Statistics and the use of data are big business. With the advent of technological advances in data collection and storage, it has become easier and less expensive to collect data in many fields, calling for new job openings for data scientists and statisticians. But these terms are often used interchangeably, which can be confusing, and leads to the question: what’s the difference between data science and applied statistics?
Data Science vs. Statistics: How Do They Differ?
In 2015, the American Statistical Association (ASA) released a statement regarding the newer field of data science — with no clear consensus. Data science was understood to include areas like:
- Database management
- Statistics and machine learning
- Distributed and parallel systems
Three years later ASA continued the discussion, with the roles and definitions of data science and statistics, continuing to cross categories and defy specificity. At times, this boils down to singular understandings like, “a data scientist is a data analyst who can code.” However, upon review of the variety of definitions and roles, it can be claimed that the purpose of a data scientist is to employ large data sets (the proverbial “big data”) to solve business and other everyday problems.
Statistics has been generally understood to be the use of both theory and experience to gain an understanding of a particular phenomenon or relationships between behaviors or events. Michigan Technological University’s online statistics program offers a deep dive into “statistical theory and methods to address the practical problems of an evolving society.”
Data Scientist vs. Statistician
While statisticians are thoroughly immersed in statistical theory, they can seamlessly move beyond theory and solve practical problems in the manner of a data scientist. Programs like Michigan Tech’s online master's in applied statistics, prepares students to use theory to create innovative solutions that incorporate business culture and practices. In this sense, it becomes clear that there is no difference between data science and statistics that renders a statistician’s skills as being less practical than a data scientist’s.
It is also important to acknowledge that statisticians have always worked with large data sets and that work has informed data science as it is generally understood today. Statisticians aren’t strangers to developing models to organize large data sets into information that can be accurately interpreted and used to create new products, interventions, and practices. Applied statisticians use this ability to not only innovate, but to also provide a deep understanding of the problem to drive organizational growth and change.
Data Science and Statistics: Collaboration and Innovation
Research proves the value of exploratory analysis has increased with the expansion of data availability and computation; value has also increased for individuals’ ability to use data to think innovatively and create hypotheses. Technology is growing at an exponential rate to produce even more data with less expense. With the advent of more data obtained much more quickly, the need for statisticians and data scientists is growing. Working together, the statistician and the data scientist can join abilities to:
- Organize data
- Create appropriate and efficient computations
- Present accurate interpretations and efficient solutions
With the right educational program, the statistician and the data scientist can be one person.
Michigan Tech continues to lead the field of statistics, finding ways for practitioners to evolve, and paving the way for applied statisticians and data scientists to work in sync.
Intrigued by the various applications of statistics in the evolving world of business and ready to learn more? Start by exploring Michigan Tech’s master’s in statistics online.