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Over the last decade, Big Data has become essential to the functioning of organizations. Data science degrees, in turn, train students how to help these organizations meet their informational needs. While undergraduate programs typically cover fundamental concepts in data collection and analysis, graduate programs offer students the chance to conduct original research or gain specialized training in technical areas, such as data mining, data engineering, or data management. 

It's a common misconception that data science is solely about applying statistics to large amounts of information. In fact, data science is a fast-evolving, multidisciplinary domain that includes aspects of computer engineering — such as machine learning and artificial intelligence — as well as advanced math, economics, and business concepts. Combining insights from these disciplines, this field looks at the science behind gathering, processing, analyzing, and communicating data to help organizations and individuals make well-informed decisions.

Data science is a fast-evolving domain that includes aspects of computer engineering as well as advanced math, economics, and business concepts.

There are, therefore, two distinguishing features of data science. The first is its emphasis on using technology to structure and draw insights from different sources of information. The second, more distinctive aspect of this field is its focus on the entire data life cycle, from collection to communication.

Time to completion depends primarily on the degree level. Full-time students can finish a bachelor's degree in four years, while those enrolled in associate or master's programs can earn their respective credentials in two years. As mentioned above, doctoral programs take significantly longer, given the requirements students need to meet to graduate. Part-time students who take fewer courses per semester typically take longer than these average completion times.

Data science programs generally include coursework in the broad subfields of machine learning, computer science, statistical research, math, and data processing. Common classes in these areas can include data systems, artificial intelligence, statistics in machine learning, and advanced data analytics. Aside from gaining this technical knowledge, students may also have the chance to develop their business, communication, and critical thinking skills.

While these learning outcomes may be common across several programs, data science curricula constantly evolve to reflect technological and business advancements. Moreover, degrees at different levels cover data science theories and subjects in varying amounts of detail, with undergraduate programs including introductory classes and graduate ones offering more room to explore individual areas of interest. As a result, data science curricula can differ significantly from program to program.

Curious to explore similar degrees? Check out our curated lists for the best online computer science degrees and the best online mathematics bachelor's programs. 

A data science degree — particularly one at the graduate level — can be challenging for some students. Data science is an interdisciplinary field that is based heavily on learning advanced math concepts and computer science theory, as well as gaining hands-on experience with research methods. The technical nature of these subjects can make data science concepts hard to grasp for those who don't have much real-world or academic experience in STEM-related fields.

If an organization uses data or information to market products, make strategic decisions, or analyze industry trends, chances are it employs a team of data scientists. This means graduates with a data science degree can find employment in numerous sectors, including business, government, and nonprofits. Moreover, with a strong set of technical abilities, as well as soft skills in areas such as communication and problem-solving, they can also explore data science careers of a non-technical nature. For example, in addition to working as database administrators or statisticians, they may be qualified for market research, business strategy, and product development roles.

With a large number of opportunities available, it's important to remember that some careers may be tied to degree level. While individuals with an undergraduate credential may be ready to apply for entry-level positions that require basic data literacy, mid-level roles may require applicants to have the sort of advanced knowledge and experience gained in a master's program. Those who are determined to work in research or academia, meanwhile, will typically require a doctorate.

With a shortage of skilled data analysts in the labor market, it may be possible to land a job without a degree. Bootcamps, for instance, are designed to train students in data science techniques, and some even help with job placement. However, without a formal data science education, you may not be able to advance your career or gain voluntary certifications that can make you a competitive candidate for higher level positions. A data science degree may also prepare you to better meet evolving organizational needs in this field, making graduates with this credential more employable.

There are some clear advantages to earning a data science degree online. First, online data science programs have the same curriculum and requirements as on-campus degrees and are, therefore, just as likely to be valued by future employers. Additionally, the flexible learning format can suit non-traditional students who work full-time or have family responsibilities. As a discipline that involves, in part, learning how to apply computer science theory, some aspects of data science may also translate well onto the online learning format.

Nevertheless, distance learners taking all their classes online should expect a different learning environment than their on-campus peers. For instance, there may be fewer opportunities to network with fellow students or interact with faculty members. Completing assignments and coursework alone may also feel isolating or daunting to some. While many programs include regular check-ins with faculty members, dedicated communication boards, and 24/7 tech support to make learning easier, distance learners should remember that earning a degree online requires self-discipline, time management, and motivation.

Potential Advantages

• A data science degree can help you secure many different types of computing occupations. Since students gain a range of skills related to data collection, analysis, and communication, they often qualify for multiple job opportunities related to information technology.
• Demand for data scientists is expected to remain high. Degree holders may find a booming labor market when they graduate, and, with employment opportunities expected to throughout the upcoming decade, aspiring data scientists have an optimistic job outlook.
• Data science is an exciting field full of chances to innovate. If you're a creative person that enjoys a challenge, this constantly evolving field may align with your personality and career goals.

Potential Drawbacks

• Without certification, a data science degree alone may not make you a competitive job candidate. Though voluntary, data science certifications can give you an edge on the job market by showing employers you have formalized training in a specific area. However, these require time and money to earn.
• As a data scientist, your job description may be blurry. This is a relatively young field in which professionals are still crafting their roles and responsibilities. If you prefer a career with more day-to-day certainty, data science may not be the right fit.

An accredited data science degree can prepare you for a lucrative career in technology. The Bureau of Labor Statistics (BLS) estimates that individuals employed in make an annual median salary of $105,990, which is more than double the national average for all occupations. But aside from earning potential, the value of a data science degree will also depend on your career goals, personal circumstances, and financial situation.