M.S. in Data Science vs Applied Statistics: What’s The Difference?
Posted on: 12/21/2017
In a single day, 2.5 quintillion bytes of data are created. The type of professionals best equipped to make use of this data between those with an M.S. in Data Science or an M.S. in Applied Statistics is hotly debated.
With data technologies rapidly advancing over the last few decades, experts in fields of study related to data exploration and mining have continually redefined themselves. The progression of “data science,” for example, has grown from a synonym of statistics to a distinct, necessary function for many of today’s leading businesses.
While earning an M.S. in Data Science can lead professionals to hold what Harvard Business Review is calling “The Sexiest Job of the 21st Century,” the role of a data scientist can also be filled by professionals with an M.S. in Applied Statistics.
After all, the ability to act on data lags far behind the ability to collect and store data, which is why statisticians, business analysts, and other data science professionals are all equally important and in high demand.
Between 2011 and 2014, job postings for data scientists more than doubled, according to the popular job search engine, Indeed.com, while the Bureau of Labor Statistics is projecting a 34% growth rate of job demand for statisticians between 2014 to 2024. In fact, organizations looking to best optimize their data can often benefit from hiring a collaborative team of professionals with backgrounds in applied and business statistics, data science, and programming.
When mapping out a higher education plan that will help satisfy the increasing demand for data experts, it’s important to consider one’s overarching career goals and how they might be attained with the successful completion of an M.S. in Data Science versus an M.S. in Applied Statistics.
Key Differences of an M.S. in Data Science vs an M.S. in Applied Statistics
M.S. in Applied Statistics:
- Courses focus on theoretical foundation in statistical theory and model building
- Mathematical and methodical approach to data analysis
- Employer-trusted, traditional methodology
M.S. in Data Science:
- Courses focus on manipulating data, machine learning, and database management concepts
- Business-driven data analysis
- Cutting-edge, but employers may be wary of this relatively new degree
M.S. in Applied Statistics
Professionals interested in gaining a thorough understanding of the theoretical foundation in statistical theory and receiving advanced training in model building are well-suited to obtain an M.S. in Applied Statistics. Those seeking this degree will also inevitably learn data science concepts, as this new field has its roots in statistical theory.
An M.S. in Applied Statistics typically offers courses that provide opportunities to acquire proficiency in programming languages such as SAS, R, and Python. Students will also learn to analyze wide varieties of data and will ideally have the chance to apply learned skills to real data sets. The M.S. in Applied Statistics will graduate experts who are confident in their ability to provide organizational leadership with a validated and thorough data analysis review.
Those who earn an M.S. in Applied Statistics (versus an M.S. in Data Science) can be confident that employers are familiar with the skillset obtained through this degree program, especially considering statistics was recently rated the second-best master’s degree for jobs by Forbes.
M.S. in Data Science
Earning an M.S. in Data Science is ideal for professionals who are interested in learning how to data mine in order to make predictions and data-driven decisions, likely in a business environment.
Course offerings included in the pursuit of an M.S. in Data Science will teach students how to extract knowledge from large amounts of data in order to weed out errors and improve business acumen. The M.S. in Data Science tends to provide students with the ability to not only recognize patterns in data, but to know how to obtain, continually reorganize, and manage data.
The M.S. in Data Science graduates students who can make predictions and sound decisions based on the validity of collected data, whereas an M.S. in Applied Statistics teaches students to understand data relationships and associations by testing statistical theorems.
While this master’s degree is cutting-edge and progressive, employers may be wary of its validity because it’s relatively new.
University of Delaware’s ASTAT
The University of Delaware offers a 100% online M.S. in Applied Statistics (ASTAT) for professionals who recognize that returning to school for an advanced degree is the optimal path for achieving their goals without disrupting a full-time work schedule.
University of Delaware’s ASTAT degree program is unique in that students are provided with multiple opportunities to learn from and apply developing skills to current, real-world problems. Distance learners benefit from close relationships the University of Delaware maintains with large, locally-based companies in the financial services, health care, chemical, pharmaceutical, technology, and farming industries.
Expert statisticians from these and other organizations were recruited to develop and instruct case-study based courses specifically for the online ASTAT. These full-time faculty members aptly prepare students for jobs with a median base salary of $80,000.