Elderly Business Intelligence Developer Task Summary – The number of data analysts is expected to grow by 25 percent between 2020 and 2030, along with increasing pay transparency laws, making this the ideal time to land a data analyst job.
Fun fact: Before we started, one of our founders worked as a data analyst for six years. With his guidance, we’ve reviewed many data analyst resumes to find out what helps data analysts get more interviews.
Elderly Business Intelligence Developer Task Summary
Crafting your data analyst resume and cover letter is the most difficult part of this process. To inspire you, we chose 22 resume samples of top data analysts for different career stages.
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Our data analyst resume examples are proven to help you put your best foot forward to land the job you’ve always wanted in 2023.
According to U.S. Bureau of Labor Statistics projections, employment of computer and information research scientists (including data analysts) will grow 16 percent between 2018 and 2028. This is much faster than the average! other works!
Due to high demand and high salaries, it makes sense that people are flocking to apply for data analyst positions.
But that doesn’t mean you should get discouraged. Applying for jobs online can feel like applying into a black hole, and we know it sucks. One of our founders took 77 job applications before landing his first data analyst job at Geico, and the next job was much easier to get.
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One problem when applying for data analytics roles is that these titles are not standardized across different companies. At one company, data analysts might spend their time creating reports in Tableau, while at another they might write machine learning models for production.
Because of this variability, it can be difficult to ensure you include the correct information on your data analytics resume. With this guide, you’ll put your best foot forward, no matter what data analyst position you’re looking for.
First, you must demonstrate that you have the right skills for the job. This means you need to accomplish two goals with the skills section of your data analyst resume. First, you need to be able to get past the automatic applicant tracking system (ATS) keyword filters, which companies use to filter candidates. Next, you want to demonstrate your technical proficiency to the person reviewing your resume.
If you’re not sure which skills to include, we analyze the data to find out which skills are most in demand by companies hiring data analysts.
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Unfortunately, these two goals are mostly opposite to each other. If your goal was simply to pass the ATS, I would list all the skills to get your foot in the door:
The problem? This method is a big red flag for technical hiring managers. You should only include skills that you would feel comfortable discussing in your data analyst interview. Additionally, the skills section should take up no more than 20 percent of the page.
Piton. Instead, simply list the technical skills you’ve coded before. And avoid including skills like “data mining” or “data analysis,” as a technical hiring manager knows these are just general terms that don’t mean much. Instead of “data mining,” list actual techniques you’ve used, such as “decision trees” or “logistic regressions.”
But even when you narrow it down, sometimes your skill list seems cluttered and difficult to read. Never fear; There are several ways to organize your skills section! To start, you can list your skills based on your level of mastery of them (“Advanced” vs. “Familiar,” for example). Alternatively, you can list your skills by skill type. You can even mention the years of experience you have with each tool.
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For programming languages, mention the libraries and frameworks you use for data visualization and manipulation in that programming language.
Most of the time, it is not necessary to include a resume objective or summary; a mistake that many data analysts make. If a summary or resume objective doesn’t add value to your application, it’s okay to skip it.
Why leave this aside? It’s redundant. Suppose you already demonstrate on your resume that you used tools like Python and SQL to turn data into actionable information. In that case, your objective does not provide any new information to the person reviewing your resume.
The summaries are similar, but are for candidates with 10+ years of experience and may include more personal accomplishments. There isn’t much difference between a resume summary and an objective; all you need to know is when you should include them.
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When a hiring manager reviews more than 50 resumes for a given position, she quickly looks for reasons to say “no.” By using these tips for formatting a resume, it will be easier for the hiring manager to see your value and ask you for an interview, bringing you one step closer to a job.
Place for this to happen. One of our team members recounted his first days out of university as a data analyst. When they were applying for jobs, they accidentally wrote the wrong email address on their resume for seven different positions.
Even if they were perfectly qualified for the position, there was no way to contact them due to a small mistake. So believe us when we say you need to triple check this section for spelling, grammar, or linking errors.
As part of your contact information, you must include your name and the position you are applying for (even if it is not your current position).
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You do not need to include your full address in this section, but you must include your city and zip code. You’ll also need your phone number in case your employer prefers that method.
Finally, include a link to your LinkedIn profile and anything else that can convey why you are a great data analyst. If you have an active Github, please include a link to it. If you participate in many Kaggle contests, include a link to your profile. Do you have a personal blog where you talk about electoral data? Be sure to include a link.
If you’re a beginner looking for your first full-time position, it’s an absolute must to include projects on your data analyst resume. However, the more work experience you gain, the more projects should become less critical. When you have more than four years of experience in the field, you should only include one project that you are exceptionally proud of.
What projects should I list? Anything where you identified (or were given) a problem and used data to find an answer to that problem. It’s okay if it’s a class project, but it’s even better if you take the initiative yourself.
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If you don’t have any such projects, now is the time to work on one. Do you have a question that has never been answered? An experiment you wanted to try? Think of a way to collect and analyze data to satisfy your curiosity.
Here’s an example: One of our founders had a hunch that the major job boards (Indeed, Glassdoor, and LinkedIn) had essentially the same jobs for data science roles. So, he manually collected data, analyzed it, and wrote on it to determine the best job board for data scientists.
The projects you include do not need to be exhaustive or innovative. Employers just want to see that you can ask a question, use data to answer it, and present your findings reasonably and clearly.
His projects section is also an opportunity to provide more context on the programming languages and libraries he listed in his “skills” section.
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Like the “projects” section, the education section of your resume will be longer for entry-level data analysts compared to more experienced data analysts. You’ll want to include relevant courses you took in school related to data analysis for entry-level data analysts.
Courses relevant to data analysis are any math, statistics, programming, and economics classes you have taken. To be an effective data analyst, you must apply the principles you learned in these classes to real-world problems and data sets.
Regardless of your level of experience, you should always mention the school you attended, what you majored in (including minors or certifications), and when you graduated. This would also be the place to list any relevant boot camps or online courses you have taken in the field.
If you have academic experience, you can also list any publications you have co-authored. Be sure to include the title of the magazine and a link to allow the hiring manager to read more if they are interested.
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Only mention your GPA on your resume if it’s something you want to highlight; In general, only include your GPA if you are a beginner and earned anything above a 3.0.
You analyze data for a living, so you know that numbers count when it comes to information. So when you talk about your work experience, your goal should be to highlight your achievements using numbers and estimates.
“Performed a customer cohort analysis using SQL and Excel and recommended an email campaign for a customer segment”
When you talk about your work, especially if it was a team project, emphasize your specific contributions. For example, you may have made a product recommendation based on prior analysis. You’d like to talk about that particular recommendation on your resume instead of the built-in feature.
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When talking about quantitative impact, it is okay to talk about the project as a whole. Following the previous example, it would be impossible
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