What can you do with a master’s degree in biostatistics, one of the highest-paying master’s degrees? Biostatisticians have responsibilities in every aspect of the data science life cycle as it pertains to matters of health, the environment, agriculture and other areas of quantitative research in biology. A typical day might be different depending on which stage of the life cycle you are working on at any given time, as well as the research you’re undertaking. The variety of research work a biostatistician might be involved in over the course of their career is one of the highlights of the occupation, according to the United States Bureau of Labor Statistics. Generally, a typical day will include working in one stage of the data science life cycle.
Capturing Data Related to Health and Biological Issues
Biostatisticians don’t just show up once all the data has been aggregated and start to analyze it. They are involved in quantitative research from the start, designing the methodology of the study and how the data will be captured. Data capture is the first stage of the data science life cycle and includes steps like acquiring, entering and extracting that data.
Biostatisticians can gather data from different sources and in different ways, and which way they choose often depends on what aspect of health they are studying. For example, research in areas of healthcare quality and patient satisfaction might prompt data collection directly from patients, such as through phone calls at different points in and after their treatment. If the biostatistician is researching rates of disease, infections and other risks, it might make more sense to gather data from patient records and physical tests, like blood tests, conducted by healthcare providers and staff.
While it takes strong technical skills to analyze data, it takes critical thinking skills to figure out the right way to capture the data that will answer your research questions accurately. You’re not just plugging information into an equation in this stage.
To make sure their findings are accurate, biostatisticians must focus on topics like the wording of questions and the units of measurement used in gathering this raw data. If they don’t, or if another party is responsible for capturing data and does so in an unsystematic fashion, they may have to “clean” the dataset by adjusting for measurement discrepancies, which can waste valuable time.
Cleaning data is one of the tasks involved in the stage of maintaining data. Other tasks include warehousing that data safely in a secure system, staging data in temporary storage areas while loading and transferring it and organizing it within new or existing data architecture.
Maintaining architecture may sound like the easiest part of the life cycle, but it’s just as important as other aspects of the work. If data isn’t safely maintained, it could get lost, corrupted or stolen, and the whole data acquisition process may be for nothing.
The stage of processing data into sets is what allows you to analyze it and ultimately make meaningful connections through statistical analysis. You have probably heard of data mining, which generally refers to finding patterns, trends and connections in large amounts of data, often through the use of traditional statistical methods and more sophisticated pattern recognition technologies. Data mining is just one part of the stage of processing data. As biostatisticians process data, they organize it into clusters and classifications that make sense according to the information observed, the samples examined and the questions the research is asking.
Data modeling, usually with the help of computer programs, is another part of this processing stage of the data science life cycle.
When it becomes time to analyze this data, biostatisticians use their knowledge of quantitative methods and of the problem or question they’re attempting to understand to determine which calculations will provide the answers they’re seeking. Since complex statistical analyses are usually performed by computers, biostatisticians may use statistical programming software to run these calculations. In some cases, the biostatistician may work with other professionals, like data scientists and data analysts, who focus on analysis more narrowly.
The more complex the dataset and the more measurements each set contains, the more challenging it can be to run analyses. In biostatistics, datasets are often high-dimensional due to the number of measurements recorded for a single sample, like a patient.
Communicating Data Findings
Even the most stunning research findings will have no impact if the biostatistician keeps those results to themselves. In the fields to which biostatistics applies, such as public health, healthcare, the environment and agriculture, failing to follow through with research by neglecting communication of data findings is particularly problematic, because a lot is at stake. The biostatistician must do a good job with tasks like data reporting and data visualization to get the results of their research out there to the world.
Once the biostatistician has effectively communicated their findings, they can be used in decision-making by stakeholders and managers in the healthcare industry, the pharmaceutical industry, the agriculture industry and environmental conservation efforts.