Switching to a STEM Career With a Master’s in Data Science

According to the World Economic Forum, there is a shortage in global science, technology, engineering, and math (STEM) talent. The fast rise of automation and artificial intelligence is creating a tremendous demand for people with data literacy and other technical skills to help organizations obtain a competitive business advantage.¹  

A data science degree can help you stand out when you make a STEM career transition. With a number of job openings and high salary potential, the data science industry is attractive to career changers. The U.S. Bureau of Labor Statistics expects the demand for data scientists to grow 36% by 2033, with a median annual salary of $112,590.²

This blog explores what you can expect to learn from a master’s degree program in data science and how to pivot into a tech career.

What a Master’s in Data Science Delivers

During the course of a data science degree, you’ll learn a mix of technical skills and soft skills that will help you solve problems across many different sectors. Data scientists extract actionable insights from raw data, which brings value to any business. In a data science program, you’ll learn what kinds of data are useful for the project you’re working on, how to analyze data sets using algorithms and models, and how to present your findings in a clear and concise manner.²

The core technical skills included in data science coursework are programming, statistics, probability, machine learning, and data engineering. You’ll also learn optimization methods for data science, as well as big data applications and workflows.

As part of your coursework, you’ll develop soft skills such as problem framing, storytelling, and ethical judgment. These skills facilitate collaboration and allow you to work with cross-functional teams that include colleagues in non-technical roles and other stakeholders. Honing your soft skills will also help you mitigate the drawbacks of data science models, such as privacy violations and biases.³

Technology and Emerging Fields

Data science is an interdisciplinary field that has value across industries and use cases. As major tech companies battle to see who will invent the first AI application that can outpace human intelligence, exceptional AI researchers are commanding salaries on par with professional athletes in some cases.⁴ Even among businesses with a more traditional focus, data scientists play an important role in analyzing user behavior and personalizing the user experience to improve customer satisfaction.⁵

For example, the manufacturing industry uses data analytics to optimize performance and production through the internet of things (IoT) and other data pipelines. Machines are equipped with sensors that automatically transmit data to the system to monitor production and resolve inefficiencies. Data scientists can analyze such data to minimize downtime and proactively perform maintenance, in addition to lowering energy costs.⁶

Climate tech is another emerging field that relies on data analytics to help mitigate harm from climate change by promoting decarbonization.⁷ With the rise of smart cities, there is also a demand for data scientists in the public innovation space. Even the sports industry has seen an increase in demand for professionals who can leverage their data analytics knowledge and skills to improve athlete performance and drive informed decision-making.⁸

Bridging From a Non-STEM Background

A STEM background, such as a degree in statistics or computer science, can be helpful when obtaining a master’s degree in data science, but it’s not strictly necessary. You can also enter and succeed in the field of data science with a non-STEM background. Depending on the admissions requirements of your program, you may need to take some prerequisites before you start. Your university may offer a bridge course that covers math and computer science fundamentals, or you may be able to go through a bootcamp or a free massive open online course (MOOC) covering essential concepts and skills.

One advantage of entering data science for non-tech professionals is the opportunity to leverage your domain expertise as a differentiator. Data scientists work in all fields, so having hands-on experience working in an industry will give you a competitive advantage, as you’re more likely to understand the issues you’ll be working to solve. You can also tap into your existing professional network when you’re looking for job opportunities.⁹

Program Selection and Admissions Tips

When you’re choosing a data science program, consider your career goals and current obligations. Think about how you prefer to learn and what type of program will best fit your lifestyle. If you’re pursuing your master’s immediately after your undergraduate degree, a full-time, on-campus program may be ideal. However, if you’re already working or have family obligations, you may ultimately prefer an online or hybrid program that will allow you to set your own schedule.¹⁰

Take the time to evaluate the curricula, including capstone projects that you can use in your portfolio when you start applying for data science roles. Ask if the program has industry ties that will help in your job search.¹⁰

Regardless of which type of program you choose, you can use your statement of purpose (SOP) to frame your career-switch narrative and highlight how your previous career will help you succeed in data science. Your SOP should support the rest of your application and explain how your background and experience add to your qualifications.¹¹

Financing and Financial Benefit Considerations

Data science is a high-growth, high-reward field, but paying for a master’s degree can be challenging. Look into scholarship options through your school and other organizations, and ask your employer about sponsoring your degree if you plan to continue working there. Income share agreements (ISAs) constitute another option for borrowing. With an ISA, you receive funding for your degree up front and agree to pay the ISA provider a percentage of your salary for a set period of time after you graduate.¹²

The upfront cost of a data science degree is balanced by the promising career opportunities that can follow. The median annual salary for people with a master’s in data science is $156,000, with a range of $127,000 to $195,000.¹³ Additionally, U.S. News and World Report ranks data science fourth in its list of the best technology jobs. The field of data science also presents significant opportunities for upward mobility through leadership positions and advancement opportunities.¹⁴

Prepare for a Rewarding Career and Diverse Opportunities

An Online Data Science, M.S. degree from the New York Institute of Technology will equip you with the skills and knowledge you need to deliver actionable business value based on data analytics. Our rigorous curriculum is taught by industry experts and delivered in a flexible online format. Throughout the program, you’ll have opportunities to engage in authentic, hands-on projects that will prepare you to succeed in any industry.

If you are ready to take the next step in your data science journey, review our admissions process. To learn more, contact us or connect with one of our admissions outreach advisors today.