How Is Mathematics Used in Other Subjects? | Sciencing
Math requires different study processes. In other courses, you learn and understand the material, but you seldom have to actually APPLY IT. Read chapter 3 Connections Between the Mathematical Sciences and Other Fields: sciences involves unearthing and understanding deep relationships among The discipline encompasses the broad range of diverse activities related to. As a math teacher, you don't have to just teach the math concepts. You can also show how these math concepts work in the real world. You can even.
Literature and Writing Literature might seem like a far cry from math, but mastering basic arithmetic can enable students to better understand poetry.
The meter of poetry, the number of words to include in a line and the effect that certain rhythms have on the reader are all products of mathematical calculations.
At a more mundane level, math can help students plan reading assignments in literature classes by discerning their average reading time and estimating how long it will take them to read a particular work. The linear, logical thinking used in mathematical problems can also help students write more clearly and logically. Sciencing Video Vault Social Studies Social studies classes, such as history, often require students to review charts and graphs that provide historical data or information on ethnic groups.
In geography classes, students might need to understand how the elevation of an area affects its population or chart the extent to which different populations have different average life spans. Knowledge of basic mathematical terms and formulas makes statistical information accessible.
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The Arts Students interested in pursuing careers in theater, music, dance or art can benefit from basic mathematical knowledge. Musical rhythm often follows complex mathematical series, and math can help students learn the basic rhythms of dances used in ballet and theater performances. Art thrives on geometry, and students who understand basic geometric formulas can craft impressive art pieces.
However, only a small fraction of the people hired by those industry leaders actually hold degrees in mathematics and statistics; these slots are often filled by individuals with training in computer science, engineering, or physical science. While those backgrounds appear to be acceptable to employers, this explosion of jobs based on mathematical science skills represents a great opportunity for the mathematical sciences, and it should stimulate the community in three ways: This is already well recognized in the areas of search technology, financial mathematics, machine learning, and data analytics.
Relation and Uses of Mathematics in Other Subjects Essay
This will be discussed in the next chapter. In the past, training in the mathematical sciences was of course essential to the education of researchers in mathematics, statistics, and many fields of science and engineering. And an undergraduate major in mathematics or statistics was always a good basic degree, a stepping-stone to many careers.
But the mathematical sciences community tends to view itself as consisting primarily of mathematical science researchers and educators and not extending more broadly. As more people trained in the mathematical sciences at all levels continue in careers that rely on mathematical sciences research, there is an opportunity for the mathematical sciences community to embrace new classes of professionals. At a number of universities, there are opportunities for undergraduate students to engage in research in nonacademic settings and internship programs for graduate students at national laboratories and industry.
Some opportunities at both the postdoctoral and more senior levels are available at national laboratories Page 71 Share Cite Suggested Citation: It would be a welcome development for opportunities of this kind to be expanded at all levels.
Experiences of this kind at the faculty level can be especially valuable.
In an ideal world, the mathematical sciences community would have a clearer understanding of its scale and impacts. In addition to the steps identified in Recommendations andannual collection of the following information would allow the community to better understand and improve itself: Perhaps the mathematical science professional societies, in concert with some funding agencies, could work to build up such an information base, which would help the enterprise move forward.
However, the committee is well aware of the challenges in gathering such data, which would very likely be imprecise and incomplete. Needless to say, these are deeply intertwined, and it is becoming increasingly standard for major research efforts to require expertise in both simulation and large-scale data analysis.
Before discussing these two major drivers, it is critical to point out that a great deal of mathematical sciences research continues to be driven by the internal logic of the subject—that is, initiated by individual researchers in response to their best understanding of promising directions. While over the years there have been important shifts in the level of activity in certain subjects—for example, the growing significance of probabilistic methods, the rise of discrete mathematics, and the growing use of Bayesian statistics—the committee did not attempt to exhaustively survey such changes or prognosticate about the subjects that are most likely to produce relevant breakthroughs.
The principal lesson is that it continues to be important for funding sources to support excellence wherever it is found and to continue to support the full range of mathematical sciences research. Indeed, all areas of the mathematical and statistical sciences have the potential to be important to innovation, but the time scale may be very long, and the nature of the link is likely to be surprising.
Many areas of the mathematical and statistical sciences that strike us now as abstract and removed from obvious application will be useful in ways that we cannot currently imagine.
On the one hand, we need a research landscape that is flexible and non-prescriptive in terms of areas to be supported. We must have a research funding landscape capable of nurturing a broad range of basic and applied research and that can take into account the changing characteristics of the research enterprise itself. And on the other hand, we need to build and maintain infrastructure that will connect the mathematical and statistical sciences to strategic growth areas.
Quoted text is from p. Page 73 Share Cite Suggested Citation: That is because computational modeling is inherently mathematical. Accordingly, those fields depend on—and profit from—advances in the mathematical sciences and the maintainance of a healthy mathematical science enterprise.Relations in Mathematics
The same is true to the extent that those sectors increasingly rely on the analysis of large-scale quantities of data. This is not to say that a mathematical scientist is needed whenever someone builds or exercises a computer simulation or analyzes data although the involvement of a mathematical scientist is often beneficial when the work is novel or complex.
But it is true that more and more scientists, engineers, and business people require or benefit from higher-level course work in the mathematical sciences, which strengthens connections between disciplines.
And it is also true that the complexity of phenomena that can now be simulated in silico, and the complexity of analyses made possible by terascale data, are pushing research frontiers in the mathematical sciences and challenging those who could have previously learned the necessary skills as they carry out their primary tasks.
Relation and Uses of Mathematics in Other Subjects Essay Example | Graduateway
As this complexity increases, we are finding more and more occasions where specialized mathematical and statistical experience is required or would be beneficial. Some readers may assume that many of the topics mentioned in this chapter fall in the domain of computer science rather than the mathematical sciences. In fact, many of these areas of inquiry straddle both fields or could be labeled either way.
For example, the process of searching data, whether in a database or on the Internet, requires both the products of computer science research and modeling and analysis tools from the mathematical sciences. The challenges of theoretical computer science itself are in fact quite mathematical, and the fields of scientific computing and machine learning sit squarely at the interface of the mathematical sciences and computer science with insight from the domain of application, in many cases.
Indeed, most modeling, simulation, and analysis is built on the output of both disciplines, and researchers with very similar backgrounds can be found in academic departments of mathematics, statistics, or computer science.
There is, of course, a great deal of mathematical sciences research that has not that much in common with computer sciences research—and, likewise, a great deal of computer science research that is not particularly close to the mathematical sciences. The reason is that mathematical science researchers not only create the tools that are translated into applications elsewhere, but they are also the creative partners who can adapt mathematical sciences results appropriately for different problems.
This latter sort of collaboration can result in breakthrough capabilities well worth the investment of time that is sometimes associated with establishing a cross-disciplinary team. It is not always enough to rely on the mathematics and statistics that is captured in textbooks or software, for two reasons: These are the benefits to the nonmathematical sciences members of the team.