given a table of user experiences representing each person’s work experiences, write a query to determine if a data scientist gets promoted faster, if they switch jobs more frequently.

More context. Let’s say we’re interested in analyzing the career paths of data scientists. We have job titles bucketed into data scientist, senior data scientist, and data science manager. We’re interested in determining if a data scientist who switches jobs more often ends up getting promoted to a manager role faster than a data scientist that stays at one job for longer.

This question has been asked in Google data analyst interviews, and it requires a bit of creative problem solving to understand how we can prove or disprove the hypothesis. The hypothesis is that data scientists that end up switching jobs more often get promoted faster.

Therefore, in analyzing this dataset, we can prove this hypothesis by separating the data scientists into specific segments on how often they jump in their careers. How would you do that?

36. Write a query to get the distribution of the number of conversations created by each user by day in the year 2020.

Our focus is getting our key metric of a number of new conversations created by day in a single query. To get this metric, we have to group by the date field and then group by the distinct number of users messaged. Afterward, we can then group by the frequency value and get the total count of that as our distribution.

37. Write a query that could display the percentage of users on our forum that would be acting fraudulently in this manner.

More context. We’re given three tables representing a forum of users and their comments on posts. We want to figure out if users are creating multiple accounts to upvote their own comments. What kind of metrics could we use to figure this out?

38. Uber users are complaining that the pick-up map is wrong. How would you verify how frequently this is actually happening?

Hint. What metric would help you investigate this problem?

39. What strategies could we try to implement to increase the outreach connection rate?

More context. Let’s say that Facebook account managers are not able to reach business owners after repeated calls to try to onboard them onto a new Facebook business product. Assume that we have training data on all of the account manager’s outreach in terms of calls made, calls picked up, time of call, etc…

One option would be to investigate when calls are most likely to be connected. Could changing our approach here improve connection rate?

40. You’re analyzing churn on Facebook. How would you investigate if a disparity exists in retention on different Facebook platforms?

Follow-up question. How would you investigate the causes of such a disparity?

Data Analytics Case Study

Data analytics case study questions combine a rotating mix of product intuition, business estimation, and data analytics.

Case questions come up in interviews when the job responsibilities lean to more of a heavy analytics space with an emphasis on solving problems and producing insights for management.

Many times data analysts will transition into a heavy analytics role when they’re required to take on more scope around the product and provide insights that upper level management can understand and interpret.

So data analytics case study questions will focus on a particular problem and you will be judged on how you break down the question, analyze the problem, and communicate your insights.

Here’s an example data analytics case study question:

41. Given a table of Stack Overflow posts data, suggest three metrics to monitor the health of the community.

Community members can create a post to ask a question, and other users can reply with answers or comments to that question. The community can express their support for the post by upvoting or downvoting.

post_analytics table:

Column Type Description
id int Primary key of posts table
user_id int ID of the user who created the post
created_at datetime Timestamp of the post
title string Title of the post
body string Text content of the post
comment_count int Total number of the comments on a post
view_count int Total number of the views on a post
answer_count int Total number of answers on a post
upvotes int Total number of upvotes on the post

More context. You work at Stack Overflow on the community team that monitors the health of the platform. Community members can create a post to ask a question, and other users can reply with answers or comments to that question. The community can express their support for the post by upvoting or downvoting.

42. Write the queries for these metrics in SQL.

This is a classic data analytics case study. A question like this is designed to assess your data intuition, product sense, and ability to isolate key metrics.

Remember: There isn’t one correct answer, but usually, the conversation should head in a similar direction.

For example, this question asks about community health. Broadly, there are several metrics you’ll want to consider: Growth rate, engagement, and user retention would provide insights into the community’s health.

The challenge with this question is to determine how to measure those metrics with the data provided.

43. Describe an analytics experiment that you designed. How were you able to measure success?

Case questions sometimes take the form of behavioral questions. Data analysts get tasked with experimenting with data to test new features or campaigns. Many behavioral questions will ask about experiments but also tap into how you approach measuring your results.

With questions like these, be sure to describe the objective of the experiment, even if it is a simple A/B test. Don’t be afraid to get technical and explain the metrics you used and the process you used to quantify the results.

44. An online marketplace introduces a new feature that lets buyers and sellers conduct audio chats. Write a query to represent if the feature is successful or not.

Bonus question. How would you measure the success of this new feature?

See a step-by-step solution to this data analytics case study problem.

45. Write a query to prove or disprove the hypothesis: CTR is dependent on the search result rating.

More context. You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance and 1 is low relevance.

Each row in the search_eventstable represents a single search with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.

46. A revised new-user email journey boosts conversion rates from 40% to 43%. However, a few months prior, CVR was 45%. How would you investigate if the new email journey caused the increase in CVR?

See a step-by-step solution to this problem on YouTube.

Python Coding Questions for Data Analysts

Python coding questions for data analysts are usually pretty simple and not as difficult as the ones seen on Leetcode. Mainly most interviewers just want to test basic knowledge of Python to the point that they know you can write scripts or some basic functions to move data between SQL and Excel or onto a dashboard.

Most data analysts never write production code, such as their code is never under scrutiny because it’s not holding a website up or performing some critical business function.

Therefore, most coding questions for data analyst interviews are generally on the easier side and mostly test basic functions that are required for data manipulation. Pandas questions may also be asked in this round of the interview.

Here’s an example Python coding question:

47. Write a function that can take a string and return a list of bigrams. (Asked by Indeed)

sentence = "Have free hours and love children?"

output = [
 ('have', 'free'),
 ('free', 'hours'),
 ('hours', 'and'),
 ('and', 'love'),
 ('love', 'children')

Bigrams are two words that are placed next to each other. To actually parse them out of a string, we need to first split the input string.

We would use the Python function .split() to create a list with each individual word as an input. Create another empty list that will eventually be filled with tuples.

Then, once we’ve identified each individual word, we need to loop through k-1 times (if k is the amount of words in a sentence) and append the current word and subsequent word to make a tuple. This tuple gets added to a list that we eventually return. Remember to use the Python function .lower() to turn all the words into lowercase!

def find_bigrams(sentence):
  input_list = sentence.split()
  bigram_list = []

  # Now we have to loop through each word
  for i in range(len(input_list)-1):
    #strip the whitespace and lower the word to ensure consistency
    bigram_list.append((input_list[i].strip().lower(), input_list[i+1].strip().lower()))
  return bigram_list

48. Explain negative indexing. What purpose does it serve?

Negative indexing is a function in Python that allows users to index arrays or lists from the last element. For example, the value -1 returns the last element, while -2 returns the second-to-last element. It is used to display data from the end of a list, or to reverse a number or string.

Example of negative indexing:

a = "Python Data Analyst Questions"
print (a[-1])
>> s

49. What is a compound data type in Python?

Compound data structures are single variables that represent multiple values. Some of the most common in Python are:

  • Lists - A collection of values where the order is important.
  • Tuples - A sequence of values where the order is important.
  • Sets - A collection of values where membership in the set is important.
  • Dictionaries - A collection of key-value pairs, where you can access values based on their keys.

50. What is the difference between Python lists, tuples, and sets? When should you use one over the other?

Lists, tuples, and sets are compound data types that serve a similar purpose: storing collections of items in Python. However, knowing the differences between each of them is crucial for compute and memory efficiency.

  • Lists are mutable collections that are ordered and allow duplicate elements. They are versatile and offer a broad range of operations such as accessing, adding, and removing items. They are suitable when the order of items matters or when you need to change the collection over time.
  • Tuples are similar to lists in that they are ordered collections. However, they are immutable, meaning you cannot change their content once defined. Tuples are faster than lists, and they can be used in situations where the content will remain constant.
  • Sets are unordered collections that do not allow duplicate elements. Because they are unordered, you cannot access elements by an index. Sets are faster than both lists and tuples for membership testing, i.e., checking if an item is in the collection. They are also beneficial when you need to remove duplicates from a collection or perform mathematical set operations such as union, intersection, and difference.

51. How would you find duplicate values in a dataset for a variable in Python?

You can check for duplicates using the Pandas duplicated() method. This will return a boolean series which is TRUE only for unique elements.


52. What is list comprehension in Python? Provide an example.

List comprehension is used to define and create a list based on an existing list. For example, if we wanted to separate all the letters in the word “retain,” and make each letter a list item, we could use list comprehension:

r_letters = [ letter for letter in 'retain' ]
print( r_letters)

We can also use list comprehension for filtering. For example, to get all the vowels in the word “retain”, we do the following:

vowels = [vowel for vowel in 'retain' if vowel in ('a', 'e', 'i', 'o', 'u')]

If you are concerned about duplicate values, you can opt for sets instead, by replacing “[]” with “{}”.

unique_vowels = {vowel for vowel in 'retain' if vowel in ('a', 'e', 'i', 'o', 'u')}

53. What is sequence unpacking? Why is it important?

Sequence unpacking is a python operation that allows you to de-structure the elements of a collection and assign them directly to variables without the need for iteration. It provides a terse method for mapping variables to the elements of a compound data structure. For example:

# instead of:
x = coordinates[0]
y = coordinates[1]

# we can unpack a list:
x, y = coordinates

# we can also do the same for sets, tuples, and dictionaries. 

We can even swap the elements of two variables without the use of a third variable:

a = 3
b = 2
a, b = b, a

assert a == 2
assert b == 3

# no assertion errors

If the size of a collection is unclear, you can use the * operator on a variable to assign all extra items to said variable:

food = ('apples', 'oranges', 'carrots', 'cabbages', 'lettuce')
apples, oranges, *vegetables = food
# apples = 'apples', oranges = 'oranges',
# vegetables = ('carrots', 'cabbanges', 'lettuce')

54. Write a function that takes in a list of dictionaries with a key and list of integers and returns a dictionary with the standard deviation of each list.

Hint: need to use the equation for standard deviation to answer this question. Using the equation, allows us to take the sum of the square of the data value minus the mean, over the total number of data points, all in a square root.

55. Given a list of timestamps in sequential order, return a list of lists grouped by week (7 days) using the first timestamp as the starting point.

This question sounds like it should be a SQL question doesn’t it? Weekly aggregation implies a form of GROUP BY in a regular SQL or pandas question. In either case, aggregation on a dataset of this form by week would be pretty trivial.

56. Given two strings A and B, return whether or not A can be shifted some number of times to get B.


A = 'abcde'
B = 'cdeab'
can_shift(A, B) == True
A = 'abc'
B = 'acb'
can_shift(A, B) == False

Hint: This problem is relatively simple if we work out the underlying algorithm that allows us to easily check for string shifts between the strings A and B.

57. Given two strings, string1 and string2, write a function is_subsequence to find out if string1 is a subsequence of string2.

Hint: Notice that in the subsequence problem set, one string in this problem will need to be traversed to check for the values of the other string. In this case, it is string2.

Statistics and Probability Interview Questions

Statistics and probability questions for data analysts will usually come up on an onsite round as a test of basic fundamentals.

Statistics questions are more likely than probability questions to show up, as statistics are the fundamental building blocks for many analyst formulas and calculations.

58. Given uniform distributions X and Y and the mean 0 and standard deviation 1 for both, what’s the probability of 2X > Y? (Asked by Snapchat)

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?

Let’s look at this question a little closer.

We’re given two normal distributions. The values can either be positive or negative but each value is equally likely to occur. Since we know the mean is 0 and the standard deviation is 1, we understand that the distributions are also symmetrical across the Y-axis.

In this scenario, we are equally likely to randomly sample a value that is greater than 0 or less than 0 from the distribution.

Now, let’s take examples of random values that we could get from each scenario. There are about six different scenarios here.

  • X > Y: Both positive
  • X > Y: Both negative
  • X < Y: Both positive
  • X < Y: Both negative
  • X > Y: X is positive Y is negative
  • X < Y: X is negative Y is positive

We can simulate a random sampling by equating that all six are equally likely to occur. If we play out each scenario and plug the variables into 2X > Y, then we see about half of the time the statement is true, or 50%.

Why is this the case? Generally if we go back to the fact that both distributions are symmetrical across the Y-axis, we can intuitively understand that if both X and Y are random variables across the same distribution, we will see 2X as being on average double positive or double negative the value that Y is.

59. 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.

60. 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?

Hint: In order to decrease our margin of error, we’ll probably have to increase our sample size. But by how much?

61. What’s the Difference Between Correlation and Covariance?

Covariance measures the linear relationship of variables, while correlation measures the strength and direction of the relationship. Therefore, correlation is a function of a covariance. For example, a correlation between two variables does not mean that the change in variable X caused the change in variable Y’s value.

62. How would you describe probability distribution to a non-technical person?

Probability distributions represent random variables and associated probabilities of different outcomes. In essence, a distribution maps the probability of various outcomes.

For example, a distribution of test grades might look similar to a normal distribution, AKA bell curve, with the highest number of students receiving Cs and Bs, and a smaller percentage of students failing or receiving a perfect score. In this way the center of the distribution would be the highest, while outcomes at either end of the scale falling lower and lower.

63. What is a non-normal distribution? Provide an example.

A probability distribution is not normal if most of its observations do not cluster around the mean, forming the bell curve. An example of a non-normal probability distribution is a uniform distribution, in which all values are equally likely to occur within a given range. A random number generator set to produce only the numbers 1-5 would create such a not normal distribution, as each value would be equally represented in your distribution after several hundred iterations.

64. What is the probability that it’s raining in Seattle?

More context. You are about to get on a plane to Seattle. You call 3 random friends in Seattle and ask each if it’s raining. Each has a 2⁄3 chance of telling you the truth and a 1⁄3 chance of messing with you by lying. All 3 friends tell you that “yes” it is raining.

Hint: There are several ways to answer this question. Given that a frequentist approach operates on the set of known principles and variables given in the original problem, you can logically deduce that P(Raining)= 1-P(Not Raining).

Since all three friends have given you the same answer as to whether or not it’s raining, what can you determine about the relationship between P(Not Raining) and the probability that each of your friends is lying?

65. If given a univariate dataset, how would you design a function to detect anomalies? What if the data is bivariate?

Before jumping into anomaly detection, discuss what the meaning of a univariate dataset is. Univariate means one variable. For example, travel time in hours from your city to 10 other cities is given in an example list below:

12, 27, 11, 41, 35, 22, 18, 43, 26, 10

This kind of single column-data set is called a univariate dataset. Anomaly detection is a way to discover unexpected values in datasets. The anomaly means data exists that is different from the normal data. For example, you can see below the dataset where one data point is unexpectedly high intuitively:

12, 27, 11, 41, 35, 22, 76767676, 18, 43, 26, 10

66. You want to look at mean and median for a dataset. When would you use one measure over the other? How do you calculate the confidence interval of each measure?

You should answer these questions in your response:

  • Which measure has the widest application?
  • What happens when the dataset has values that are way above or below most other values?
  • How would your choice of metric be influenced by the data being non-continuous?

67. You have a biased and unbiased coin. You select a random coin and flip it two times. What is the probability that both flips result in the same side?

Hint: The first step in solving this problem is to separate it into two instances– one where you grab the fair coin, and one where you grab the biased coin. Solve for the probabilities of flipping the same side separately for both.

68. What could be the cause of a capital approval rate decrease?

Capital approval rates have gone down for our overall approval rate. Let’s say last week it was 85% and the approval rate went down to 82% this week which is a statistically significant reduction.

The first analysis shows that all approval rates stayed flat or increased over time when looking at the individual products.

  • Product 1: 84% to 85% week over week
  • Product 2: 77% to 77% week over week
  • Product 3: 81% to 82% week over week
  • Product 4: 88% to 88% week over week

Hint: This would be an example of Simpson’s Paradox which is a phenomenon in statistics and probability. Simpson’s Paradox occurs when a trend shows in several groups but either disappears or is reversed when combining the data.

69. How would you explain confidence intervals?

In probability, confidence intervals refer to a range of values that you expect your estimate to fall between if you were to rerun a test. Confidence intervals are a range that are equal to the mean of your estimate plus or minus the variation.

For example, if a presidential popularity poll had a confidence interval of 93%, encompassing a 50%-55% approval, it would be expected that, if you re-polled your sample 100 more times, 93 times the estimate would fall between the upper and lower values of your interval. Those other seven events would fall outside, which is to say either below the 50% or above 55%. More polling would allow you to get closer to the true population average, and narrow the interval.

70. You have to draw two cards from a shuffled deck, one at a time. What’s the probability that the second card is not an ace?

One question to add: does order matter here? Is drawing an ace on the second card the same thing as drawing an ace on the first card and still drawing a second card? Let’s see if we can solve and prove this out.

We can generalize to two scenarios when drawing two cards of getting an ace:

  1. Drawing an ace on the first card and an ace on the second card
  2. Drawing not an ace on the first card and an ace on the second card

If we model the probability of the first scenario we can multiply the two probabilities of each occurrence to get the actual probability.

A/B Testing and Experimentation

A/B testing and experimentation questions for data analysts tend to explore the candidate’s ability to properly conduct A/B tests. You should have strong knowledge of p-values, confidence intervals, and assessing the validity of the experiment.

71. The PM checks the results of an A/B test (standard control and variant) and finds a .04 p-value. How would you assess the validity of the result? How would you assess the validity of the result?

In this particular question, you’ll need to clarify the context of how the A/B test was set up and measured.

If we have an A/B test to analyze, there are two main ways in which we can look for invalidity. We could likely re-phrase the question to: How do you set up and measure an A/B test correctly?

Let’s start out by answering the first part of figuring out the validity of the set up of the A/B test:

1. How were the user groups separated?

Can we determine that the control and variant groups were sampled accordingly to the test conditions?

If we’re testing changes to a landing page to increase conversion, can we compare the two different users in the groups to see different metrics in which the distributions should look the same?

For example, if the groups were randomly bucketed, does the distribution of traffic from different attribution channels still look similar or is the variant A traffic channel coming primarily from Facebook ads and the variant B from email? If testing group B has more traffic coming from email then that could be a biased test.

2. Were the variants equal in all other aspects?

The outside world often has a much larger effect on metrics than product changes do. Users can behave very differently depending on the day of week, the time of year, the weather (especially in the case of a travel company like Airbnb), or whether they learned about the website through an online ad or found the site organically.

If the variants A’s landing page has a picture of the Eifel Tower and the submit button on the top of the page, and variant B’s landing page has a large picture of an ugly man and the submit button on the bottom of the page, then we could get conflicting results based on the change to multiple features.


Looking at the actual measurement of the p-value, we understand that industry standard is .05, which means that 19 out of 20 times that we perform that test, we’re going to be correct that there is a difference between the populations.

However, we have to note a couple of things about the test in the measurement process.

What was the sample size of the test?

Additionally, how long did it take before the product manager measured the p-value? Lastly, how did the product manager measure the p-value and did they do so by continually monitoring the test?

If the product manager ran a T-test with a small sample size, they could very well easily get a p-value under 0.05. Many times, the source of confusion in AB testing is how much time you need to make a conclusion about the results of an experiment.

The problem with using the p-value as a stopping criterion is that the statistical test that gives you a p-value assumes that you designed the experiment with a sample and effect size in mind. If we continuously monitor the development of a test and the resulting p-value, we are very likely to see an effect, even if there is none. The opposite error is also common when you stop an experiment too early, before an effect becomes visible.

The number one most important reason is that we are performing a statistical test every time you compute a p-value and the more you do it, the more likely you are to find an effect.

How long should we recommend an experiment to run for then? To prevent a false negative (a Type II error), the best practice is to determine the minimum effect size that we care about and compute, based on the sample size (the number of new samples that come every day) and the certainty you want, how long to run the experiment for, before starting the experiment.

72. How can you effectively design an A/B test? Are there times when A/B testing shouldn’t be used?

Split testing fails when you have unclear goals. That’s why it’s imperative to start backwards with that goal. Is it to increase conversions? Are you trying to increase engagement and time spent on page? Once you have that goal, you can start experimenting with variables.

73. How much traffic would you need to drive to a page for the result of an A/B test to be statistically significant?

Statistical significance - or having 95% confidence in the results - requires the right volume of data. That’s why most A/B tests run for 2-8 weeks. Comparing metrics like conversions is fairly easy to calculate. In fact, most A/B tools have built-in calculators.

74. How would you conduct an experiment to test a new ETA estimate feature in Uber? How would you know if your results were significant?

Hint: A question like this asks you to think hypothetically about A/B testing. But the format is the same: Walk the interviewer through setting up the test and how you arrive at a statistically relevant result.

75. How would you explain P-value to someone who is non-technical?

The p-value is a fundamental concept in statistical testing. First, why does this kind of question matter? What an interviewer is looking for here is can you answer this question in a way that both conveys your understanding of statistics but can also answer a question from a non-technical worker that doesn’t understand why a p-value might matter.

For example, if you were a data scientist and explained to a PM that the ad campaign test has a .08 p-value, why should the PM care about this number?

76. Your company wants to test new marketing channels. How would you design an A/B test for the most efficient marketing spend?

The new channels include: Youtube Ads, Google search ads, Facebook ads, direct mail campaigns.

To start, you’d want to follow up with some clarifying questions and make some assumptions. Let’s assume, for example, that most efficient means lowest cost per conversion, and that we’ve been asked to spend evenly across all platforms.

77. You want to run an experiment, but found that the distribution of the dataset is not normal. What kind of analysis would you run and how would you measure which variant won?

Understanding whether your data abides by or violates a normal distribution is an important first step in your subsequent data analysis.

This understanding will change which statistical tests you want to use if you need to immediately look for statistical significance. For example, you cannot run a t-test if your distribution is non-normal since this test uses mean/average as a way to find differences between groups.

78. You want to A/B test pricing levels for subscriptions. The PM asks you to design a two-week test. How do you approach this? How do you determine if the pricing increase is a good business decision?

Hint: Is A/B testing a price difference a good idea? Would it encourage users to opt-out of your test, if they were seeing different prices for a product?

Is there a better way to test pricing?

79. A survey shows that app users who use an optional location-sharing feature are “less happy” with the app as a whole. Is the feature actually causing users to be unhappy?

Causal relationships are hard to come by, and truly determining causality is tough as the world is full of confounding variables. Because of this, instead of causality, we can dissect the correlation between the location sharing feature and the user unhappiness level.

At its core, this interview question is testing how you can dig into the science and statistics behind their assumption. The interviewer is asking essentially a small variation of a traditional experimental design with survey research and wants to know how you would either validate or disprove this claim.

Product Metrics Data Analyst Questions

Metrics is a common product analyst interview question subject, and you’ll also see this type of question in product-oriented data analyst roles. In general, these questions test your ability to choose metrics to investigate problems or measure success. These questions require strong product sense to answer.

80. You’re given a list of marketing channels and their costs. What metrics would you use to determine the value of each marketing channel?

The first thing we’ll want to do when faced with an interview question like this one is to ask a few clarifying questions. Answer these questions first:

  • What is the company’s business model?
  • Is there one product or many?

Let’s say it’s a SaaS business that offers a free Studio model of their product, but makes their money selling enterprise subscriptions. This gives us a better sense of how they’re approaching their customers. They’re saying: here’s a good free tool, but you can pay to make it even better.

  • How many marketing channels are there?

Imagine what your analysis would look like if the answer to this question was “a few.” Now imagine what your analysis would look like if the answer to this question was “hundreds.”

  • Are some marketing channels bigger than others? What’s the proportion?

Mode could be spending 90% of its marketing budget on Facebook Ads and 10% on affiliate marketing, or vice versa. We can’t know unless we ask.

  • What is meant by “the value of each marketing channel?”

Here’s where we start getting into the meat of the question.

81. A PM at Facebook comes to you and tells you that friend requests are down 10%. What do you do?

This question has been asked in Facebook data analyst interviews. See an example solution to this question on YouTube.

82. What are some reasons why the average number of comments per user would be decreasing, and what metrics would you look into?

More context. Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slight decrease in the average number of comments per user from January to March in this city. The company has been consistently growing new users in the city from January to March.

Let’s model an example scenario to help us see the data.

  • Jan: 10000 users, 30000 comments, 3 comments/user
  • Feb: 20000 users, 50000 comments, 2.5 comments/user
  • Mar: 30000 users, 60000 comments, 2 comments/user

We’re given information that total user count is increasing linearly, which means that the decreasing comments/user is not an effect of a declining user base creating a loss of network effects on the platform. What else can we hypothesize, then?

83. How would you measure the success of Facebook Groups?

Start here: What is the point of Facebook Groups? Primarily we could say Facebook Groups provides a way for Facebook users to connect with other users through a shared interest or real-life/offline relationship.

How could we use the goals of Facebook Groups to measure success?

84. What kind of analysis would you conduct to recommend UI changes?

More context. You have access to a set of tables summarizing user event data for a community forum app. You’re asked to conduct a user journey analysis using this data with the eventual goal of improving the user interface.

85. How would you measure the success of Uber Eats?

See a step-by-step solution for this question on YouTube.

86. What success metrics would you be interested in for an advertising-driven consumer product?

With this question, you might define success in terms of advertising performance. A few metrics you might be interested in are:

  • CTR
  • CPC
  • Pageviews or daily actives (for apps)
  • Conversion rate
  • Number of purchases
  • Cost per conversion

87. How do success metrics change by product type?

Let’s look at two examples: An eCommerce product like Groupon vs. a subscription product like Netflix.

E-commerce metrics tend to be related to conversions and sales. Therefore, you might be interested in the number of purchases, conversion rate, quarterly or monthly sales, and cost of goods sold.

Subscription products tend to focus more on subscriber costs and revenue, like churn rates, cost of customer acquisition, average revenue per user, lifetime value, and monthly recurring revenue.

88. Given a dataset of raw events, how would you come up with a measurement to define what a “session” is for the company?

More context. Let’s say that you’re given event data from users on a social networking site like Facebook. A product manager is interested in understanding the average number of “sessions” that occur every day. However, the company has not technically defined what a “session” is yet.

The best the product manager can do is illustrate an example of a user browsing Facebook in the morning on their phone and then again during lunch as two distinct “sessions.” There must be a period of time where the user leaves Facebook to do another task before coming back again anew.

89. Some of the success metrics for the LinkedIn newsfeed algorithm are going up, while others are going down. What would you look at?

See a solution for this question on YouTube.

90. The number of products or subscriptions sold is declining. How would you investigate this problem?

This question provides you with a chance to show your expertise in analyzing sale metrics and KPIs. Some of the challenges you might bring up include competitor price analysis, examining core customer experiences, and investigating evolving customer desires. Your goal in your response should be to outline how you would perform root cause analysis.

Tip. Start with some clarifying questions like, What is the product? Who is the audience? How long has the decline in sales persisted?

91. You’re asked to investigate how to improve search results. What metrics would you investigate? What would you look at to determine if current search results are effective?

More context. Specifically, we want to improve search results for people looking for things to do in San Francisco.

Excel Interview Questions

Excel is still a widely used tool by data analysts, and in interviews, Excel questions typically focus on advanced features. These questions might ask for definitions, or you may be required to perform some Excel tasks.

Data analysts should also have strong knowledge of data visualization. Data visualization interview questions typically focus on design and presenting data, and may be more behavioral in nature. Be prepared to talk about how you make data accessible on dashboards.

92. Explain the Excel VLOOKUP function? What are the limitations of VLOOKUP?

This function allows users to find data from one column, and return a corresponding value from another.

For example, if you were analyzing a spreadsheet of customer data, you might use VLOOKUP to find a customer name and the corresponding phone number.

One limitation of VLOOKUP is that it only looks to the right of the column you are analyzing. For example, you couldn’t return a value from column A, if you used column B as the lookup column.

Another limitation is that VLOOKUP only returns the first value; if the spreadsheet contains duplicate records, you wouldn’t see any duplicates.

93. What is conditional formatting in Excel? When is a good time to use conditional formatting?

Conditional formatting allows users to change the appearance of a cell based on specified conditions.

Using conditional formatting, you can quickly highlight cells or ranges of cells, based on your conditions. Data analysts use conditional formatting to visualize data, to identify patterns or trends, or to detect potential issues.

94. What are your favorite data visualization tools?

Data analysts will get asked what tools they have experience with. Choose a few that you’re most comfortable with and explain the features that you like.

95. What are some challenges you’ve experienced working with large volumes of data?

One tip: Think of questions like this in terms of Big Data’s 5 Vs: volume, velocity, variety, veracity and value.

96. Can you use multiple data formats in pivot tables?

Data can be imported from a variety of sources by selecting the Data tab and clicking Get External Data > From Other Sources. Excel worksheet data, data feeds, text files and other such data formats can be imported, but you will need to create relationships between the imported tables and those in your worksheet before using them to create a pivot table.

97. When creating a visualization, you suspect data is missing. What do you do?

In your answer, provide an overview of your data validation process. For example, you might say, “The first step I would do would be to prepare a data validation report, which reveals why the data failed.” Then, you might talk through strategies for analyzing the dataset or techniques to process missing data, like deletion or mean/median/mode imputation.

Visualization Interview Questions

Data visualization involves presenting data in a graphical or pictorial format. This allows viewers to see data trends and patterns that may not be easy to understand in text-based data. Tableau, Power BI, and Python libraries such as Matplotlib and Seaborn are some of the most commonly used tools for data visualization.

98. Discuss your experience with creating visualizations in tools such as Tableau, Power BI, or Python. What distinct features have you utilized in each?

This question requires you to detail your hands-on experience with the mentioned tools. It involves discussing specific features you have used in Tableau, Power BI, and Python, such as creating different types of charts, setting up dashboards, or using Python libraries like Matplotlib and Seaborn for custom visualizations.

99. What is DAX and why is it important in Power BI?

DAX, or Data Analysis Expressions, is a library of functions and operators used to create formulas in Power BI, Analysis Services, and Power Pivot in Excel. These formulas, or expressions, are used to define custom calculations for tables and fields, and to manipulate data within the model.

100. Imagine you’re working on a sales report and you have a table of daily sales data. You want to calculate the monthly sales total. How could you use DAX to do this?

This question tests your understanding of DAX time-intelligence functions. A suitable response could be:

I would use a combination of the SUM and CALCULATE functions along with a Date table. First, I would create a measure using the SUM function to total the sales. Then, I would use the CALCULATE function along with the DATESMTD (Dates Month to Date) function to calculate the monthly total. The DAX expression would look something like this:

*Monthly Sales = CALCULATE(SUM(Sales[Daily Sales]), DATESMTD('Date'[Date]))*

101. Suppose a company has collected a large dataset on customer behavior, including demographics, transaction data, browsing history, and customer service interactions. You are tasked with presenting this data to the executive team, who are not data professionals. How would you go about this?

This question assesses your ability to analyze complex datasets and create straightforward, impactful visualizations. Your response might include:

“Understanding the audience is key. For an executive summary, it’s important to focus on high-level insights. I would start by performing exploratory data analysis to identify key trends and relationships within the data. From this, I could determine which aspects are most relevant to the executive team’s interests and strategic goals.

For visualization, I would use a tool like Tableau or Power BI, known for their user-friendly, interactive dashboards. To make the data more digestible, I would utilize various chart types such as bar graphs for categorical data comparison, line graphs for trend analysis, or pie charts for proportions.

To add an interactive element, I’d implement filters to allow executives to view data for different demographics, products, or time periods. It’s crucial to keep the design clean and ensure the visuals tell a clear story. For the presentation, I would walk them through the dashboard, explain key insights, and address any questions.”

102. You are working for an e-commerce company that needs a real-time dashboard to monitor sales across various product categories. Would you use Tableau or Power BI for this task? How would you leverage the chosen tool’s features to create the dashboard?

Your response should demonstrate your knowledge of both Tableau and Power BI and your ability to select the most appropriate tool for a specific task.

“For real-time sales monitoring, both Tableau and Power BI can be effective. However, if the company uses Microsoft’s suite of products and requires extensive integration with these services, I would lean towards Power BI as it’s part of the same ecosystem.

Power BI has robust real-time capabilities. I would leverage Power BI’s DirectQuery feature to connect to the sales database, ensuring the data displayed on the dashboard is always up-to-date. The tool also allows for streaming datasets that can be used for continuously streaming and updating data.

To visualize sales, I would design a dashboard that includes key metrics such as total sales, sales by product category, and changes in sales over time. I would also include slicers to allow users to filter data by region, time period, or other relevant dimensions.

Power BI also allows creating alerts based on KPIs that could notify the team when a sales target is reached or when there are significant changes in sales trends.”

More Resources for Data Analyst Interviews

If you are interested in a career path for a data analyst or have a data analyst interview coming up, review Interview Query’s data science course, which includes modules in SQL, Python, product metrics and statistics. SQL is hands-down the most commonly asked subject in data analyst interviews. See our list of 50+ SQL data science interview questions or our guide to SQL questions for data analysts.

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