Data Analyst Interview Questions

A great way to land a job as a Data Analyst is to spend some time before the interview researching the position, the field of data analytics, and how you have worked with data in the past. This article will provide some sample interview questions, as well as tips for answering them, to help with your pre-interview jitters.

What to Expect in a Data Analyst Interview

Interviewing for a job can be stressful, especially if you don’t know what to expect. That’s why the more you prepare for a data analytics interview, the more you can anticipate the sorts of questions they might ask and be ready to make a great impression. While no two interviews are the same, there are some common areas interviewers may focus their questions on, as well as skills and qualifications they will be looking for you to discuss at the interview.

An interviewer will likely ask a balance of industry-relevant and personal questions or some combination of both within the same question. For example, they not only want you to demonstrate your familiarity with Excel but may also want to know which sorts of macros you created to expedite data analysis and why. Similarly, they will likely inquire about the hard and soft skills you’ve acquired. To this end, they might pose questions such as the role teamwork or collaboration plays in your analytic process or how creativity informs your experience with data visualization. Ideally, your answers to these questions will shed light on your experience working with various tools and analytic software packages and how your personality type can play an integral role in their organization.

Top Interview Questions for Data Analysts

The following seven questions represent some typical data analytics interview questions that may be posed to you by a hiring committee.

Question #1: What is data cleansing, and how do you perform it?

This is one of the most-asked questions in a data analytics interview. If you are asked this question, you will need to demonstrate that you know what data cleansing is. Data cleansing is the act of going through large datasets to locate any inconsistencies or errors so that the data is ready to be analyzed. Then, you will likely want to mention that there are several ways to clean data, such as:

• Remove any observations that aren’t relevant or that are duplicates.
• Address any structural errors, such as typos, strange names, or improperly capitalized words.
• Delete unwanted outliers, like those that result from improperly entering data.
• Account for missing data, which can involve removing observations that have missing values, input missing values that are based on additional observations, or making changes to how the data is being used so that null values are adequately navigated.

Question #2: What are the most helpful tools for performing data analysis?

Because Data Analysts use different platforms and tools to perform analytical tasks, this question is popular in interviews because it gives you a chance to talk about the tools you already know and are comfortable using. Here are a few of the most-common tools Data Analysts use when working with data:

• Tableaufor data visualization.
• Microsoft Excel for organizing data, performing advanced calculations, and graphing the findings.
• Pythonfor creating data models and data visualizations.
• R for data mining, data manipulation, and data visualization, as well as complicated statistical calculations.
• SQL for designing and managing the data from relational database management systems.

Question #3: What data analysis project was most challenging for you to complete, and which was the most successful?

This question gives the interviewer an avenue into any strengths and weaknesses you bring to the table. It also gives them a chance to hear you discuss the methods you used to overcome these challenges and to briefly discuss what entails “success” in a data-driven project. When preparing to answer this question, it’s a good idea to consult the job description so that you can weave in some of the listed requirements or skills. Also, make sure to transform any challenging examples into learning experiences that taught you valuable and relevant lessons, such as realizing that the data you were using was too small of a sample size or that it was incomplete and how you learned from this experience.

Question #4: What characterizes a good data model?

If you’re given this question at an interview, there are a few characteristics you could mention to indicate that you know a model is developed and “good”:

• Its performance should be predictable so that the practice of establishing outcomes can be done at least to near-accurate standards.
• It should respond to changes and be adaptive to accommodate the needs of a growing business.
• It should be able to be scaled in proportion to any data changes.
• It should be customizable so that it generates a profit for customers or clients.

Question #5: Can you define “data wrangling”?

Data wrangling pertains to discovering, cleaning, organizing, validating, and manipulating raw data so that it is put into a better format to enable decision-making within an organization. When performed correctly, the data wrangling process has the potential to turn out vast amounts of data that originated from various sources into a more workable format. If given this question, you may also consider mentioning some common data wrangling techniques you are familiar with, such as joining, grouping, merging, and sorting.

Question #6: What’s the largest dataset you’ve ever handled?

At the heart of a question such as this is another data-related question: “Do you have experience handling large datasets?” Because most organizations are currently handling more data than they have in the past, they need to know that job candidates have the skills and training to work with large, complicated sets of data. Be specific when answering this question. You may consider mentioning the size of the data you’ve worked with before this interview, as well as what kind of data was included in the set. How many variables did you need to manage?

Remember that your answer doesn’t have to be from a work situation. In fact, you may have studied data analytics in a bootcamp, certificate program, or as part of a major in college and in your coursework, encountered datasets of different sizes. Try to recall any independent projects you completed during your studies or any portfolio pieces you assembled when formulating your response.

Question #7: What’s the difference between data profiling and data mining?

A question such as this tests your overall knowledge in the field of data analytics. Use this question as an opportunity to show off your data smarts. Data profiling involves identifying the various attributes of the data that’s in a dataset, such as functional dependencies, type of data, and distributions. On the other hand, data mining pertains to locating patterns within the data that weren’t initially obvious. To perform data mining, you must analyze the data to extract any correlations or dependencies.

Get Started Learning Data Analytics with Hands-On Classes

Data analytics is currently one of the most in-demand professions across the U.S. If you’re interested in learning how to analyze and visualize data, Noble Desktop’s Python for Data Science Bootcamp is a great starting point. This intensive, 30-hour course covers core Python skills that are useful for the data sciences, such as an overview of the various data types and how to create data visualizations. Noble also offers an 18-hour SQL Bootcamp in which students learn how to filter data, write SQL queries, and gather insights from data.

For those looking to learn specifically about data analytics, courses such as the Data Analytics Certificate or Data Analytics Technologies Bootcamp are available in-person in NYC, as well as in the live online format. These rigorous learning options cover core data analysis tools like SQL, Excel, and Tableau.

If you’re looking for learning options close to home, you can also search for live online and in-person data analytic courses with the help of Noble’s Classes Near Me tool. Over 340 courses are currently available by Noble and other top educational providers in topics like data visualization and data analytics, among others.