While Statistics is all about numbers, are math skills or analytic skills more important? (Master in Statistics)

Are statistics all about numbers?

Statistics involves numbers in the form of numerical data. If there is no data, there will be no statistics. The discipline concerns itself with the branch of mathematics that collects, analyzes, interprets, explains, and presents a small group or myriad of numbers. The result of the process may formulate a projection, such as pharmaceutical studies, to determine what percentage of patients may experience a specific side effect. On the other hand, the data merely represents information from the collection of numbers, such as population figures from census reports.

The application of statistics goes beyond science, like politics, industry, psychology, banking, Wall Street, accounting, management, and more analyze numbers. Relationships between supply and demand, as wells as imports and exports, apply statistical information. Bankers use statistics to estimate the number of people making deposits compared to the number of people requesting loans. Currently, the world is awash in statistics related to the spread of the coronavirus, known as COVID-19. News sources report on the number of infected people, death totals, the percentage of fatalities per 100,000 people, projections of future infections, and others.

College Level

Beginning with an undergraduate degree in statistics, you will have a range of courses in disciplines associated with mathematics. Consider mathematics as the trunk of a tree from which there are several branches. Each branch designates a specific area. Examples are calculus, geometry, trigonometry, algebra, algorithms, differential equations, and statistics.

A Bachelor of Science in Statistics emphasizes math courses, such as those mentioned above. Most programs incorporate classes in the analysis of data or Applied Statistics – the course titled from Wright State University. This two-part course of six credit hours teaches computer software analysis and display of data, regression analysis, and analysis of variance techniques. Analysis of data is crucial to working with and studying statistics.

Individuals not conversant in the language of statistics will have zero comprehension of terms used in the Theory of Probability course at Stanford University. Its Master of Science in Statistics course by this title studies random variables, expectation, central limit theorem, conditional probability, exponential spaces, and laws of large numbers. A class in Statistical Inference explores decision theory, interval estimation, Bayesian analysis, and maximum likelihood. Therefore, a heavy concentration of mathematics and analysis exist.

Undoubtedly, graduate programs in statistics devote the majority of hours to different branches of mathematics. However, classes that explore the collection and analysis of data are equally important. The assembly of data is of little use if there is no analysis. The analyst must have the skills to decipher that numbers, mainly when gathered to support or contradict a hypothesis. Or the data applies to a clinical study of a new drug. What does the data reveal about the efficacy of the medication?

Statistics involves the collection of data, followed by the analysis or interpretation of said data or figures. Criminologists collect data on where and when crimes occur to evaluate the allocation of police officers. The compilation of this information is of no value if there is no interpretation. Typically, there is a purpose for the collection of numbers. The exception is the presentation of statistics strictly for information purposes—for example, the mortality rates by different diseases or traffic accidents.

Is there a way to reduce the emphasis on math disciplines, and still analyze the data? The answer is a Bachelor of Science or Master’s degree in data science or data analysis. The Bachelor of Science in Data Management/Data Analytics at Western Governors University is one example. Data management studies physical schemas, databases, tables, views, foreign keys/primary keys (FKs/PKs), and populating tables. Data analysis covers the techniques and procedures involved in data analytics, including the presentation of questions solved from a given data set, set up experiments, statistics, and hypotheses.

At the graduate level, there is an emphasis on the verification of data, analytic techniques, methods for analyzing data, the role of regulatory agencies, and the ethical issues on the use of data. Students might also learn how to synthesize the technical components of data analysis into reports, presentations, and visual dashboards in a consistent and professional format. These programs generally require prior coursework at the undergraduate level in computer science or computer programming.

Conclusion

The complexity of math courses requires students, enrolled in a graduate statistics program, to have an aptitude for math and all of its branches. For students, enamored with math should succeed in such a program. The role of analytics, although a critical component, can be learned, whereas math demands a penchant for the subject.