Whether you are drafting or updating your resume, this article will walk you through what a data resume looks like and insights into how to tailor your resume for the data analytics and data science industries.
It is important to note that depending on the role you’re applying to, you will be crafting a resume that looks a little different than other industries.
A Look into the Data Resume
The data resume highlights your proficiency with data tools, statistical methods, programming languages, and domain language that are critical for gaining insights from complex data sets. In this field, your resume serves two key purposes:
- Demonstrate technical and analytical skills: Employers need proof that you can manipulate data, build models, and visualize findings effectively.
- Showcase problem-solving and impact: The key is to show how you have leverage your skills to solve real problems, improve processes, or influence decisions instead of just listing them off.
Key Sections of the Data Resume
Like other resumes, you will have an education section and a work (or related) experience section. You may also have other common sections like activities, campus involvement, skills, certifications, awards, or research.
Here’s where things differ: skills section and projects section.
Skills Section will have categories to highlight your different types of tech skills in addition to non-tech skills. These categories may be programming languages, machine learning and statistical tools, visualization tools, databases, etc. You can choose the appropriate categories based on your skills and experience. Order your skills from most proficient to least proficient, avoiding qualifiers like “familiar with” to keep the focus on your strengths.
Projects Section is where you bring your skills to life. Projects demonstrate your practical experience and initiative through hackathons, capstone projects, volunteer work, data challenges, and personal projects of interest.
Project Format: Project title (Personal, Work, Group, or Academic) and brief description of what you did, including technologies and data tools employed, and tangible outcomes or impact.
The examples to the right can be found in a completed resume example in the Resume Guide.
Organizing Your Data Resume
The order of your resume will also look a bit different from other industries. Below is the order of sections commonly used on a data resume (keep in mind, not all of these sections may apply to your experience or the story you want to share on your resume).
- Header (Name, Contact Information, ePortfolio or LinkedIn if you have one)
- Education
- Skills
- Projects
- Related Experience (aka, internship, work, or research experience)
- Campus Involvement and Leadership
Tailoring Your Data Resume
As you are evaluating your data resume, the following considerations can help you effectively tailor your resume for data analytics/data science industries.
- Be specific: Align your resume language and keywords with the job description. Use strong action verbs like “engineered”, “cleaned”, “visualized”, or “automated”. Include as many technologies and methodologies listed in the description to the extent of which accurately reflect your skills and experience.
- Pay attention to the company’s location: Resume norms vary globally. For example, in the U.S., including a photo on your resume is uncommon, whereas it might be expected somewhere else. Tools like Interstride can help you customize your resume based on the geographical area. If you are applying to a company in the U.S., this resume example provides a solid start.
- Be intentional about disclosing information: Anticipating questions about items such as gaps in your education, major or career changes, time away, or prefer to go by a name that is different from your legal name can give you a chance to craft a response based on what you are comfortable sharing. For instance, you may primarily disclose your preferred name on your resume, while using your required legal name for legal documents such as applications and background checks. When discussing these topics during hiring, keep explanations brief, confident, and focused on your strengths or growth. If you’re unsure how much to share or how to frame it, consider working with a career advisor to craft responses based on your comfortability and the hiring process.
Final Tips to Create an Effective Data Resume
To ensure your data resume stands out, these tips are important to keep in mind when reviewing your resume.
- Quantify results whenever possible to grab the reader’s attention.
- Keep descriptions concise but impactful, utilizing bullet points for readability.
- Showcase your project work through platforms such as personal websites or Github.
- Proofread carefully; get a trusted mentor, friend, or career advisor to look over your resume.
Example Data Resume
This example resume is a general starting point and serves as a guide. This is not a definitive, comprehensive list of sections or experiences.
You can find more example resumes in our Resume Guide. You might also puruse resumes of industry professionals on LinkedIn to generate ideas of what to include and how to format your resume.
