7 Things Students Are Missing in a Data Science Resume

Adding these 7 key elements to your resume will improve your odds of getting an interview call. Remember, after graduating from the university, your full-time job is to find a job, so put some effort into preparing your resume.

7 Things Students Are Missing in a Data Science Resume
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As I reflect on my days as a student, I now realize that there were a few crucial elements that were missing from my data science resume. These shortcomings probably resulted in my being rejected for various job positions. Not only was I unable to present myself as a valuable asset to potential teams, but I also struggled to showcase my ability to solve data science problems. However, with time,  I got better and collaborated with multiple teams to figure out what I was missing and how I could do better if I had to start over.

In this blog, I will share the 7 things that students often overlook in their data science resumes, which can prevent hiring managers from calling them for interviews. 


1. Simple and Readable Resume


Complicating your resume with technical terms, too much information, or unconventional formats can lead to it being rejected right away. Your resume should be easy to read and understand, even by someone not deeply versed in data science. Use a clean, professional layout with clear headings, bullet points, and a standard font. Avoid dense blocks of text. Remember, the goal is to communicate your skills and experiences as quickly and effectively as possible to the hiring manager.


2. Quantifiable Achievements


When you are listing your previous work experiences or projects in the experience section, it is recommended to focus on quantifiable achievements rather than simply listing your responsibilities. 

For example, instead of stating "Developed machine learning models", you could write "Developed a machine learning model that increased sales by 15%." This will demonstrate the tangible impact of your work and showcase your ability to drive results.


3. Data Science Specific Skills


When creating a list of your technical skills, it's crucial to highlight the ones that are directly relevant to data science. Avoid including skills that are not related to data science, such as graphic designing or video editing. Keep your list of skills concise, and write the number of years of experience you have in each. 

Make sure to mention programming languages like Python or R, data visualization tools like Tableau or Power BI, and data analysis tools like SQL or pandas. Additionally, it's worth mentioning your experience with popular machine learning libraries such as PyTorch or scikit-learn.


4. Soft Skills and Teamwork


Data science is not solely dependent on technical abilities. Collaboration and communication skills are crucial. Including experiences where you worked as part of a team, especially in multidisciplinary settings or instances where you communicated complex data insights to non-technical stakeholders, can demonstrate your soft skills.


5. Real World Experience


Employers value practical, hands-on experience in the field of data science. If you have completed internships, projects, or research in data science, be sure to highlight these experiences in your resume. Include details about the projects you worked on, the tools and technologies you used, and the results you achieved.

Students often underestimate the power of showcasing relevant projects. Whether it’s a class assignment, a capstone project, or something you built for fun, include projects that demonstrate your skills in data analysis, programming, machine learning, and problem-solving. Be sure to describe the project goal, your role, the tools and techniques used, and the outcome. Links to GitHub repositories or project websites can also add credibility.


6. Adaptability and Problem Solving Skills


The field of data science is continually evolving, and employers are seeking candidates who can adapt to new challenges and technologies. 

As a data scientist, you may find yourself jumping from being a data analyst to a machine learning engineer in just a few months. Your company may even ask you to deploy machine learning models in production and learn how to manage them. 

The role of a data scientist is fluid, and you have to be mentally prepared for the role changes. You can demonstrate your adaptability and problem-solving skills by highlighting any experiences in which you had to learn a new tool or technique quickly, or where you successfully tackled a complex problem.


7. Links to a Professional Portfolio


Creating an online portfolio and sharing it on your resume is extremely important. This will enable the hiring managers to quickly have a look at your previous projects and the tools you have used to solve certain data problems. You can check out the top platform for creating a data science portfolio for free: 7 Free Platforms for Building a Strong Data Science Portfolio

Failing to include a link to your GitHub repository or a personal website where you showcase your projects is a missed opportunity. 


Final Thoughts


One important thing to keep in mind while submitting your resume for job applications is to modify it according to the job requirements. Look for the skills required for the job and try to include them in your resume to increase your chances of getting an interview call. Apart from your resume, networking, and LinkedIn can be very helpful in finding jobs and freelance projects. Consistently maintaining your LinkedIn profile and posting regularly can go a long way in establishing your professional presence.

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.