Data science has exploded in popularity as companies seek new ways to optimize performance and make strategic decisions. Experts in this field use statistical analysis and sophisticated technology to generate insights from vast amounts of data, such as sales records and social media traffic. From there, they help executives and other departments make smarter business decisions based on what they’ve learned.
As interest in data science has grown, so has the number of job opportunities. According to the Bureau of Labor Statistics, the demand for data scientists will increase by a staggering 35% between 2021 and 2031.1
Learning the right tools and strategies will allow you to navigate the rapidly evolving data science landscape. In this blog, you’ll find expert tips for every stage of your career, from building basic skills to seeking more advanced positions. You’ll also learn about essential data science skills and technologies, including machine learning and data visualization.
Acquire a Strong Foundation
You can prepare for a data science career by thoroughly understanding common approaches and tools. Many roles require knowledge of these foundational concepts:2
- Mathematics and statistics: Use statistical analysis and modeling to interpret large data sets and predict future trends
- Programming languages: Data scientists use programming languages such as Python and R to build data pipelines, automate tasks, and create data visualizations
- Data manipulation and statistical analysis: Gather, clean, and interpret data using statistical methods and tools, such as Microsoft Excel
Develop Core Data Science Skills
Data scientists use many methodologies and tools to interpret complex datasets. These essential data science skills will help you get started:
- Machine learning: Use artificial intelligence-powered algorithms to detect trends in data, make predictions, and answer research questions2
- Data visualization: Transform data into infographics, maps, charts, and other graphics with platforms like Tableau and Microsoft BI3
- Data storytelling: Make data sets and insights more accessible to broad audiences by explaining your findings in compelling narratives2
- Data preprocessing: Improve the quality of raw data by cleaning it and organizing it in a structured database3
- Feature engineering: Pick relevant variables from datasets to answer research questions3
Complete Relevant Education and Training
Building a career in data science requires lifelong learning. Luckily, there are many ways to obtain a comprehensive data science education. Many colleges and universities offer degrees and certificates in the field, such as the Online Data Science, M.S. at New York Institute of Technology. These programs allow you to study the latest approaches, theories, and tools in data science. At New York Tech, you can also gain practical experience by working on funded research with faculty members from sponsors such as the U.S. Department of Defense, National Science Foundation, and National Institutes of Health.
Outside of school, you can participate in informal educational opportunities like data science competitions and hackathons. These events allow you to sharpen your skills as you race to complete challenges and solve problems with data science approaches.
Gain Practical Experience
Hands-on experience in data science can heighten your expertise and help you make a positive impression on potential employers. Consider completing an internship or apprenticeship to build your skills and develop a professional network.
You can also gain experience by working on real data projects and case studies. For example, you could volunteer to help a local soup kitchen assess the effectiveness of a donation campaign. As you complete projects, assemble them in a digital portfolio to showcase your data science skills to employers.
Pursue Networking and Professional Development Opportunities
Accelerate your data science career by networking with and learning from experts in the field. Online platforms like LinkedIn and Kaggle let you connect virtually with data science professionals and share resources. Joining a master’s program is also a great way to network, as your cohort is full of other bright, like-minded individuals and taught by expert faculty who have their own impressive connections.
Joining data science organizations is another effective way to engage in networking and professional development. Groups like the Association of Data Scientists and the American Statistical Association often host industry events in the form of conferences, webinars, and workshops.4,5 These gatherings can help you keep up with the latest developments in the field and gain new skills.
Apply for Data Science Jobs
Companies of all kinds are now looking for data-savvy professionals, catapulting the number of data science jobs into the thousands. But even with all of that demand, high-paying data science jobs could attract dozens or hundreds of qualified candidates. These strategies can help you differentiate yourself and land a job.
Tailor Your Resumes and Cover Letters
Many companies use applicant tracking systems (ATS) to filter out unqualified candidates. Ensure that your application passes ATS by incorporating relevant keywords from the job description into your cover letter and resume (e.g., data visualization, programming languages, machine learning). This tactic also allows you to demonstrate how your experience and skills align with the position for which you’re applying.6
Prepare for Technical Interviews and Coding Challenges
Since so much of data science roles are based on output, you may need to demonstrate your data science skills by completing a technical interview or coding challenge. Set yourself up for success by researching common interview questions and completing practice coding challenges.7
Showcase Your Skills
Highlight your technical proficiency during interviews by discussing specific projects you’ve completed and sharing your portfolio. You should also mention your internships, certificates, and other data science education when answering questions.
Ethical Considerations in Data Science
Data scientists often work with sensitive information, so it’s essential to understand the ethical implications of data science practices. Familiarize yourself with data privacy and security measures, and ensure that you prioritize fairness and accountability when making data-driven decisions.2
Career Advancement Opportunities
As you gain experience, you can explore new opportunities for career advancement. Many data scientists use their skills to pursue specialized careers as data engineers and analysts. You can also seek leadership and managerial roles when working in teams, which could lead to a promotion.
Become a Data Science Leader
Invest in your future by pursuing an Online Data Science, M.S. at New York Institute of Technology. Our cutting-edge courses are designed by top faculty to prepare you for the evolving demands of the job market. You’ll develop sophisticated technical skills that will make you stand out from other job applicants, as well as build a strong foundation in data science that will maximize your earning potential. You can further distinguish yourself by taking specialized electives in artificial intelligence, information security, and other areas.
Get in touch with an admissions outreach advisor today to learn more.
- Retrieved on September 5, 2023, from bls.gov/ooh/math/data-scientists.htm
- Retrieved on September 5, 2023, from ncbi.nlm.nih.gov/books/NBK532764/
- Retrieved on September 5, 2023, from ncbi.nlm.nih.gov/pmc/articles/PMC9754225/
- Retrieved on September 5, 2023, from adasci.org/
- Retrieved on September 5, 2023, from amstat.org/
- Retrieved on September 5, 2023, from forbes.com/sites/ashleystahl/2022/12/12/5-resume-hacks-to-pass-ats/
- Retrieved on September 5, 2023, from freecodecamp.org/learn/coding-interview-prep/