As the volume of data generated by companies continues to rise, data science is projected to grow in response to the need for data-driven decision-making. Businesses will be looking for prospective employees who can sort through a wealth of information and interpret the data for the benefit of the company.
Employers are looking for people with practical, real-world experience that can go beyond their coursework to make a difference. You can begin preparing yourself now to make the best impression on employers using these guidelines:
Build a Strong Technical Foundation
The first step towards making your degree stand out to employers is to build a solid technical foundation that proves you know how to work effectively.
Master core programming skills like being able to write clean, readable code as well as use programming languages such as Python and/or R (depending on which field you go into), and programs like Git or GitHub, SQL, or Power BI. These are key in breaking down and understanding data, and employers will expect you to already be familiar with these programs.
Most industry roles require strong statistics and foundational math, with specialized roles often requiring advanced math. The most important discipline to have a strong grasp of before getting hired is statistics, which is key to interpreting data correctly and making predictions as accurately as possible. Other important math skills include linear algebra and calculus, to ensure you can understand important concepts.
Gain Real-World Experiences
Real-world experiences are one of the best ways to stand out to employers as a recent graduate. Many applicants share similar foundational coursework, so employers often look at how you have used what you have learned and how you apply it to real problems. These experiences prepare you for full-time work in data science and prove that you already have skills like communication and problem-solving.
Internships are the strongest way to get experience as a college student. They give students a taste of what working as a full-time data science professional will look like, and provide an opportunity to apply classroom knowledge in the workplace. You will most likely be working with a team, which gives you an opportunity to develop your communication skills.
An internship also helps you connect with professionals in the field who might be willing to either hire or put in a good word for you when the time comes to find a full-time job. The importance of connecting with professionals in the field cannot be overlooked.
College is also a great place to connect with others in the field. Professors often know of projects that you could contribute to or will give guidance on any personal projects you are working on. Academic societies are another great resource that offers opportunities to meet others and learn about conferences and other academic activities.
Develop Strong Communication and Data Storytelling Skills
Data science is more successful for businesses and organizations when you can explain what the data says and what it means for future plans. There are many different groupsof people you will need to contact, from teammates to higher-ups to stakeholders. Each one needs different information communicated differently; for example, your teammates will understand technical jargon and the programs you are working with, while a stakeholder will most likely not. Finding a way to communicate information to a wide range of people will be a major advantage when entering the job market.
Here are some ways you can practice this skill while still in school:
- Writing Project Reports: Writing project reports helps students become familiar with explaining projects from start to finish. You will learn how to explain complex ideas clearly, organize technical information and data, and translate the data into actionable insights for stakeholders.
- Giving Presentations: Presenting information to an audience is a valuable skill to work on before entering the workforce. You can learn how to use graphs and other visual aids to explain findings and support your reasoning, as well as learn how to field questions and speak clearly and confidently.
- Participating in Group Projects: Although many students dislike group projects, most projects in the workforce will require at least some collaboration. Learning how to divide tasks, share work, and solve problems as a team requires many skills essential to data science teams.
Communicating actionable insights is what separates good data scientists from great ones.
Create a Professional Portfolio
A professional portfolio is the best way to showcase your skills and accomplishments to employers in a short period of time. It can demonstrate concrete evidence of your ability to solve problems and to study data science both in and out of the classroom, including the skills discussed above.
Your portfolio should include examples from every facet of data science. These are just some examples:
- Coding and Programming: Demonstrate your abilities by showing clean code, how you handle problems like missing values or duplicates, and other projects that have used your coding and programming skills.
- Exploratory Data Analysis: Investigate data to discover the patterns, trends, and relationships hidden under the surface. EDA is all about showing your ability to interpret what the data means for the specific needs of the project and the organization you are working for.
- Data Visualization: Data visualization makes data easy to understand by creating visual aids like graphs, charts, and dashboards. It is an easy way to communicate results clearly and efficiently to both technical and non-technical audiences.
Although you want to share specifics about your projects and other accomplishments, it is important to remember that employers will most likely not take the time to fully read your portfolio or resume. More likely, they will skim to gather the general information, so keep explanations brief, punchy, and conducive to skimming. Another helpful strategy is to tailor your resume to each job by using specific keywords found in the job description and highlighting measurable achievements.
Be a Lifelong Learner
As new developments in data science emerge, it is important to be up to date on the latest industry tools and findings. Understanding the role of AI, for example, and what it could mean for the field is something that current and future employees should be aware of. Other opportunities, like completing certifications and participating in competitions, show employers that you are willing to put in the work to go the extra mile. As you continue to hone your craft and make connections with others, you will gain experience and knowledge that will make you invaluable in the field.
Citations
Data Scientists - U.S. Bureau of Labor Statistics
How Much Math Do You Need to Become a Data Scientist? - Flat Iron School
Data Science Internships: Benefits and Tips for Success - New York Institute of Technology
Communication Skills for Data Science - Pragmatic Institute
Portfolio Content - Yu Dong Blog
Tailoring Your Resume - The University of Arizona
AI Impact on Entry-Level Jobs: Why Junior Roles Are Vanishing - Digital Digest