Our everyday choices, from our Google queries to where we travel, are pieces of data that collectively paint a bigger picture of trends in behavior, activity and demographics. These insights can help governments, businesses, hospitals and other organizations make more informed decisions to improve efficiency, better serve clients or even save lives. 

While there is no single pathway to kicking off a career in data science, the process involves acquiring relevant knowledge and a robust skill set that will allow you to help solve some of the world’s most pressing challenges. 

There are a number of reasons why people choose to pursue job opportunities in data science, starting from a simple interest in the discipline to career outlook and earning potential. The field of data science offers versatility. Data science can be applied in multiple industries including retail, transportation, entertainment and even sports, as it allows organizations to maximize efficiency, grow customer relationships and reach business goals. 

Data scientists do more than analyze data and build statistical models. They are also creative strategists whose skills in communicating are just as valuable as critical thinking. Businesses depend on data scientists to communicate the insights they gather and help develop a plan of action for implementing decisions. They work closely with different stakeholders within an organization, often employing a multifaceted approach to their work that values both quantitative and qualitative abilities. 

No two data science jobs are the same. Each one calls for specific competencies and comes with its own set of responsibilities. So, how hard is data science? For some, studying data science may be challenging at first. Data science is a multidisciplinary field that combines technical know-how (like coding and machine learning) with soft skills (such as communication and analytical thinking). Those joining the workforce may find that there is a fairly high barrier to entry, too. 

Across industries, the profession has unique complexities and comprises a great deal of problem-solving. And on top of that, the field is constantly evolving. But there are multiple opportunities to learn along the way, and with ample preparation, a career in data science is achievable. Data scientists help create practical solutions that advance an organization’s goals—and they learn how to do that by obtaining a robust knowledge base and skill set. In addition, to a professional degree, data scientists can choose to pursue a variety of data science certifications and other professional development resources to grow their knowledge, while bolstering their credentials.

Being intellectually curious, paying close attention to detail, acting as a proactive problem solver and being open to learning new skills on the job are a few personal traits that may help these professionals to be effective in the workplace, master core competencies and keep up with new developments in the field over the course of their careers. 

While there is no single, formal pathway to becoming a data scientist, a master’s in data science may be beneficial in advancing career trajectory. Exploring topics like business analytics and cloud management, a master’s program may allow aspiring professionals to enhance and tailor their existing skills and interests, while shaping focus within the field. Because data science has practical applications, a master’s degree program provides ample opportunities to explore that with built-in support and guidance from professors and peers. 

The data science career path doesn’t look the same for everyone. A number of professionals enter the workforce with education and training in computer science, statistics or engineering, and may end up in either one of these fields in an entry-level role. Those who study data science or a related discipline may also work in healthcare, where the statistical models they build support clinical decision-making and inform the development of new medical devices. Alternatively, a data scientist’s expertise may land them a job in government, where they work to increase the public’s access to the thousands of data sets generated by the federal government. There are even data scientist jobs in the gaming industry. Data scientists can transition from industry to industry, and over the course of their career, they may assume job titles like data engineer and data analyst.  

Ultimately, a data scientist’s career path will depend on personal goals, interests and educational background. 

Data scientist career outlook is relatively strong according to a 2017 study on data science and iSchools. That’s partly because of the increasing demand for the discipline across various sectors. Data science career prospects may be found in a variety of industries. From startups to government, data science jobs span a number of industries. Finance, insurance, scientific services and retail trade are a few common examples. Depending on the role, some allow for versatility in responsibilities, while others may be more structured. Similarly, some companies offer remote data science jobs. Entry-level data science jobs give graduates an opportunity to build their knowledge base and data science portfolio. 

The Bureau of Labor Statistics (BLS) projects that data science and mathematical science jobs will grow 31% from 2019 to 2029, faster than the average for all jobs.

The Quant Crunch [PDF, 4.3 MB], a 2017 study conducted by Burning Glass Technologies and IBM, notes that data scientists may earn annual salaries well over $100,000. Data scientist salary, however, varies based on factors such as location, employer, job title and industry. A candidate’s qualifications and collective years of experience may open the door for them to pursue management positions, which typically command higher pay than those at the entry level. And depending on the business or organization, there may be a sign-on bonus. 

Starting a career in data science or making a data science career change may seem challenging, particularly with no prior experience, but there are a number of ways aspiring data scientists can grow their knowledge and gain hands-on work experience. The recommendations listed below may be used for reference when considering how to start a data science career or how to become a data scientist with no experience, and they don’t need to be followed in sequence.

There is no minimum education requirement for becoming a data scientist. Some professionals enter the field with a background in data analytics or computer science, and others an undergraduate degree in social sciences, which may be useful when communicating data insights in the workplace. 

However, a combination of technical and non-technical skills may help along the way. Preparing data for effective analysis, writing code and leveraging machine learning are among the technical skills Tableau recommends for data scientists. Critical thinking is equally as important as having a technical skill set rooted in the desire to innovate, problem-solve, collaborate and be proactive. 

It’s worth noting that qualifications may vary from role to role. Take health care data scientists for example. According to a 2019 study published in the Journal of the American Medical Informatics Association, their qualifications are focused on performance improvement, product development, statistics, R, machine learning, storytelling and Python. 

Is a master’s in data science worth it? A master’s degree can help candidates gain a competitive edge in the job market, especially considering the opportunities to hone technical skills and establish a network of industry professionals over the course of the program. While bootcamps and other alternatives can be useful to boost your knowledge, a master’s program may provide relevant tools and resources to help map out and build one’s career roadmap.

Syracuse University offers an online MS in applied data science allowing those with an interest in the field to explore data science in a vibrant digital community while working or attending to other personal obligations. The master’s in data science curriculum is designed to teach students from the ground up, providing a foundational approach to analytics applications that can help graduates start a career in data science.