Data science has gained significant visibility over the past few years. Many students and professionals ask: Is data science a good career path, or is it just the latest career trend? And while there are many responses to this question, the following list pinpoints five reasons why data science is a good career path. The list focuses on longevity and growth opportunities in the industry and the many ways that data science has not just developed as an industry but as a field with specific skills that are applicable across industries.
5 Reasons Why Data Science is a Great Career Path
1. Data Science is Growing
One of the most cited reasons why data science is a good career path is the projected growth of data science and data analytics over the coming decades. Data science is a fast-growing field that is expected to continue growing apace with adjacent data science-focused STEM fields and industries. The Bureau of Labor Statistics projects at least a 15% growth in employment opportunities for computer and information research scientists, including data science, from 2019 to 2029.
Data science is also growing in the types of available data science jobs, even outside the fields of data science and data analytics. Positions that require data science skills include data engineer, data architect, and data researcher. New fields and specializations have also developed from data science and will continue to do so over time.
2. Data Science is Skills Based
The data science career path is highly skills-based. Therefore, you should stay current on your industry's most desirable data science skills. Data scientists can benefit from developing more training and skills, including engineering, machine learning, data visualization, and storytelling.
Learning new data science tools, skills, and software can jumpstart your career growth in the data science industry. This skills-based foundation also means that as you grow and develop in your data science career, there are opportunities to educate and collaborate with others, creating new career possibilities and networking opportunities by developing projects and data science portfolios.
3. Data Science is Transferable
The data science skills, software, and tools learned while pursuing a career in data science are also transferable to other fields and industries within and outside of STEM. For example, developing programming skills is an essential part of building a career in data science. And while programming with Python or SQL is standard for data scientists, this skill set is also applicable in industries like information technology or web development and design.
Whether you are transferring your skills from one data science job to another or one industry to another, knowledge of data science and its tools gives you more options to further your career.
4. Data Science is Dynamic
The dynamic nature of data science makes it an appealing career path for newcomers and career-change seekers. Today’s data-driven world and the rise of big data and social media platforms have fundamentally altered and grown what was once considered a narrow career field. So, even as data science has the veneer of a new field, researchers and professionals have always engaged in the work of analyzing data and sharing their findings.
Many fields and career paths require the skills and processes of data science, which are fundamental to research, design, and development. Aspiring data scientists can look forward to continuing changes in the field as new methods are developed to collect, analyze, and organize information and data. The dynamic nature of this field ensures that the way that we use data science now is probably not the way that we will use it in the future. So, keeping your skills current will be essential to your career success.
5. Data Science is the Future
It’s impossible to predict the future of every data science job. But the ubiquity and longevity of information and data don’t seem to have an expiration date. Even as software programs automate data collection and analysis processes, data science professionals are essential for understanding the algorithms used and accurately analyzing the output. Software, platforms, and technology collect massive amounts of data from users and customer engagement. Companies that profit from such data will continue to need professionals to clean and organize it and oversee the development of algorithms, recommendation systems, and other machine learning tools.
The not-so-distant future will be defined by the burgeoning use of the Internet of Things, automation, and machine learning. Data science professionals and industry experts will be needed to continue developing related products and services and aggregate and analyze the data of these products and services. DaTheta scientists will also need skills in cybersecurity, ethical AI, and more responsible machine learning to ensure that they protect user information and data. As data scientists develop skills in automation and software engineering, and database management and development, the data science career path will continue to be worthwhile.
Beginning Your Career in Data Science?
Noble Desktop's data science classes and certificate programs are phase one of beginning a career in data science. Certificate programs, such as the Data Science Certificate and the Data Analytics Certificate, offer aspiring data scientists and analysts hands-on training and professional development. More specialized courses, like the Python for Data Science Bootcamp, are also useful for students and professionals who want to learn data science fundamentals, like Python, while also staying updated on industry trends, like automation and machine learning.