84.51 Degrees Data Analyst Interview Questions + Guide in 2025

84.51 Degrees is a data science and analytics company that leverages data to drive insights and strategies for retail and consumer products.

As a Data Analyst at 84.51 Degrees, you will play a crucial role in transforming raw data into actionable insights that influence business decisions. Key responsibilities include analyzing complex datasets, performing statistical analyses, and developing visualizations to communicate findings effectively. You will also be expected to collaborate with cross-functional teams to understand business needs and provide data-driven recommendations. A successful candidate will possess strong analytical skills, a solid foundation in statistics and probability, and proficiency in SQL for data manipulation. Additionally, familiarity with data visualization tools and an understanding of algorithms will be beneficial. The ideal candidate will demonstrate a passion for data, possess excellent problem-solving abilities, and exhibit effective communication skills to convey complex concepts in an understandable manner.

This guide will help you prepare for your interview by highlighting the critical skills and competencies required for the Data Analyst role at 84.51 Degrees, ensuring you can articulate your qualifications and experiences effectively.

The interview process for a Data Analyst position at 84.51 Degrees is structured to assess both technical skills and cultural fit within the company. It typically consists of several rounds, each designed to evaluate different aspects of a candidate's qualifications and personality.

1. Initial Screening

The process begins with an initial screening, which is usually a phone interview conducted by a recruiter. This conversation focuses on your background, interest in the role, and understanding of the company. The recruiter will also gauge your fit for the company culture and may ask about your experiences related to data analysis and problem-solving.

2. Technical Assessment

Following the initial screening, candidates often participate in a technical assessment. This may take place over video call or in-person and typically involves solving SQL problems or discussing data analysis techniques. Candidates should be prepared to demonstrate their analytical thinking and problem-solving skills through practical exercises, such as case studies or coding challenges.

3. Behavioral Interview

The next step usually involves a behavioral interview, where candidates meet with team members or managers. This round focuses on understanding how you approach teamwork, handle challenges, and apply your analytical skills in real-world scenarios. Expect questions that explore your past experiences, particularly those that highlight your ability to work with data sets and collaborate with others.

4. Onsite or Final Interview

The final stage of the interview process may include an onsite interview or a series of back-to-back interviews conducted virtually. This round often consists of multiple interviews with different team members, including technical and behavioral assessments. Candidates may be asked to explain their thought processes in detail, tackle case studies, and discuss their previous projects. This is also an opportunity for you to ask questions about the team dynamics and the specific work you would be doing.

5. Offer and Feedback

After the final interviews, candidates typically receive feedback relatively quickly. If selected, you will be presented with an offer, and the company may provide insights into the next steps regarding onboarding and integration into the team.

As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those that relate to your analytical skills and experiences.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

The interview process at 84.51 degrees typically consists of multiple rounds, starting with a screening interview, followed by a more in-depth technical and behavioral interview. Familiarize yourself with this structure so you can prepare accordingly. Expect a mix of personal questions, technical challenges, and case studies that assess your problem-solving abilities. Knowing what to expect will help you feel more at ease during the interview.

Prepare for Behavioral Questions

Behavioral questions are a significant part of the interview process. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Be ready to discuss your past experiences, particularly those that highlight your teamwork, problem-solving skills, and adaptability. Interviewers are interested in understanding how you approach challenges and work with others, so be genuine and reflective in your answers.

Brush Up on Technical Skills

Given the emphasis on SQL and analytics in the role, ensure you are comfortable with SQL queries and data manipulation. Practice solving SQL problems, as well as basic statistics and probability concepts. You may be asked to explain technical concepts in simple terms, so be prepared to articulate your thought process clearly. Additionally, familiarize yourself with data visualization techniques, as you may be asked to present data in a meaningful way.

Engage with Case Studies

Expect to encounter case study questions that require you to analyze data and provide insights. Practice working through case studies related to data trends and analytics. Be prepared to discuss your thought process and the rationale behind your conclusions. This will demonstrate your analytical skills and ability to apply theoretical knowledge to real-world scenarios.

Show Enthusiasm for the Company

Demonstrating genuine interest in 84.51 degrees and its mission can set you apart from other candidates. Research the company’s values, recent projects, and industry challenges. Be ready to articulate why you want to work there and how your skills align with their goals. This not only shows your enthusiasm but also helps you assess if the company is a good fit for you.

Be Yourself

The interviewers at 84.51 degrees are known for being friendly and approachable. Use this to your advantage by being authentic and personable during your interviews. Share your passions, interests, and experiences that have shaped your career path. This will help create a connection with your interviewers and make the conversation more engaging.

Prepare Questions

At the end of your interviews, you will likely have the opportunity to ask questions. Prepare thoughtful questions that reflect your interest in the role and the company. Inquire about team dynamics, ongoing projects, or the company’s approach to data analytics. This not only shows your enthusiasm but also helps you gather valuable information to make an informed decision if you receive an offer.

By following these tips and preparing thoroughly, you will be well-equipped to navigate the interview process at 84.51 degrees and showcase your potential as a Data Analyst. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Analyst interview at 84.51 degrees. The interview process will likely assess your technical skills in statistics, SQL, and data analytics, as well as your problem-solving abilities and cultural fit within the company. Be prepared to discuss your past experiences, technical knowledge, and how you approach data-driven challenges.

Statistics

1. Can you explain the difference between descriptive and inferential statistics?

Understanding the distinction between these two branches of statistics is crucial for data analysis.

How to Answer

Describe how descriptive statistics summarize data from a sample, while inferential statistics make predictions or inferences about a population based on a sample.

Example

“Descriptive statistics provide a summary of the data, such as mean, median, and mode, which helps in understanding the data set. In contrast, inferential statistics allow us to make predictions or generalizations about a larger population based on a sample, using techniques like hypothesis testing and confidence intervals.”

2. How would you handle missing data in a dataset?

This question assesses your approach to data integrity and analysis.

How to Answer

Discuss various methods such as imputation, deletion, or using algorithms that support missing values, and explain your reasoning for choosing a particular method.

Example

“I would first analyze the extent and pattern of the missing data. If the missing data is minimal, I might use imputation techniques like mean or median substitution. However, if a significant portion is missing, I would consider using algorithms that can handle missing values or even removing those records if they don't significantly impact the analysis.”

3. What is a p-value, and how do you interpret it?

This question tests your understanding of hypothesis testing.

How to Answer

Explain the concept of p-value in the context of statistical significance and hypothesis testing.

Example

“A p-value is the probability of obtaining results at least as extreme as the observed results, assuming that the null hypothesis is true. A low p-value (typically less than 0.05) indicates strong evidence against the null hypothesis, leading us to reject it in favor of the alternative hypothesis.”

4. Describe a time when you used statistical analysis to solve a problem.

This question allows you to showcase your practical experience.

How to Answer

Provide a specific example where you applied statistical methods to derive insights or solve a business problem.

Example

“In my previous role, I analyzed customer purchase data to identify trends. By applying regression analysis, I was able to predict future sales based on seasonal trends, which helped the marketing team tailor their campaigns effectively.”

SQL

1. How would you write a SQL query to find the top 10 customers by total sales?

This question assesses your SQL skills and ability to manipulate data.

How to Answer

Outline the SQL syntax you would use, including SELECT, FROM, JOIN, and ORDER BY clauses.

Example

“I would use the following SQL query: SELECT customer_id, SUM(sales) AS total_sales FROM sales_data GROUP BY customer_id ORDER BY total_sales DESC LIMIT 10; This query aggregates sales by customer and orders them to find the top 10.”

2. Can you explain the difference between INNER JOIN and LEFT JOIN?

This question tests your understanding of SQL joins.

How to Answer

Clarify how INNER JOIN returns only matching records, while LEFT JOIN returns all records from the left table and matched records from the right table.

Example

“An INNER JOIN returns only the rows where there is a match in both tables, while a LEFT JOIN returns all rows from the left table, along with matched rows from the right table. If there’s no match, NULL values are returned for the right table’s columns.”

3. Write a SQL query to find duplicate records in a table.

This question evaluates your ability to identify data quality issues.

How to Answer

Describe the SQL query you would use to find duplicates based on specific columns.

Example

“I would use the following SQL query: SELECT column_name, COUNT(*) FROM table_name GROUP BY column_name HAVING COUNT(*) > 1; This query groups records by the specified column and counts occurrences, returning only those with more than one entry.”

4. How do you optimize a slow-running SQL query?

This question assesses your problem-solving skills in database management.

How to Answer

Discuss techniques such as indexing, query restructuring, and analyzing execution plans.

Example

“To optimize a slow-running SQL query, I would first analyze the execution plan to identify bottlenecks. Then, I might add indexes to frequently queried columns, restructure the query to reduce complexity, or limit the dataset being processed by using WHERE clauses effectively.”

Data Analytics

1. Describe a data analysis project you worked on. What was your approach?

This question allows you to demonstrate your analytical skills and project experience.

How to Answer

Outline the project, your role, the tools you used, and the impact of your analysis.

Example

“I worked on a project analyzing customer feedback data to improve product features. I used Python for data cleaning and visualization, and Tableau for presenting insights. My analysis led to actionable recommendations that increased customer satisfaction by 20%.”

2. How do you ensure the accuracy of your data analysis?

This question tests your attention to detail and commitment to quality.

How to Answer

Discuss methods such as data validation, cross-referencing with other data sources, and peer reviews.

Example

“I ensure accuracy by implementing data validation checks during the data cleaning process, cross-referencing results with other reliable data sources, and conducting peer reviews of my analysis to catch any potential errors.”

3. What tools do you use for data visualization, and why?

This question assesses your familiarity with data visualization tools.

How to Answer

Mention specific tools you are proficient in and explain why you prefer them.

Example

“I primarily use Tableau and Power BI for data visualization because they offer user-friendly interfaces and powerful features for creating interactive dashboards. They allow me to present complex data in a clear and engaging manner, making it easier for stakeholders to understand insights.”

4. How would you approach a case study involving customer data?

This question evaluates your problem-solving and analytical thinking.

How to Answer

Outline your approach to understanding the problem, analyzing the data, and deriving insights.

Example

“I would start by clearly defining the objectives of the case study. Then, I would gather and clean the customer data, perform exploratory data analysis to identify trends, and apply statistical methods to derive insights. Finally, I would present my findings with actionable recommendations to improve customer engagement.”