A master’s in statistics degree, one of the highest paying master’s degrees, sounds a lot more focused than it really is. Statistics, the broad field of the analysis and interpretation of numerical data, can be applied to many different fields. Many different subtopics also make up the field of statistics and the coursework graduate students take in pursuit of this degree. Within the typical core coursework of a master’s in statistics program, students are likely to take classes in probability, theoretical statistics, applied statistics and statistical. Subtopics of statistics that you may take as an elective course include programming and computation for statistics, data science and biostatistics.
When you flip a coin or roll a set of dice, what are the odds of a certain outcome? That’s the fundamental question the study of probability, which is an important part of a master’s in statistics curriculum, seeks to answer. In a chance experiment, as simple as a coin flip or as complicated as studying the weather patterns, probability refers to the likelihood of an outcome happening. Of course, studies in probability at the graduate level are a lot more complicated than this example of a coin flip. As a master’s in statistics student, you will learn about conditional probability, random variables, discrete and continuous spaces and the laws of working with large numbers.
Having a thorough understanding of probability is important when you work in statistics because this knowledge can help you determine if findings and patterns are statistically significant or if they are simply a result of random chance.
Theoretical Statistics and Applied Statistics
A lot of coursework in statistics can be divided into either theoretical or applied statistics. Classes in theoretical statistics are based on theories and abstract concepts. Applied statistics refers to using statistical methods to analyze and interpret real data and solve real problems.
Most master’s in statistics programs will include some degree of study in both theoretical and applied statistics. Even master’s degree programs in applied statistics will still introduce students to the theoretical foundations of statistical concepts, although they will devote a lot more time to covering when and how to use those concepts than to the logical underpinnings of these theories.
Although there’s nothing wrong with studying the theoretical aspects of statistics, students who look for opportunities to gain real-life experience applying statistical methods will be better prepared for the job market, according to U.S. News & World Report.
Statistical Methods and Research
Whether in the academic sense or in industry, in the service of improving performance and profitability, research is an important part of statistics. Conducting research is how the quantitative data that statistics professionals analyze comes to exist. Research can take many forms, from random experimentation to controlled experimentation and from surveys and questionnaires to observational studies. If you’re going to work in statistics, then you should have an understanding of statistical and research methods and experimental design and analysis.
Students of graduate programs in statistics may learn about topics like survey methodology and population sampling as well as the mathematical and statistical methods of analysis, such as simple and multiple linear regression.
Statistics and Computation Programming
Computer technology is widely used in modern careers in statistics, so students in a master’s in statistics program would benefit from taking at least one course in programming. Learning the basics of the most popular programming languages in statistics, such as Python, R, Java and C++, can help you succeed in a wider array of job roles, since different positions may require knowledge of different programming languages.
Perhaps an even bigger benefit of taking courses in statistical programming is learning how to learn computer programming languages for use in statistical analysis. Programming languages will keep evolving, so being able to pick up new technologies is crucial.
One popular subspecialty of statistics is data science. When you study data science, you will often take classes in scientific computing, software development and machine learning. Data mining and artificial intelligence are possible aspects of study you could pursue, but so is advanced programming in languages such as R. If you go into the field of data science, your coding skills need to be as sharp as your analytical skills, and you are more likely to spend time working in an aspect such as database management or machine learning.
Generally, data science tends to be a more computer-focused aspect of statistics or, by some accounts, a separate field that includes aspects of statistics.
Statistics can be applied to many different fields, sometimes acquiring specialized names when used in certain fields. Biostatistics refers to the application of statistical concepts and methods to the field of biology, the life science concerned with living organisms. The coursework that falls under the subtopic of biostatistics includes sequences of study in the methods used in biostatistics, but it doesn’t end there. Students will also study the procedures and design of clinical trials, including how statistical methods are used to evaluate safety and efficacy data, as well as survival analysis.
Biostatistics may be offered as a concentration within a master’s in statistics program or as its own master’s degree, often offered through a school of health sciences or public health.