How to Build a Data Science Portfolio & Resume
After you complete a degree or certificate program, it’s time to pursue a career in the field of data science. For practically all industries, data scientist positions are expanding with new roles being created every day. According to the Bureau of Labor Statistics, database administrators and computer systems analyst positions are expected to grow by 8% through 2030. The hiring process can be uniquely demanding, from selecting the right companies for application submission and preparing to answer difficult data science interview questions, to facing rejection due to inexperience or limited skills, there are fast-changing challenges applicants must face. While you may feel prepared because of the knowledge base that you’ve cultivated in your academic and professional training, there’s still an important step that requires pinpoint attention: preparing a data science resume and portfolio to appeal to a future employer.
What Is a Data Science Resume?
Resumes, in general, are excellent windows to introduce yourself to a preferred company. Typically limited to a single one-sided page, resumes can be viewed as a surface introduction to who you are as a candidate for a potential position. For those pursuing a career in data science, it’s important to present the information appropriately to appeal to an audience that values professional and academic experience, technical and soft skills, and the awards you’ve received that make your application competitive. Data science professionals who are your direct competitors for hire will be creating and submitting their resumes from different stages of their careers. The resume of a recent college graduate will contain a much different focus than a data science professional who has prior experience in the field. Both candidates can create compelling resume materials, but both need to concentrate on the strengths that they uniquely bring to the table. A successful data science resume will contain general information about the applicant and specific material that appeals to an open position. Creating a new resume for every position for which you apply is an overwhelming, tedious process. To avoid this, it’s important to revise your resume only slightly when a particular position calls for it. For example, if a listing includes that an applicant must have experience working with a more obscure data science programming language, you could win over a recruiter by editing your resume to illustrate that you have experience working in that language (if you do). A balanced, effective, and competitive data science resume should try to include these sections:
Contact Information
The first piece of information a future employer sees on your resume should be your name. In text larger than the rest of your resume’s content, your name should appear in a pronounced, clear way. Your contact information should be attached to your name, either directly beneath it on the page or closely associated, and include these components:
- Email address
- Phone number
- Link(s) to your online data science portfolio
This information is vital as it will be the point of contact should an interested employer choose to reach out to you. The more accessible you can make this information, the better chance you’ll have of hearing back on your application.
Education Experience
Because most employers will require at least a bachelor’s degree for entry level data science positions, highlighting your past educational experience is integral to a successful resume. In the educational experience section, it’s a good idea to include the most recent degree you’ve completed. If you’re still finishing your bachelor’s degree when applying, you should be sure to include your expected graduation date. When you list your education experience, it’s important to make clear which school you went to and when you completed your degree. This information lets an employer know how recently you completed your degree, and how that degree fits into the position of interest. Listing your GPA in this section can also be an important component of this section. Generally, if your GPA from your undergraduate or master’s degree is greater than or equal to a 3.0, you should feel comfortable advertising it. Otherwise, it would probably be best to leave your GPA unlisted. Finally, this is the appropriate section to include any relevant certificates you’ve completed. For example, if you’ve gotten a degree in mathematics but have some kind of certification in a data science-related topic, you should feel confident including that information. This nuanced approach will let an employer know you have strived to advance your career and professionalism in the field and have the academic training to prove it.
Skills
While other parts of the resume have clearer and more rigid conventions, the skill section can be more subjective and is your opportunity to introduce specifications of your professional profile that may not be evident in other areas. A lot of the required skills employers seek in new hires can be found in the job listing. Some technical and soft skills that you should outline on your data science resume include:
- Programming Languages Proficiencies (Python, R, Tableau, SQL)
- Flexibility and Adaptability to new challenges
- Self-motivation
- Leadership or management styles or approaches
- Data science strategies
These are some general starting points that should be based off information provided in the job posting. Paying close attention to the language used in the posting is key here. Employers will want candidates who have the skills that they call out specifically.
Work Experience
A section that showcases your work experience, regardless of your professional background, is an important step to help employers learn more about you. If you have prior experience in the field, this is the perfect opportunity to outline where you’ve worked and what tasks you performed in your role. In this capacity, you can bolster your proven skillset with evidence from your professional past. When listing your work experience, it’s considered best practice to include the time window for how long you worked for a company or organization. This section may seem light if you’re a recent (or pending) graduate entering your chosen field. If, as a student, you don’t have relevant, field-related work experience, listing your past jobs here is still important. This shows future employers your work ethic, reliability, and track record, as well as the propensity to pick up on a new direction, amassing different experience and adapting to new learning methods. Though the jobs you held may not be data science-specific, they reflect information that employers in the industry may find appealing, illustrating how enterprising a candidate may be.
Honors & Awards
Employers will be eager to know what honors or awards you have received in school or in the workplace. Use this opportunity to outline any occasion in which you have been recognized for your academic and professional performance. General examples of appropriate honors and awards to include in this section can be scholarships and Dean’s List recognition. More field-specific awards can be competitions, technology events, or hackathons that you’ve participated in or won.
Activities and Volunteering Experience
If you find that your professional past may be lacking, a section that includes volunteering or community service experiences can be invaluable and speak greatly to initiative, character, personal responsibility, and involvement. This section can relay to a prospective employer that while you haven’t yet developed in the industry directly, you have still served your community.
Different Resumes for Different Career Stages
Competitive data science resumes will look differently for people who are entering the field at different points in their professional lives. Candidates aiming to land a job in data science will essentially fall into three categories: candidates who have just graduated, candidates who are transferring into the field of data science, and candidates who have already gained experience in the industry. Data analyst and data visualization expert Hana of Trending-Analytics.com encourages, “I strongly recommend creating a data portfolio, even if you are a beginner, as it showcases your skills and competencies in a more effective way than just listing them out in your resume. With that said, focus on quality over quantity when building your portfolio.”
Tips on Data Science Resumes for Applicants Who Just Graduated
Applicants who are fresh out of school will likely have to highlight certain aspects of their academic careers because of a general lack of experience in the field. For the most part, applicants can showcase volunteer experience, work experience that may not appear as relevant, and above all, educational experience. In these instances, it’s important to lean on the skills you have gained and your recently completed, organized data science portfolio. The key here is effectively communicating to a prospective employer that you are capable of carrying the fundamentals of data science into the role and help guide their organization forward..
Data Science Resumes for Career Transfers
Finding the right career can be difficult for everyone. Fortunately, there is always the opportunity to change directions and explore a new field. Data science is an attractive option for many candidates in other technical fields because of the way the industry is expected to grow as well as how widely applicable the related skills. Applicants who aim to transfer into the data science space are seen as more naturally prepared than transfers in other nuanced industries. Data skills translate and scale widely, you need only to create application materials that reflect a propensity to learning new technical, programming, and soft skills. For those aiming to pursue a data science career in a move from a different field, composing a competitive portfolio may prove especially challenging. In this capacity, it’s greatly beneficial for a candidate to pursue a certificate program or bootcamp in data science to build out this component more cohesively.
Resume Tips for Applicants with Prior Experience
While this may appear to be the most advantageous position from which to create and submit a data science resume, it still poses unique challenges and requires a unique focus. You can give greater context to the skills and educational experience that you outline in the resume by including prior work information that details the specific tasks you’ve completed.
How to Build a Data Science Portfolio
If you’re following this guide in sequence, then you’ve already included a link to your data science portfolio in the contact information of your resume. This measure will ensure that the projects you’ve completed – either in the classroom or at work – can be viewed by prospective employers. One aspect that practically all job candidates should be sure to include in a data science portfolio is a link to your Github profile. While using Github alone can sometimes serve as a suitable portfolio option, having a standalone data science portfolio can communicate additional skills to your prospective employer. Through either approach (or both), a well-organized, cohesive, and digestible Github profile will serve as a vital extension to any candidate’s application materials. Creating and uploading projects to a Github profile should be part of the coursework you completed as a student in whatever data science program you attended. For those aiming to transition into a career in data science from another field, creating a Github profile and proving your skill in pushing and pulling projects is a must. Having an active or previously used Github will send the message to future employers that you’re capable of employing programming languages that will be used on the job. These kinds of languages should include at least one of the following:
By clearly showcasing projects on your Github to which you’ve contributed, you’ll be able to make the case to your prospective employer that you have the technical expertise and collaboration skills to ensure you can successfully contribute to team projects. But you shouldn’t rely solely on the content in your Github profile. Instead, consider building out other materials for a comprehensive data science portfolio website that underscores an additional attention to detail.
Tips for Building a Data Science Portfolio
Applicants have free reign over what they want to include in a competitive, accessible portfolio, but there are some aspects to include that will set your portfolio apart. Jason Goodman, a data scientist for Airbnb, suggested that successful data science portfolio projects tend to include the following components:
- A Demonstration of Working with Real Data: By using real, raw data in the projects contained in your portfolio, you will demonstrate to your prospective employer that you can clean, organize, and build out visualizations with data.
- Scraped Data – By Your Own Means: Goodman contends that scraping data from most pages on the web is not as daunting as it may seem. By showing that you can collect data from sports statistics or housing price ranges, you will communicate that you can use clever means to gather data.
- Work from Public APIs: By pulling data from a public application programming interface (API), you will illustrate how you can build data sets from publicly available information.
- Projects that Incorporate New Data, New Conclusions: Projects that invoke boring or well-known data points won’t help your data science portfolio. Conversely, as Goodman exemplifies, using unconventional data like rap lyrics will communicate to your future employer that you have an investigative eye and a novel approach to data science.
- Your Personal Interests in Data Science Projects: Goodman stresses that the best data science project to have in your portfolio will tell a story about your personal or professional interests. It will also be infinitely more valuable than projects you think employers will find impressive. These projects will often fall flat and will relay tired, boring conclusions. Your future employer will want to know you, so this is the perfect opportunity to curate projects around what you find important.
- Data Visualization: A key portion to building a data science portfolio will be how you decide to include data visualizations. By putting effort into the aesthetics of your data science projects through graphic design and layout, you will show your future employer your capability for careful and cosmetic attention to detail.
- Concise, Clear Conclusions: Successful projects in data science portfolios must include brief, direct conclusions. As Goodman states, “people have short attention spans,” and this is especially true of those conducting the hiring process, sifting through hundreds if not thousands of viable candidates. Get to the point quickly, and your projects will leave a longer lasting impression.
- Interactive Projects: A data science portfolio that includes interactive elements will engage the user more effectively. For example, data visualizations that prompt users to click to learn more, or projects that include a quiz will prove memorable for employers reviewing your application materials.
One of the best ways to build a portfolio as a student is to dive into the relevant coursework that will give you an opportunity to create unique, independent data science projects. By getting a Master’s in Data Science, you’ll gain the skills necessary to expand your portfolio with a proven basis of knowledge and pursue a dynamic and vibrant new focus. Explore graduate-level data science programs today and begin your journey into finding a job as a data scientist.
