Question: 1 Can you share details about the largest data set you’ve worked with?
How many entries and variables did the data set comprise? What kind of data was included?

Answer:

How to Answer
Working with large datasets and dealing with a substantial number of variables and columns is important for a lot of hiring managers. When answering the question, you don’t have to reveal background information about the project or how you managed each stage. Focus on the size and type of data.
Answer Example
“I believe the largest data set I’ve worked with was within a joint software development project. The data set comprised more than a million records and 600-700 variables. My team and I had to work with Marketing data which we later loaded into an analytical tool to perform EDA.”

Question: 2 In your role as a data analyst, have you ever recommended a switch to different processes or tools?
What was the result of your recommendation?

Answer:

How to Answer
For hiring managers, it’s important that they pick a data analyst who is not only knowledgeable but also confident enough to initiate a change that would improve the company’s status quo. When talking about the recommendation you made, give as many details as possible, including your reasoning behind it. Even if the recommendation you made was not implemented, it still demonstrates that you’re driven and you strive for improvement.

Answer Example
“Although data from non-technical departments is usually handled by data analysts, I’ve worked for a company where colleagues who were not on the data analysis side had access to data. This brought on many cases of misinterpreted data that caused significant damage to the overall company strategy. I gathered examples and pointed out that working with data dictionaries can actually do more harm than good. I recommended that my coworkers depend on data analysts for data access. Once we implemented my recommendation, the cases misinterpreted data dropped drastically.”

Question: 3 How would you assess your writing skills?
When do you use written form of communication in your role as a data analyst?

Answer:

How to Answer
Working with numbers is not the only aspect of a data analyst job. Data analysts also need strong writing skills, so they can present the results of their analysis to management and stakeholders efficiently. If you think you are not the greatest data “storyteller”, make sure you’re making efforts in that direction, e.g. through additional training.

Answer Example
“Over time, I’ve had plenty of opportunities to enhance my writing skills, be it through email communication with coworkers, or through writing analytical project summaries for the upper management. I believe I can interpret data in a clear and succinct manner. However, I’m constantly looking for ways to improve my writing skills even further.”

Question: 4 Have you ever used both quantitative and qualitative data within the same project?

Answer:

How to Answer
To conduct meaningful analysis, data analysts must use both the quantitative and qualitative data available to them. In surveys, there are both quantitative and qualitative questions, so merging those 2 types of data presents no challenge whatsoever. In other cases, though, a data analyst must use creativity to find matching qualitative data. That said, when answering this question, talk about the project where the most creative thinking was required.

Answer Example
“In my experience, I’ve performed a few analyses where I had qualitative survey data at my disposal. However, I realized I can actually enhance the validity of my recommendations by also implementing valuable data from external survey sources. So, for a product development project, I used qualitative data provided by our distributors, and it yielded great results.”

Question: 5 What is your experience in conducting presentations to various audiences?

Answer:

How to Answer
Strong presentation skills are extremely valuable for any data analyst. Employers are looking for candidates who not only possess brilliant analytical skills, but also have the confidence and eloquence to present their results to different audiences, including upper-level management and executives, and non-technical coworkers.
So, when talking about the audiences you’ve presented to, make sure you mention the following:

• Size of the audience;
• Whether it included executives;
• Departments and background of the audience;
• Whether the presentation was in person or remote, as the latter can be very challenging.

Answer Example
“In my role as a Data Analyst, I have presented to various audiences made up of coworkers and clients with differing backgrounds. I’ve given presentations to both small and larger groups. I believe the largest so far has been around 30 people, mostly colleagues from non-technical departments. All of these presentations were conducted in person, except for 1 which was remote via video conference call with senior management.”

Question: 6 Have you worked in an industry similar to ours?

Answer:

How to Answer
This is a pretty straightforward question, aiming to assess if you have industry-specific skills and experience. Even if you don’t, make sure you’ve prepared an answer in advance where you explain how you can apply your background skills from a different field to the benefit of the company.
Answer Example
“As a data analyst with financial background, I can say there are a few similarities between this industry and healthcare. I think the most prominent one is data security. Both industries utilize highly sensitive personal data that must be kept secure and confidential. This leads to 2 things: more restricted access to data, and, consequently, more time to complete its analysis. This has taught me to be more time-efficient when it comes to passing through all the security. Moreover, I learned how important it is to clearly state the reasons for requiring certain data for my analysis.”

Question: 7 Have you earned any certifications to boost your career opportunities as a Data Analyst?

Answer:

How to Answer
Hiring managers appreciate a candidate who is serious about advancing their career options through additional qualifications. Certificates prove that you have put in the effort to master new skills and knowledge of the latest analytical tools and subjects. While answering the question, list the certificates you have acquired and briefly explain how they’ve helped you boost your data analyst career. If you haven’t earned any certifications so far, make sure you mention the ones you’d like to work towards and why.
Answer Example
“I’m always looking for ways to upgrade my analytics skillset. This is why I recently earned a certification in Customer Analytics in Python. The training and requirements to finish it really helped me sharpen my skills in analyzing customer data and predicting the purchase behavior of clients.”

Question: 8 Name a few libraries used in Python for data analysis.

Answer:

These are the most important Python libraries you should mention.
Numpy is an essential library, as it used for matrices and arrays and includes methods for their manipulation.
Pandas is the second library, which is used in almost any data analysis performed in Python.
It includes data structures and operations for manipulating numerical tables and time series. It often uses numpy to produce linear math results and is, therefore, a lot faster than standard Python. So, a good knowledge of the Pandas library is a must if you’re a data analyst using Python.
Scipy and Scikit Learn –are two of the main machine learning libraries.

Sci-py boasts an impressive number of mathematical algorithms and high-level commands and classes to help data scientists in their data analysis tasks. Scikit learn was originally developed during a “Google Summer of Code” project, as a third party extension for Scipy. Scikit learn includes various classification, regression, and clustering algorithms, designed to be incorporated with the Scipy and Numpy packages.
And once you’re done with machine learning, you’ll also need a good way of visualizing the results. Matplotlib\ Seaborn are-visualization libraries, which are great for that.
Tensorflow, Keras and Pytorch are libraries for deep learning. If you want to train neural networks, for example in the context of NLPs or Computer Vision, these are the way to go. Here knowing the difference between Tensorflow 1 and Tensorflow 2 could be a bonus during an interview.

Question: 9 What is a Logistic regression?

Answer:

A logistic regression is one of the simplest classification models. It is widely used mainly due to its simplicity and ease of interpretation. Logistic regressions are well understood and studied throughout the years and thus are still a data scientist’s preferred classification choice on many occasions.
A logistic regression could be used in 2 distinct ways that sound different yet are reached in the same way, methodologically speaking.
The first use case is whenever we’ve got a categorical outcome. Examples are: Yes/No, Will buy/Won’t buy, and 0/1 situations. As any other classification method, a logistic regression would output the category it deems most probable to be the answer.
Speaking of probabilities, we reach the second use case. We could employ a logistic regression to determine the exact probability that an event is going to occur.
The mechanics of the two use cases follow the same path.
For instance, imagine a logistic regression predicts that a customer is 70% likely to buy and 30% likely to not buy. Under these conditions, the prediction will be classified as ‘Will buy’. Depending on our needs we could use one the probabilistic representation or simply the output class.
Finally, it is useful to note that we were discussing a binary logistic regression.
Binary here stands for an outcome with only 2 possibilities. The logistic regression model could be generalized to many categories, in which case it would be called a multinomial logistic regression.
At this point, you may or may not decide to mention the multinomial logistic regression. In 99% of the cases where we use the term ‘logistic regression’, we mean binary logistic regression. Referring to the multinomial case could prompt the interviewer to ask you additional questions on multinomial logistic regression, which would definitely be much trickier for you, especially if you have never used it.

Question: 10 Have you worked with comparatively large data sets in a project?
How did you collect and prepare the data for analysis?

Answer:

How to Answer
Working with large data sets can be challenging. So, with this question, the hiring manager wants to assess your ability to deal with the issues that might occur. If you have relevant experience, talk about the problems you have faced and how you managed to resolve them. In case you’ve never experienced any issues working with large data sets, describe the details of the project and all the stages of preparing the data for analysis.
Answer Example
“In the last company I was in, I often worked with large data sets from external suppliers. For example, survey responses for Customer Analytics projects. And that means a large data set with a huge sample size. So, to prepare the data for analysis, I’d go through the following steps. First, I’d run predetermined frequencies and queries to check the validity of the data. This helped me pin down various problems, such as missing data, problems with the data type, or skip-pattern errors in the survey data set. I’d check with the supplier, so we can implement the necessary corrections before we move forward with the analysis. Once done, I’d often consult with a Data Engineer to pick the most suitable analysis tool for a data set of this size. Finally, I’d load the data and start my analysis.”

Question: 11 Which tools have you used in each stage of your previous data analysis projects?

Answer:

How to Answer
A data analyst must be experienced in using a wide range of tools in the various phases of their analyses – from preparation, through exploration, to presenting the end results. Hiring managers know that a single tool can be utilized in multiple stages of the analytical process. So, if that’s your experience, make sure you highlight it. This will demonstrate your expertise in working with that specific tool. However, if you have worked with multiple tools throughout your experience, share that, too. That’s how you’ll showcase the span of your skills.
Answer Example
“In my experience as a data analyst, I’ve used a variety of tools that have helped me build up a strong skillset. In the preparation and exploration stages, I’ve mostly used Microsoft Excel and Microsoft Access, depending on the complexity of the data set. While in the exploration phase, I’ve also used SAS and SPSS to extract insights from the data. Apart from these statistical programs, I’ve employed analytical tools, such as Tableau and Cognos Analytics. I find Tableau, together with Power BI to be great tools for creating powerful dashboard visualizations. And, of course, Excel and PowerPoint are classic tools for building in-company presentations.”

Question: 12 In large companies, data is often stored in multiple data warehouses.
Have you ever worked on a complex analytical project, where you had to query multiple data warehouses in order to gather the required data?

Answer:

How to Answer
The technical complexity of your work as a Data Analyst may vary depending on the size of the companies you have worked at in the past. Strong technical skills is an important attribute of a Data Analyst’s background. Having experience retrieving data from multiple data warehouses demonstrates your understanding of databases, data structures, and programming languages.
The size of the companies you’ve worked for can affect the technical complexity of your tasks as a data analyst. That said, a strong technical skillset is always a plus in the eyes of your future employer. So, having retrieved data from multiple data warehouses in your work on past projects will showcase your expertise in databases and data structures, as well as in programming languages.
Answer Example
“I’ve had the chance to work for a big corporation in the past. I can say my work there has been of great importance to developing my technical skillset. Once, I queried against 5 different data warehouses to retrieve the data for a large-scale company project. Once I had all the necessary records and variables, I built a dataset I later utilized in my analysis.”

Question: 1 What tools or software do you prefer using in the various phases of data analysis and why?

Answer:

How to Answer
Although you might think you should have experience with as many tools as possible to ace this question, this is not the case. Each company uses specific data analysis tools, so it’s normal that your expertise is limited to those. Of course, if you have worked for a large number of companies, you’re bound to have exposure to a wider variety of analytical software. That said, the interviewer would like to know which tools you feel comfortable with, rather than the number of tools you’ve utilized.
Answer Example
“When it comes to data analysis tools, I can say I’m a traditionalist. That’s why I find Microsoft Excel and Microsoft Access most useful. I feel truly comfortable working with those, and they’re available in almost every company out there. Moreover, you can achieve great results with them with the right training.”

Question: 2 Have you ever created or worked with statistical models?
If so, please describe how you’ve used it to solve a business task.

Answer:

How to Answer
As a data analyst, you don’t specifically need experience with statistical models, unless it’s required for the job you’re applying for. If you haven’t been involved in building, using, or maintaining statistical models, be open about it and mention any knowledge or partial experience you may have.
Answer Example
“Being a data analyst, I can’t say I’ve had direct experience building statistical models. However, I’ve helped the statistical department by making sure they have access to the proper data and analyzing it. The model in question was built with the purpose of identifying the customers who were most inclined to buy additional products and predicting when they were most likely to make that decision. My job was to establish the appropriate variables used in the model and assess its performance once it was ready.”

Question: 3 Which step of a data analysis project do you enjoy the most?

Answer:

How to Answer
It’s normal for a data analyst to have preferences for certain tasks over others. However, you’ll most probably be expected to deal with all steps of a project – from querying and cleaning, through analyzing, to communicating findings. So, make sure you don’t show antipathy to any of the above. Instead, use this question to highlight your strengths. Just focus on the task you like performing the most and explain why it’s your favorite.
Answer Example
“If I had to select one step as a favorite, it would be analyzing the data. I enjoy developing a variety of hypotheses and searching for evidence to support or refute them. Sometimes, while following my analytical plan, I have stumbled upon interesting and unexpected learnings from the data. I believe there is always something to be learned from the data, whether big or small, that will help me in future analytical projects.”

Question: 4 What’s your knowledge of statistics and how have you used it in your work as a data analyst?

Answer:

How to Answer
Data analysts should have basic statistics knowledge and experience. That means you should be comfortable with calculating mean, median, and mode, as well as conducting significance testing. In addition, as a data analyst, you must be able to interpret the above in connection to the business. If a higher level of statistics is required, it will be listed in the job description.

Answer Example
“In my line of work, I’ve used basic statistics – mostly calculated the mean and standard variances, as well as significance testing. The latter helped me determine the statistical significance of measurement differences between two populations for a project. I’ve also determined the relationship between 2 variables in a data set, working with correlation coefficients.”

Question: 5 What scripting languages have you used in your projects as a data analyst?
Which one you you’d say you like best?

Answer:

How to Answer
Most large companies work with numerous scripting languages. So, a good command of more than one is definitely a plus. Nevertheless, if you aren’t well familiar with the main language used by the company you apply at, you can still make a good impression. Demonstrate enthusiasm to expand your knowledge, and point out that your fluency in other scripting languages gives you a solid foundation for learning new ones.
Answer Example
“I’m most confident in using SQL since that’s the language I’ve worked with throughout my Data Analyst experience. I also have a basic understanding of Python and have recently enrolled in a Python Programming course to sharpen my skills. So far, I’ve discovered that my expertise in SQL helps me advance in Python with ease.”

Question: 6 How many years of SQL programming experience do you have?
In your latest job, how many of your analytical projects involved using SQL?

Answer:

How to Answer
SQL is considered as one of the easiest scripting languages to learn. So, if you want to be competitive in the job market as a Data Analyst, you should be able to demonstrate an excellent command of SQL. Even if you don’t have many years of experience, highlight how your skills have improved with each new project.
Answer Example
“I’ve used SQL in at least 80% of my projects over a period of 5 years. Of course, I’ve also turned to other programming languages for the different phases of my projects. But, all in all, it’s SQL that I’ve utilized the most and consider the best for most of my data analyst tasks.”

Question: 7 Which Excel functions have you used on a regular basis so far?
Can you describe in detail how you’ve used Excel as an analytical tool in your projects?

Answer:

How to Answer
If you are an Excel expert, it would be difficult to list all the functions you have experience using. Instead, concentrate on highlighting the more difficult ones, particularly statistical functions. If you have experience utilizing the more challenging functions, hiring managers will presume you have experience using the more basic ones. Be sure to highlight your pivot table skills, as well as your ability to create graphs in Excel. If you have not attained these skills yet, it is worthwhile to invest in training to learn them.
If you’re an Excel pro, there is no need to recite each and every function you’ve used. Instead, highlight your advanced Excel skills, such as working with statistical functions, pivot tables, and graphs. Of course, if you lack the experience, it’s worth considering a specialized Excel training that will help you build a competitive skillset.

Question: 8 What’s your experience in creating dashboards?
Can you share what tools you’ve used for the purpose?

Answer:

How to Answer
Dashboards are essential for managers, as they visually capture KPIs and metrics and help them track business goals. That said, data analysts are often involved in both building and updating dashboards. Some of the best tools for the purpose are Excel, Tableau, and Power BI (so make sure you’ve got a good command of those). When you talk about your experience, outline the types of data visualizations, and metrics you used in your dashboard.
Answer Example
“In my line of work. I’ve created dashboards related to customer analytics in both Power BI and Excel. That means I used marketing metrics, such as brand awareness, sales, and customer satisfaction. To visualize the data, I operated with pie charts, bar graphs, line graphs, and tables.”

Question: 1 Imagine the following situation:
As a data analyst, you’ll often work with stakeholders who lack the technical background and deeper understanding of data and databases. Have you ever been in a situation like this and how did you handle this challenge?

Answer:

How to Answer
Data analysts often face the challenge of communicating findings to coworkers from different departments
or senior management with limited understanding of data. This requires excellent skills in interpreting specific terms using non-technical language. Moreover, it also requires extra patience to listen to your coworkers’ questions and provide answers in an easy-to-digest way. Show the interviewer that you’re capable of working efficiently with people from different types of background who don’t speak your “language”.
Answer Example
“In my work with stakeholders, it often comes down to the same challenge – facing a question I don’t have the answer to, due to limitations of the gathered data or the structure of the database. In such cases, I analyze the available data to deliver answers to the most closely related questions. Then, I give the stakeholders a basic explanation of the current data limitations and propose the development of a project that would allow us to gather the unavailable data in the future. This shows them that I care about their needs and I’m willing to go the extra mile to provide them with what they need.”

Question: 2 Tell me about a time you and your team were surprised by the results of a project.

Answer:

How to Answer
When starting an analysis, most data analysts have a rough prediction of the outcome rested on findings from previous projects. But there’s always room for surprise, and sometimes the results are completely unexpected. This question gives you a chance to talk about the types of analytical projects you’ve been involved in. Plus, it allows you to demonstrate your excitement about drawing new learnings from your projects. And don’t forget to mention the action you and the stakeholders took as a result of the unexpected outcome.
Answer Example
“While performing routine analysis of a customer database, I was completely surprised to discover a customer subsegment that the company could target with a new suitable product and a relevant message. That presented a great opportunity for additional revenue for the company by utilizing a subset of an existing customer base. Everyone on my team was pleasantly surprised and soon enough we began devising strategies with Product Development to address the needs of this newly discovered subsegment.”

Question: 3 Why do you think creativity is important for a data analyst?
How have you used creative thinking in your work so far?

Answer:

How to Answer
A data analyst is usually seen as a professional with a technical background and excellent math and statistical skills. However, even though creativity is not the first data analyst quality that comes to your mind, it’s still important in developing analytical plans and data visualizations, and even finding unorthodox solutions to data issues. That said, provide an answer with an example of your out-of-the-box way of thinking.
Answer Example
“I can say creativity can make all the difference in a data analyst’s work. In my personal experience, it has helped me find intriguing ways to present analysis results to clients. Moreover, it has helped me devise new data checks that identify issues resulting in anomalous results during data analysis.”

Question: 4 What are the most important skills a data analyst should possess to work efficiently with team members with various backgrounds, roles, and duties?

Answer:

How to Answer
When answering this question, keep in mind that the hiring manager would like to hear something different than “communication skills”. Think of an approach you’ve used in your role as a data analyst to improve the quality of work in a cross-functional team.
Answer Example
“I think the role of a data analyst goes beyond explaining technical terms in a non-technical language. I always strive to gain a deeper understanding of the work of my colleagues, so I can bridge my explanation of statistical concepts to the specific parts of the business they deal with, and how these concepts relate to the tasks at hand they need to solve.”

Question: 5 In your opinion, which soft skills are essential for a data analyst and why?

How to Answer

Soft skills, a.k.a. non-technical skills are important for working efficiently with others and maintaining a high level of performance. As with most professions, data analysts should be aware of how their behavior and work habits affect the members on their team. Therefore, here you should base your answer on past work experience and highlight an important soft skill you have developed

Answer Example
“I believe leadership skills are one of the major soft skills a data analyst should develop. The way I understand it, leadership means taking action to guide and help the members on your team. And this
doesn’t necessarily mean you have to be in a managerial position. In my line of work, leadership would translate into providing expert insights regarding company data and its interpretation. That’s a skill I’ve worked hard to develop over the years. I can say being confident in my abilities has now established me as a leading figure in my area, and my team members know they can rely on my expertise.”

Question: 6 Tell us about a project where, due to data limitations, the stakeholders couldn’t reach the answer they needed.

How did you resolve this issue?

How to Answer
The interviewer wants to be reassured that, as a data analyst, you can deal with all types of data challenges. That’s particularly important when collaborating with stakeholders who may lack an in-depth understanding of data. This question is also ideal for showcasing your problem-solving skills

Answer Example
“A few years back, I worked on a customer segmentation project initiated by the company executives. Unfortunately, they couldn’t come up with a substantial customer segmentation plan, as the
data in the customer data warehouse wasn’t robust enough. To help with the progress of the project, I
worked closely with the data warehouse team. Ourcollaboration resulted in outlining data initiatives
and actionable steps which ultimately led the project to its final goal.”