The data science industry is less interested in your classes and curriculum. They want to see how you have applied your learning on tangible, creative, and personally owned projects. Those projects can be motivated by personal ideas, inspired by classroom experiences, or driven by business consulting from data science society, our consulting class, or your part-time work.
Create a resume that shows your potential employer that you complete data science work that makes an impact. Your resume is not a historical record of every class, service project, award, and employment experience from the age of 16. Craft your resume to help them understand why you can fit with their data science team.
Data Science Resume Advice
We recommend that you get feedback from your faculty advisor and data science professors on how your resume performs. Take their feedback and make the changes. In general, we include some standard counsel that we often give.
The Feel of your Resume
Your resume should be enticing to read. Employers should catch the flow and structure at a glance. Using fonts, bullets, and whitespace appropriately will facilitate engagement with your resume. beamjobs.com provides examples of resumes with a good feel. Think about how you want to present your information. It matters.
ELITE Data Science provides excellent examples and advice on building your resume. Here are a few key concepts from their guidance.
Don’t Bury the Lead.
Resume reviewers will be scanning, and they might be tired. Your resume could be their 50th of the day. Do them (and you) a huge favor… Make their job easier!
Your resume should be consistent and have a flow.
- By Section: Structure your resume to put the most impressive sections first. For example, if you’re still in school and have cool course projects (but less work experience), put the coursework section before the work experience section.
- By Experience: Within each section, you’ll usually list experiences chronologically, but there will be some tiebreakers. For example, you should start with your most impressive course projects in the coursework section.
- By Bullet Point: Under each experience, the first bullet point should be the most impactful. It should entice the reviewer to stop scanning and start reading.
Steps 7-14 of the ELITE Data Science resume tips provide sound advice on crafting your resume. Please review their material.
Many of the example resumes you see will try to split the resume into ‘Personal’, ‘Professional’, ’ School Projects’, and/or ‘Service’. As our BYU-I curriculum is entangled with company-driven work and personal projects, we recommend that you have a single section on your resume where you can list your data science projects (paid, unpaid, personal, and client requested). This section could be titled ’ Related Experiences’ or ’ Data Science Experiences’.
In that section, you would include any projects representing your creativity, programming, analytics, and problem-solving. You would not have jobs unrelated to data science (E.g., waitress your Freshman year) or traditional class assignments that are completed by all students (you can and should include unique individual projects using skills from those classes).
Treat each project/experience as a unique experience. Help your potential employer understand your role in the project and the timeframe when you started and completed the project. Highlight:
- The purpose of the work.
- The data science skills and tools you used for the project.
- The benefits of the work.
Defining and labeling your role
You should label your role and then the company
Brigham Young University - Idaho
Brigham Young University - Idaho
Many of you will have experiences with our Data Science Society and Consulting Class. We get this work through a collaboration with the Research and Business Development Center (RBDC). We recommend that you list those experiences like the following.
Data Science Consultant for [COMPANY OF PROJECT]
RBDC Student Data Science Group
Many example resumes have skills sections where the job candidate creates a list of programming languages, software, and analytics skills. These lists can be problematic if you just make a list of 10-20 items. The employer most likely knows that you are not an expert at all of them and can have a hard time differentiating between those you prefer and excel in versus those that you saw in one course.
Make sure that your Related Experiences section directly mentions how you used those skills, tools, and languages to complete the project.
Make sure your LinkedIn profile matches the details on your resume. Edit your LinkedIn public prifile URL for clarity on your resume.
Podcast on Data Science
Infographic on Undergraduate Resumes