How is Mathematics used?
Zippia reviewed thousands of resumes to understand how mathematics is used in different jobs. Explore the list of common job responsibilities related to mathematics below:
- Assisted in homework completion and advancing reading comprehension and mathematics skills.
- Used my mathematics and reading skills to teach new and exciting ways for children of special needs to learn differently.
- Provided assistance to students in first and second grade in the development of mathematics and reading skills.
- Provide homework guidance, reinforcing the learning of basic concepts and study skills; Specialization in basic mathematics
- Worked with groups of 6-8 highs students developing their skills in mathematics and computers.
- Tutor children in various subjects of Mathematics and English to achieve higher test scores.
Are Mathematics skills in demand?
Yes, mathematics skills are in demand today. Currently, 42,374 job openings list mathematics skills as a requirement. The job descriptions that most frequently include mathematics skills are children's tutor, student teacher assistant, and teacher's assistant and tutor.
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What jobs can you get with Mathematics skills?
You can get a job as a children's tutor, student teacher assistant, and teacher's assistant and tutor with mathematics skills. After analyzing resumes and job postings, we identified these as the most common job titles for candidates with mathematics skills.
Student Teacher Assistant
- CPR
- Mathematics
- Art Projects
- Pre-K
- Classroom Management
- Educational Programs
Teacher's Assistant And Tutor
- Math
- Mathematics
- Administration Policies
- Label Materials
- Chemistry
- Language Arts
America Reads Tutor
- Math
- Mathematics
- Kindergarten
- Elementary Schools
- Language Arts
- Classroom Management
Volunteer Teacher Assistant
- Mathematics
- Art Projects
- Group Lessons
- Classroom Management
- Learning Environment
- Social Development
Tutor Coordinator
- Mathematics
- Math
- Academic Support
- Student Athletes
- Chemistry
- Language Arts
Tutor/Mentor
Job description:
Tutors and mentors are two different teaching jobs. Tutors oversee helping students understand varied subjects, assessing as well as encouraging them during the learning process. On the other hand, mentors go far beyond the role of tutors. They are wise and trusted counselors who help students get the motivation they need to advance in their chosen careers. Also, they share with their mentees their career paths, act as their role model, as well as provide guidance and emotional support.
- Math
- Mathematics
- Homework Assignments
- Mentoring Students
- Role Model
- Study
Charter
Job description:
Charters are the people who observe horse races, describe race call outs, and record statistical data of the race to use for publication. They use a formula to compute race completion times for all except for winning horses. Their binoculars are used during a race to view distance markers along the tracks and call out the horses' numbers, positions, and related data for other workers to record. They must have good communication skills, computer skills, and a good understanding of the horse racing industry.
- Strong Customer Service
- Mathematics
- School Programming
- RAN
- Social Studies
- Classroom Management
Private Tutor
Job description:
A private tutor is responsible for teaching students, usually in a home setting, within their proposed availability. Private tutors develop effective learning methods for the students' easy comprehension, utilize multiple learning resources, evaluate students' learning by giving activities, identifying areas of improvement, and monitoring the student's progress every session. Private tutors must be highly-knowledgeable of their subject areas to share learning techniques and assist the students with homework and other academic tasks. They must have excellent communication skills in responding to the students' inquiries and concerns to develop their comprehension.
- Mathematics
- Math
- GRE
- Language
- Organic Chemistry
- Grammar
Classroom Assistant
Job description:
A classroom assistant is responsible for monitoring the class activities and the students' learning progress under the command of a head instructor. Classroom assistants help the teachers in conducting engaging learning activities, creating comprehensive lesson plans, and gathering educational materials to support the students' needs. They take the initiative on observing the class when the teacher is away, leaving them activities to work on as the teacher instructed. A classroom assistant must have excellent communication and organization skills, as they also serve as a liaison between the students and the teacher for inquiries and concerns.
- CPR
- Mathematics
- Photocopying
- IEP
- Homework Assignments
- Child Care
Teaching Instructor
- Mathematics
- Political Science
- Lab Reports
- Course Curriculum
- Fine Arts
- Undergraduate Courses
Kindergarten Paraprofessional
- Math
- Mathematics
- Instructional Materials
- Classroom Management
- Behavior Management
- Bulletin Boards
Teacher Internship
Job description:
Teaching interns are individuals who assist teachers and educators in the classroom. The interns are instructed to fulfill the tasks set out and provided by the supervisors for them. They take part in meetings and jot down their minutes. It is part of their job to conduct research at the request of the supervisor. They make updates to social media platforms and make posts. Also, they create images to be used in posts through different social media platforms.
- Mathematics
- Classroom Management Strategies
- Language Arts
- Professional Development
- Learning Styles
- Social Studies
Classroom Volunteer
- Math
- Mathematics
- Kindergarten
- Language Arts
- Classroom Management
- Grade Class
Interrelated Special Education Teacher
- Mathematics
- Math
- Language Arts
- IEPs
- Education Programs
- Classroom Environment
GED Teacher
- Classroom Management
- Math
- Mathematics
- Social Studies
- Language Arts
- Curriculum Frameworks
How much can you earn with Mathematics skills?
You can earn up to $34,162 a year with mathematics skills if you become a children's tutor, the highest-paying job that requires mathematics skills. Student teacher assistants can earn the second-highest salary among jobs that use Python, $27,992 a year.
Job Title | Average Salary | Hourly Rate |
---|---|---|
Children's Tutor | $34,162 | $16 |
Student Teacher Assistant | $27,992 | $13 |
Teacher's Assistant And Tutor | $25,056 | $12 |
America Reads Tutor | $24,653 | $12 |
Volunteer Teacher Assistant | $27,869 | $13 |
Companies using Mathematics in 2025
The top companies that look for employees with mathematics skills are Navy Mutual, Claire's, and Raytheon Technologies. In the millions of job postings we reviewed, these companies mention mathematics skills most frequently.
Rank | Company | % Of All Skills | Job Openings |
---|---|---|---|
1 | Navy Mutual | 12% | 8,316 |
2 | Claire's | 10% | 1,080 |
3 | Raytheon Technologies | 9% | 1,224 |
4 | Army National Guard | 9% | 475 |
5 | U.S. Department of the Treasury | 9% | 0 |
20 courses for Mathematics skills
1. Mathematics for Engineers
This specialization was developed for engineering students to self-study engineering mathematics. We expect students to already be familiar with single variable calculus and computer programming. Through this specialization, students will learn matrix algebra, differential equations, vector calculus, numerical methods, and MATLAB programming. This will provide them with the tools to effectively apply mathematics to engineering problems and be well-equipped to pursue a degree in engineering. To get a better understanding of what this specialization has to offer, be sure to watch the Promotional Video!...
2. SQL Mathematical Functions
Welcome to this project-based course, SQL Mathematical Functions. In this project, you will learn how to use SQL Mathematical Functions to manipulate tables in a database. By the end of this 2-hour-long project, you will be able to use different Mathematical Functions to retrieve the desired result from a database. In this project, you will learn how to use SQL Mathematical Functions like CEIL(), FLOOR(), RANDOM(), SETSEED(), ROUND(), TRUNC(), SQRT(), CBRT(), and POWER() to manipulate data in the employees database. In this project, we will move systematically by first introducing the functions using a simple example. Then, we will write slightly complex queries using the Mathematical Functions in real-life applications. Also, for this hands-on project, we will use PostgreSQL as our preferred database management system (DBMS). Therefore, to complete this project, it is required that you have prior experience with using PostgreSQL. Similarly, this project is an intermediate SQL concept; so, a good foundation for writing SQL queries is vital to complete this project. If you are not familiar with writing queries in SQL and want to learn these concepts, start with my previous guided projects titled “Querying Databases using SQL SELECT statement," and “Performing Data Aggregation using SQL Aggregate Functions.” I taught these guided projects using PostgreSQL. So, taking these projects will give the needed requisite to complete this SQL Mathematical Functions project. However, if you are comfortable writing queries in PostgreSQL, please join me on this wonderful ride! Let’s get our hands dirty!...
3. Discrete Mathematics
WHAT IS THIS COURSE ABOUT? Discrete Mathematics (DM), or Discrete Math is the backbone of Mathematics and Computer Science. DM is the study of topics that are discrete rather than continuous, for that, the course is a MUST for any Math or CS student. The topics that are covered in this course are the most essential ones, those that will touch every Math and Science student at some point in their education. The goal of this course is to build the mathematical foundation for computer science courses such as data structures, algorithms, relational and database theory, and for mathematics courses such as linear and abstract algebra, combinatorics, probability, logic and set theory, and number theory. Discrete Mathematics gives students the ability to understand Math language and based on that, the course is divided into the following sections: SetsLogicNumber Theory ProofsFunctionsRelationsGraph TheoryStatisticsCombinatorics and Sequences and Series YOU WILL ALSO GET: Lifetime AccessQ & A section with supportCertificate of completion30-day money-back guaranteeHOW IS IT DELIVERED? I know visually seeing a problem getting solved is the easiest and the most direct way for a student to learn so I designed the course keeping this in mind. The materials are delivered through videos to make complex subjects easy to comprehend. More details on certain lessons are delivered through text files to provide more explanations or examples. The course is taught in plain English, away from cloudy, complicated mathematical jargon, to help the student learn the material rather than getting stuck on fancy words. HOW DO I LEARN BETTER? There are quizzes after each lecture so you can test your knowledge and see how much of the material has sunk in. I suggest you go through each lesson several times to better understand the content...
4. Mathematics for Machine Learning
For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.\n\nIn the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.\n\nThe second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.\n\nThe third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.\n\nAt the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning...
5. Mathematics for Computer Science
“Welcome to Introduction to Numerical Mathematics. This is designed to give you part of the mathematical foundations needed to work in computer science in any of its strands, from business to visual digital arts, music, games. At any stage of the problem solving and modelling stage you will require numerical and computational tools. We get you started in binary and other number bases, some tools to make sense of sequences of numbers, how to represent space numerical using coordinates, how to study variations of quantities via functions and their graphs. For this we prepared computing and everyday life problems for you to solve using these tools, from sending secret messages to designing computer graphics. If you wish to take it further you can join the BSc Computer Science degree and complete the full module ‘Numerical Mathematics’. Enjoy!”...
6. Applied Mathematical Optimization
Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criterion, from some set of available alternatives.[1] It is generally divided into two subfields: discrete optimization and continuous optimization. Optimization problems arise in all quantitative disciplines from computer science and engineering[2] to operations research and economics, and the development of solution methods has been of interest in mathematics for centuries.[3]In the more general approach, an optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations constitutes a large area of applied mathematics. More generally, optimization includes finding best available values of some objective function given a defined domain (or input), including a variety of different types of objective functions and different types of domains. Optimization problems can be divided into two categories, depending on whether the variables are continuous or discrete: An optimization problem with discrete variables is known as a discrete optimization, in which an object such as an integer, permutation or graph must be found from a countable set. A problem with continuous variables is known as a continuous optimization, in which an optimal value from a continuous function must be found. They can include constrained problems and multimodal problems...
7. Applied Business Mathematics
Welcome to Applied Business Mathematics. As we all know that Mathematics is the mother of all sciences even social sciences. In this course, Mathematics is a key role in the education of students belonging to Management Sciences, Business, Economics, and the Social Sciences. This course is appropriate for all levels of management, business, and economics students. This course focuses on the following topics: Study concept of Linear Equation and its applications in different business and economic models. Study System of Linear Equations along with applications in daily life with the help of different examples. Linear Inequality along with their solutions on the number line with various examples. Using MS Excel and Python to solve System of Linear Equation with application in different business and management models. Solve System of Linear Equations using Matrices techniques like Gauss Jordan Method and Gaussian Elimination Method. Concept of Mathematical Function along with concepts of Linear Cost Function, Linear Revenue Functions, Linear Profit Functions, Break-Even Models. In the Finance Mathematics section, we will focus on Simple and Compound Interest, Annuities, Cost-Benefit Analysis, and some more useful concepts, and compare the difference between Simple and Compound Interest with the help of graphs in Python. An introduction to Linear Programming with different techniques like Simplex Method, Big-M Method, Dual Method along with some graphical techniques...
8. Mathematics of Finance
Learn the basics of the mathematics behind finance. In this course you will learn about compound interest, annuities, and amortization of loans. For more details check out all the lecture titles and descriptions!...
9. Mathematical Biostatistics Boot Camp 1
This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required...
10. Mathematical Biostatistics Boot Camp 2
Learn fundamental concepts in data analysis and statistical inference, focusing on one and two independent samples...
11. Mathematics for Machine Learning: PCA
This intermediate-level course introduces the mathematical foundations to derive Principal Component Analysis (PCA), a fundamental dimensionality reduction technique. We'll cover some basic statistics of data sets, such as mean values and variances, we'll compute distances and angles between vectors using inner products and derive orthogonal projections of data onto lower-dimensional subspaces. Using all these tools, we'll then derive PCA as a method that minimizes the average squared reconstruction error between data points and their reconstruction. At the end of this course, you'll be familiar with important mathematical concepts and you can implement PCA all by yourself. If you’re struggling, you'll find a set of jupyter notebooks that will allow you to explore properties of the techniques and walk you through what you need to do to get on track. If you are already an expert, this course may refresh some of your knowledge. The lectures, examples and exercises require: 1. Some ability of abstract thinking 2. Good background in linear algebra (e.g., matrix and vector algebra, linear independence, basis) 3. Basic background in multivariate calculus (e.g., partial derivatives, basic optimization) 4. Basic knowledge in python programming and numpy Disclaimer: This course is substantially more abstract and requires more programming than the other two courses of the specialization. However, this type of abstract thinking, algebraic manipulation and programming is necessary if you want to understand and develop machine learning algorithms...
12. Mathematics for Machine Learning: Multivariate Calculus
This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the “rise over run” formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be. We also spend some time talking about where calculus comes up in the training of neural networks, before finally showing you how it is applied in linear regression models. This course is intended to offer an intuitive understanding of calculus, as well as the language necessary to look concepts up yourselves when you get stuck. Hopefully, without going into too much detail, you’ll still come away with the confidence to dive into some more focused machine learning courses in future...
13. Master Discrete Mathematics: Logic
Are you struggling with the basic mathematical skill you need as a computer scientist? Are you unhappy with your instructor's ability to teach the fundamental skills you need to do well in your math courses? Master Discrete Mathematics: Logic is perfect for you. I cover all of the important topics thoroughly at a university level with lecture videos, examples, additional problems, and sample exams with unique and challenging questions that will help you identify your weak points and master the material. Each video will cover all the relevant information you need to know in under 15 minutes. I will make things simple, but still tackle the tricky questions that might seem confusing. I encourage you to preview the example videos and problem set I have available for free below. You will succeed in Discrete Math...
14. Introduction to Discrete Mathematics for Computer Science
Discrete Mathematics is the language of Computer Science. One needs to be fluent in it to work in many fields including data science, machine learning, and software engineering (it is not a coincidence that math puzzles are often used for interviews). We introduce you to this language through a fun try-this-before-we-explain-everything approach: first you solve many interactive puzzles that are carefully designed specifically for this online specialization, and then we explain how to solve the puzzles, and introduce important ideas along the way. We believe that this way, you will get a deeper understanding and will better appreciate the beauty of the underlying ideas (not to mention the self confidence that you gain if you invent these ideas on your own!). To bring your experience closer to IT-applications, we incorporate programming examples, problems, and projects in the specialization...
15. Mathematics for Machine Learning and Data Science
Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly Specialization is where you’ll master the fundamental mathematics toolkit of machine learning.\n\nMany machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. This is a beginner-friendly program, with a recommended background of at least high school mathematics. We also recommend a basic familiarity with Python, as labs use Python to demonstrate learning objectives in the environment where they’re most applicable to machine learning and data science...
16. Constructivism and Mathematics, Science, and Technology Education
This course is designed to help participants examine the implications of constructivism for learning and teaching in science, mathematics, and technology focused areas. Course readings, discussions, and assignments will examine constructivist views of learning, research on students' ideas and idea-based interactions, research on instructional approaches taking student ideas into account, and challenges in implementing constructivist perspectives in instruction...
17. English for Science, Technology, Engineering, and Mathematics
Welcome to English for Science, Technology, Engineering, and Mathematics, a course created by the University of Pennsylvania, and funded by the U.S. Department of State Bureau of Educational and Cultural Affairs, Office of English Language Programs. To enroll in this course for free, click on “Enroll now” and then select "Full Course. No certificate." If you want to get a Coursera Verified Certificate for free, please fill out the Financial Aid form. This course is designed for non-native English speakers who are interested in improving their English skills in the sciences. In this course, you will explore some of the most innovative areas of scientific study, while expanding your vocabulary and the language skills needed to share scientific information within your community. In unit 1, you will learn how to preview texts and practice some of the language used to make comparisons when talking about global warming and climate change. In unit 2, you will examine the chemistry of climate change and the language of cause and effect. In Unit 3, you will learn about some of the impacts of Climate Change and the language used to describe these effects. In Unit 4, you will learn reading strategies that can help you explore the science behind some new energy systems. In the final unit, you will investigate practical advances in Nanotechnology that help slow down climate change, while developing your own research skills in English. Unless otherwise noted, all course materials are available for re-use, repurposing and free distribution under a Creative Commons 4.0 Attribution license. Supplemental reading materials were provided by Newsela, which publishes daily news articles at a level that's just right for each English language learner...
18. Master Discrete Mathematics: Set Theory
Are you struggling with the basic mathematical skill you need as a computer scientist?Are you unhappy with your instructor's ability to teach the fundamental skills you need to do well in your math courses?Master Discrete Mathematics: Set Theory is perfect for you. I cover all of the important topics thoroughly at a university level with lecture videos, example videos, additional problems, and sample exams with unique and challenging questions that will help you identify your weak points and master the material. Each video will cover all the relevant information you need to know in under 15 minutes. I will make things simple, but still tackle the tricky questions that might seem confusing. I encourage you to preview the example videos and problem set I have available for free below. You will succeed in Discrete Math...
19. Master in Geogebra for Mathematics
What is the course about?GEOGEBRA is one of the most used applications for mathematics and dynamic geometry worldwide. It is an excellent option to solve exercises and problems graphically and algebraically. It is FREE, since it responds to a free software project, developed by universities for educational use and is available for computer and mobile devices (Android and Iphone). In this course we will learn about the program options for various Mathematics topics such as: geometric constructions, function analysis, calculus, 3D graphics, through different exercises that will help you to know the graphing commands and tools in each case. If you are a student or teacher, the course is for you, as it serves both to learn and to teach mathematics. In addition, we will see how to do simulations or modeling in a realistic way, to better visualize the problems and exercises. What topics does the course contain?The topics that we will deal with in the course are:• Graphs and analysis of functions• Plane and space geometry• Graphical resolution of inequalities• Algebraic calculus• Statistical graphs and probability• 3D graphicsWhy should you take this course?We will teach you to use Geogebra from the basics, with practical exercises that will help you better understand each application. We will also give you additional tools for your classes, so that you can make videos or tutorials...
20. Mathematical Foundations of Machine Learning
Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math. Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. But understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increase the impact you can make over the course of your career. Led by deep learning guru Dr. Jon Krohn, this course provides a firm grasp of the mathematics - namely linear algebra and calculus - that underlies machine learning algorithms and data science models. Course SectionsLinear Algebra Data StructuresTensor OperationsMatrix PropertiesEigenvectors and EigenvaluesMatrix Operations for Machine LearningLimitsDerivatives and DifferentiationAutomatic DifferentiationPartial-Derivative CalculusIntegral CalculusThroughout each of the sections, you'll find plenty of hands-on assignments, Python code demos, and practical exercises to get your math game in top form! This Mathematical Foundations of Machine Learning course is complete, but in the future, we intend on adding bonus content from related subjects beyond math, namely: probability, statistics, data structures, algorithms, and optimization. Enrollment now includes free, unlimited access to all of this future course content - over 25 hours in total. Are you ready to become an outstanding data scientist? See you in the classroom...