What To Include on a Data Analyst Resume


Data Analysts are a hot commodity, ranking highly in the World Economic Forum’s list of the most in-demand jobs. And as organizations continue to find new ways to utilize data and offer Data Analysts competitive salaries, it’s no wonder people are flocking to the field.

If you want to land a Data Analyst job, you’ll need a strong resume to set yourself apart from the competition and stand out as the perfect candidate. Ahead, we’ll give you a breakdown of what to include in your Data Analyst resume and how to prepare for your upcoming interviews.

Why do you need a Data Analyst resume?

Your resume provides a snapshot of your skills and experience, and it shows recruiters and hiring managers that you have all the required tools to do the job. A good resume will include both your technical skills and the soft skills you’ll need to collaborate effectively and work as part of a team (more on that later).

The breakdown below is a great starting point for your Data Analyst resume, but you might want to consider customizing your resume for each job you apply for. Companies often use software that scans your resume for keywords before sending it to a hiring manager, so if you want to maximize your chances of landing an interview, you’ll want to ensure it’s aligned with the job description.

What to include on a Data Analyst resume

Here are a few key sections to include on your resume.

Contact information

Your contact information should be at the top of your resume. Along with your name and email address (make sure it’s a professional one), you may want to include links to your LinkedIn profile, GitHub, and portfolio.


Summaries are optional, but if you choose to include one, write a brief outline of your experience and what you’re looking for in your new role.

For example, if you’re seeking your first Data Analyst position, your summary might say, “Motivated, responsible Data Analyst experienced in the retail sector seeking Junior Data Analyst position at Big Box Store Corporation.”

If you’re more experienced, you could include specific achievements. For example, “Meticulous Data Analyst with four years of experience seeking Data Analyst position with Acme Corporation. Experienced in analyzing data to improve processes and reduce operating costs by 10%.”

Data analytics skills

After your summary, add a section that summarizes your technical skills. Some skills to highlight include:

  • Statistics and probability
  • Relational databases
  • Pattern and trend identification
  • Data mining
  • Database design and management
  • SharePoint
  • Advanced Excel functions
  • Programming languages (SQL, C++, Java, etc.)
  • Predictive modeling techniques, including decision trees and generalized linear models (GLM)
  • Big data analytics

Job experience

After your skills section, add your relevant work experience. Most people start with their most recent position and work backward.

Beneath your title and the companies you worked for, list your responsibilities and highlight what you’ve accomplished. Bonus points if you include any metrics. Did you grow sales at your previous company? Did you improve processes? Not only will these achievements help illustrate your impact, they’ll also give you material to discuss during your interview.

To that end, quantifying your accomplishments with specific figures is another great way to stand out. Saying you grew sales by 10% will make more of an impact than just saying you increased sales.

Your experience section is also a great opportunity to showcase your soft skills. Instead of listing them in the skills section, consider weaving them into your responsibilities. How have you demonstrated leadership? How have communication and collaboration played a role in your day-to-day?

This is especially important if you’re applying for your first job as a Data Analyst. Highlighting your transferable skills helps illustrate how your past experiences have prepared you for the role. Every company wants dedicated team members who show initiative, have excellent communication skills, work well with others, and pay attention to detail.

Courses and education

In this section, you’ll want to highlight your education. List any degrees you have, even if they’re not directly related to data analysis, along with any certifications you have and online courses you’ve completed.

And don’t be intimidated if you lack a traditional degree. More and more companies are dropping degree requirements to widen their talent pools. As long as you have the skills to do the job, you can be a strong candidate. And of course, you should list any online courses and/or certifications you’ve gotten. If you get a certification from Codecademy, you can even post it directly to your LinkedIn account.

What if this is your first Data Analyst job?

If this is your first Data Analyst position, you may not have years of technical experience, and that’s okay. You can bolster your resume by highlighting projects you’ve completed, whether you made them independently or as part of a course. If you need help finding potential inclusions, check out our data visualization projects.

How to get ready for your Data Analyst interview

Once you get your resume polished and start submitting, the next step will be your interviews, which may consist of technical and behavioral interviews. In a behavioral interview, you’ll likely be asked general data questions, visualization, communication, and experimental design questions. And in a technical interview, you may be given coding challenges and asked to demonstrate your skill with data analytics.

To get ready for your interview, your first step is to brush up on your skills. For example, you might take a SQL course to refresh your knowledge. We also offer a Data Analyst Interview Preparation Skill Path that’ll help you prepare with common interview questions, develop a portfolio project, and learn how to navigate job postings. For more helpful resources, check out our Interview Prep page.

Getting started in data analytics

Data Analysts play a critical role in helping organizations make informed decisions. If you need help breaking into the field, you can start by learning programming languages like Python or SQL. And if you already have some programming skills, you can start learning how to apply them to data analytics with  Skill Paths like Analyze data with SQL and Analyze financial data with Python.

We also offer a Data Scientist: Analytics Specialist Career Path that teaches you all the skills you need to analyze data. You’ll learn how to organize data, dig into that data to find insights, and communicate your findings. You’ll also build projects you can use to build a portfolio.Ready to get started? Sign up now!

Data Analytics Courses & Tutorials | Codecademy
Data analytics is the process of taking raw data and turning it into something meaningful we can understand. By finding trends and patterns, you can make predictions and uncover new information that helps inform decisions. There’s a great demand for Data Analysts in healthcare, marketing, retail, i…

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