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Are you aspiring to become a data analyst? If so, you will likely have to go through an interview process.
I know that this can be daunting for you, but with the right preparation, it can be a breeze!
I myself have prepared for data analyst interviews, so I thought I’d share my experience.
In this article, I will discuss how to prepare for data analyst interviews in seven simple steps.
Let’s get started!
How to Prepare for Data Analyst Interview?
1. Update Your Data Science Portfolio
The first step to preparing for an interview is to make sure that your portfolio and resume are up-to-date.
This means making sure that you have included any recent data analysis projects or other relevant experience on your resume, as well as including links to any online portfolios or repositories where you have shared your data analysis work.
For data science portfolios, this means getting your messy Github page that you’ve used for a code dump in order! I’ve had many random personal data analysis projects done up during my days as a student in my Github, so I had to clean it up and add a simple README page.
If you’re using R for your personal data analysis project, make sure you upload them into an online version such as a Rmarkdown file on Rpubs or on any other websites. If you use bookdown or any other R packages to demonstrate data analysis work, make sure you clean them up.
If you have any web apps such as R shiny web apps, make sure they are live. Any other web apps that you deploy through Python such as Streamlit apps or through Dash should also be deployed online.
When you do any sample dashboard on Tableau, make sure you upload them to a Tableau Public profile, because this can be seen by data analyst managers, who can assess your skills before the interview.
If you have any projects based on learning Power BI online, make sure to showcase them too!
I also make sure my digital portfolio website contains links to all the different data analysis projects I’ve done online as described above.
Here’s the truth, employers and managers really do look through your digital portfolio because they value how much work you’ve done in the past compared to what you can code on the spot.
It’s also a great way to win interviewers over before even attending a data analyst interview!
I cannot stress enough to place your experience before your education in the resume. Employers don’t really look too closely at education unless you’re from a really good university. Experience matters a lot in data analytics because of how technical the role is.
Make sure to demonstrate that you have a diverse data analytics skill set in your resume. This will go a long way to win employers before you even attend the data analyst interview.
2. Update Your Linkedin Portfolio
The second step to preparing for a data analyst interview is to make sure that your LinkedIn profile is up-to-date.
This means adding any relevant experience, projects, and skills that you have listed in your resume.
In particular, it’s crucial to highlight how you are using different programming languages (e.g. Python, R, SQL) to perform data analysis and how you are using different software packages (e.g. Tableau, Matlab, SAS).
This will demonstrate that you know the skills required in the data analysis process, which is important for a data analyst job.
This will show employers how you have developed your data analysis skills over time and how you can use them to solve real-world business problems.
LinkedIn is great for showing off your technical data analytics skills as they can be listed on the profile in a special segment where others can affirm you for.
Additionally, make sure that your LinkedIn profile is free of typos and grammatical errors, as this can project a sloppy, unprofessional image.
If you’re not confident in how to create the perfect LinkedIn profile, consider spending some time browsing other data analyst profiles to get inspiration and guidance.
Whether it’s reading through blogs or actually looking at specific profiles on LinkedIn, there are many resources out there to help you make the most of this important professional networking platform.
Data analysts that employers love typically have acquired multiple experiences over time. Employers will look at these and find out how your skills can best fit the data analyst job description they have put out.
Oh, and don’t forget to include any data analytics of AI certifications that you’ve done in the past on your profile. These can help prove your technical knowledge to employers – even if they seem insignificant to you!
Trust me, because I have had my boss tell me that they looked at the course I’ve done in the past too!
If you have a robust LinkedIn profile, make sure you’re ready to answer questions that interviewers pose that pertain to it too.
3. List Answers to Possible Interview Questions
Once you have updated your resume and LinkedIn profile, it’s time to think about how you might answer data analyst interview questions.
Data analyst interviews will always have a technical aspect to them to test your technical skills. as such, you will need to prepare for them beforehand.
One of the best ways to prepare is to list out possible technical interview questions and how you might answer them. You can also ask your friends, family members, or colleagues how they prepared for their own interviews so that you can get some good tips and advice.
Some common data analyst interview questions include:
- What are your favorite data analysis tools or software packages, and how do you use them?
- How do you handle data preparation and cleaning, and how does this differ across different types of data sets?
- What are your favorite techniques for visualizing data and how do you choose which technique to use for a given dataset?
- Describe a complex business problem you’ve solved using data analysis.
Be sure to practice answering these questions and others like them so that you are comfortable going into the interview feeling confident and prepared.
There are many different types of non-technical data analysis questions that a hiring manager might ask. Examples include questions about how you approach a problem, how you prioritize tasks and projects, how you communicate with your team or colleagues, and so on.
Think carefully about how you might answer these questions, and highlight any relevant work experience or projects that you have done to demonstrate your skills.
You can also ask a friend or colleague to do some mock interviews with you, so they can give you feedback on how well you are answering the different types of data analyst interview questions.
Finally, it’s important to be prepared for how the interviewer might assess your answers and how you communicate them. For example, it may be helpful to make sure that you articulate your answer clearly and concisely without any filler words or long-winded explanations.
By preparing ahead of time, you will be able to walk into your data analyst interview feeling confident and ready to showcase your skills.
4. Brush Up on Technical Skills in the Data Tech Stack
Data analysts always need to keep up-to-date with the latest skills in data analytics to stay relevant in the industry.
To prepare for a data analyst interview, you should familiarize yourself with the different technical skills that are typically required in this profession.
One important skill is understanding how to work with different tools and software packages in the data tech stack. This can include learning how to use specific programming languages like Python or R, as well as how to use visualization tools like Tableau or Matlab.
These may include how to use handle missing data and using common R packages such as dplyr, ggplot2, or other data science packages.
These are the common packages that you’ll be asked to use for any data cleaning-related questions given to you during the technical interview.
You’ll need to refresh your memory on how to code again if you’ve been out of it for a while.
If you use Python often, recalling how to use common libraries for data science and data analytics such as NumPy, pandas, TensorFlow and scikit-learn will really help you from stumbling during a technical interview.
Do remember to experiment with coding in different data science IDEs, as they each are used in different use cases, especially Python.
In addition to these technical skills, data analysts should also have a solid understanding of how to perform data mining on large datasets and how different statistical techniques can be used to derive insights from them.
The data analysis process is always different for each project, so it’s important to stay up-to-date with how different data tools and algorithms are changing over time.
By doing this, you can continue to develop your skills as a data analyst and be ready to impress potential employers during the data analyst interview process.
Other key areas that you will want to focus on include how to use machine learning algorithms, how to do data modeling, and how to communicate complex analysis results clearly and effectively.
5. Research the Company and How You Align With Its Goals
In any data analyst interview, employers will try to assess if the candidate is suitable for the company, and not just their capabilities as a data analyst.
Employers want to hire employees that can align well with the company’s goals and at the same time, excel in their role as data analysts with good demonstrated knowledge of statistical methods, data analytics software, and the ability to interpret data.
As such, it’s important to research the company and how your skills and knowledge align with its goals.
One way to do this is by looking at the job description or any materials that are available on their website. This can help you get a sense of how they typically conduct data analysis projects, as well as what key aspects they will be focusing on as they hire new data analysts.
For example, before I applied for my role as a healthcare data analyst, I learned more about how data is more sensitive in the healthcare setting, how it was difficult to onboard stakeholders to adopt data-driven methods, and the difficulty to promote the data literacy of healthcare professionals.
These were essential during my interview, as my interviewer was impressed that I knew of the challenges that a healthcare data analyst faced on the daily basis. This gave them the confidence to know that I knew the industry well and would be a great fit for the job. So, know how data is used in your industry well!
Another way to find out how you can align well with the company’s goals is by speaking directly to current or past employees of the company. This will give you a better sense of how the team works together and how their skills complement each other.
You can even connect to them on LinkedIn and have a short chat with them about their experience and what the job scope in the job was like!
Overall, being well-prepared for your data analyst interview means doing your research on the company and how you can help them achieve their goals by demonstrating your skills and knowledge as a data analyst.
By focusing on how you align well with the company’s mission and strategies, you can show that you are a good fit for the position and have what it takes to be successful in the role.
6. Study the Job Description to Learn How to Provide a Business Need
In addition to being well-versed in technical skills and how your knowledge aligns with the company’s goals, it’s also important to be able to talk about how you can help the business.
One of the most common questions that potential employers will ask during a data analyst interview is how you would solve a business problem or how you can help the company meet its goals.
To prepare for this, it’s important to take some time to read through the job description thoroughly and look at any existing projects that are listed.
This will give you a better sense of how your skills can be used in a practical way to improve the business or add value to their data.
For example, if you are applying for a data analyst position in the retail industry, you might want to read about how different retailers are using data analysis to improve their strategies and marketing efforts.
This could help you better understand how your skills are relevant to their needs and how you can provide a business need that will benefit them.
If there isn’t any data analysis project that’s listed in the job description, make sure you ask the hiring manager during the data analyst interview what kinds of data mining or data cleaning problems they face and then demonstrate how you would solve them.
One easy way to figure out this and impress your interviewer during your analyst interview is to pose a smart question during the questions section!
You can ask them a question like: “What type of data do you typically analyze and what problems do you face on a daily basis?” This is a smart question to ask because you will also find out if the data analyst role you are interviewing for is the right fit for you.
After the employer answers the question, you can then reply with a suggested data analysis process and state how you will specific data analytics tools to perform certain data mining or data cleaning steps.
Then describe how you would go about solving the problem and business need. This will demonstrate your thinking process to future employers, giving them further insight into how you work.
7. Research on Salary Expectations and Compensation Packages
In addition to being well-prepared with your technical skills and how they can benefit the company, it’s also important to be aware of how much you’re worth in the job market as a data analyst.
This means researching average compensation packages for similar roles and how you stack up against other data analysts in terms of salary and experience.
By doing this, you can have a realistic idea of how much you can expect to earn in your new role and how to negotiate the compensation package that is right for you.
To start your research on data analyst salaries and packages, take some time to look at job postings from data analyst roles in Glassdoor or PayScale. These help you gauge the expected salary for your experience and skillset.
However, do take them with a pinch of salt, depending on the number of entries the site has received for the role in your region.
Also, take the industry you work in into consideration. If you’re working in an MNC, chances are, you’ll be paid more, but if you are applying to work in a startup, expect to be paid less for the same data analyst role.
Data analysts are also paid differently depending on the country they are working in. Do make sure you are looking at the results from your country before making your judgment on the pay.
And that’s it! I hope you’ve learned the 7 steps on how to prepare for data analyst interview!
Data analytics is an industry full of opportunities as the amount of data is only increasing with incredible speed.
As a data analyst, it’s important to be well-prepared and confident in your skills so that you can succeed in your new role and make the most of this exciting career opportunity.
Whether you are looking for a position in a large corporation or a startup, these tips will help you prepare for any data analyst interview with ease!
Thanks for reading and all the best for your data analyst interviews!
Justin is the founder and author of Justjooz. He is a Nanyang Technological University (NTU) alumni and a former data analyst.
Now, Justin runs the Justjooz blog full-time, hoping to share his deep knowledge of business, tech, web3, and analytics with others.
To unwind, Justin enjoys gaming and reading.