Five Types of Mathematics in the Field of Data Science
- Linear Algebra
Data science has taken the world by storm. Data science impacts every other industry, from social media marketing and retail to healthcare and technological developments.
Data science uses many skills, including analysis, reading comprehension, visual adaptation, and calculation. Not least among these skills is mathematics. While students may choose to specialize in one or two mathematical branches dependent on their professional goals, generally they will learn a number of mathematical branches throughout their degree studies in order to graduate. Here are five different types of mathematics used in data science.
Related resource: 20 BEST DATA SCIENCE CERTIFICATE PROGRAMS
Although algebra is used in data science as more of a stepping stone mathematical system, mastery of algebraic skills is vital to success in the field. Algebra teaches data science students how to calculate variables in data science, a skill critical to making predictions and calculating data ranges. Developing strong skills in algebra is also important to success in statistics, linear algebra, probability, and calculus later on.
Calculus is an extremely abstract mathematical form, but understanding its function is important to data science. Studying calculus assists the student in understanding algorithm calculation, machine learning, and the assessment of changes in data streams and information in real-time or in a posteriori analysis.
The greater majority of data science degree programs require calculus, but students committed to completing their degree will be successful with careful attention to the development of mathematical skills leading to calculus and utilization of mathematical resources at their institution.
Probability involves the use of statistics and prediction and is an important mathematical branch in data science. Probability is used to track and assess trends in data in every industry, and is used heavily in retail- and sales-based industries to calculate product success. Probability uses skills the student develops in algebra and statistics classes.
4. Linear Algebra
Linear algebra is a convex of algebraic equations and geometry and is an execution-based form of mathematics versus abstract. Linear algebra also instructs the student in higher-form mathematical concepts such as planes, matrices, and vectors, and to analyze and manipulate potential outcomes visually via its practice. Additionally, it is used in machine learning, regularization, correlative matrices, and value decomposition.
Statistics involves the algebraic calculation of existing data and assessing trends based on that data. It allows data scientists to make estimates on trends and make predictions regarding them, as well as to analyze past trends and draw conclusions from them. Statistics are used in every industry and have an especially relevant impact on healthcare, retail, and marketing.
According to the Bureau of Labor Statistics, data science is one of the fastest-growing science sectors. Each of these mathematical branches represents not just raw knowledge, but a critical skillset to success in data science. Students in data science who work diligently at their mathematical skills will not only find success in their degree program but professionally upon graduation and in the years of their data science careers to come.