Online BS in Mathematics CurriculumOnline BS in Mathematics CurriculumOnline BS in Mathematics Curriculum
Mathematics is among the most versatile and flexible degrees you can pursue as an undergraduate. The knowledge and skills you can gain through foundational, theoretical, and practical college math courses help equip you to seek careers in diverse settings like education, statistics, analytics, data science, and technology.
When you earn your online bachelor’s in math, you can use the knowledge you gain in the classroom to think logically and solve problems creatively in your career. Check out how our online mathematics curriculum can help prepare to achieve your educational and professional goals.
Maryville students are brave
“I’m taking Intro to Python right now and am amazed by Professor Bean’s knowledge. I’m happy to see the expertise of my professors in math and data science at MU are comparable to videos of MITOpenCourseWare.” – Miguel Montes, Online Bachelor of Science in Mathematics student
Maryville University Online BS in Math Curriculum
Maryville’s online Bachelor of Science in Mathematics comprises 128 credit hours and includes coursework in general education, the mathematics major, and mathematics electives. Build your collegelevel math foundation and advance your understanding with our required mathematics major courses. Our program is also designed with a builtin data science minor to help you develop skills to prepare for hightech careers in coding, machine learning, and more.

Prerequisite: MATH152. This is the third course of the calculus sequence. Topics include vectorvalued functions; partial derivatives and applications; multiple integrals and applications; double integrals in polar form; substitutions in multiple integrals; line integrals; Green’s Theorem in the plane; surface integrals; Stokes Theorem.

This course introduces the basic mathematical theory and proofs of the fundamental theorems and formulas in preparation for further studies in mathematics, data science, and mathematics education. The course prepares students for the demand of advanced courses while giving students an opportunity to witness and participate in the intrinsic beauty of formal mathematical thought. Topics include logic; set theory and related topics, mathematical induction and recursion; fundamental counting principles; combinatorics; basic study of number theory; and complex numbers.

This course covers fundamental principles of number theory. Topics include primes and composites; divisors and multiples, divisibility, remainders; the Euclidean Algorithm; the fundamental theorem of arithmetic; congruencies and applications of congruencies; and continued fractions.

This one semester course is designed to introduce the students to the fundamental concepts underlying the study of linear algebra. Topics include matrix algebra; systems of linear equations; vector spaces and subspaces; basis and dimensions; orthogonality; determinants; eigenvalues and eigenvectors; diagonalization of matrices; and linear transformations.

This course introduces the use of mathematical modeling based on calculus and differential equations. Topics include firstorder differential equations; Eulers method and RungeKutta method; linear equations of higher order; nonlinear differential equations; systems of equations; transforms; and numerical methods. Practical applications are emphasized and computers will be employed to illustrate the underlying mathematical principles.

This is the first in a sequence of two onesemester courses on probability. Topics include basic probability concepts, conditional probability, Bayes Theorem, distribution of random variables; moments, moment generating functions, percentiles, mode, skewness, univariate transformations, discrete distributions (binomial, uniform, hypergeometric, geometric, negative binomial, Poisson), and continuous distributions (uniform, exponential). This course is calculus—based.

This is the second in a sequence of two onesemester courses on probability. Topic includes probability function and probability density function of one continuous random variable such as exponential distribution, normal distribution, Gamma distribution, beta distribution, and log normal distribution; mixed distributions; joint probability functions and joint probability density functions; conditional probability and marginal probability distributions; central limit theorem; joint moment generating and transformations; covariance and correlation coefficients. This course is calculus based.

This course introduces students to basic concepts of inference and main methods of estimation. Topics include statistical inferences such as point and interval estimation of parameters, statistical hypotheses and statistical tests; inferences for single samples; inference for two samples; inferences for proportion and count data; and advance estimation methods including Moment, percentile matching and Maximum Likelihood. This course emphasizes the applications of the theory to statistics and estimation. This is a calculusbased one semester course. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. Students who receive a B or higher in this course are eligible to receive VEE (Validation by Education Experience) credit from the Society of Actuaries in Mathematical Statistics.

The fundamental analysis course will cover topics both in real analysis and complex analysis. Topics covered in real analysis include sets, properties of the real numbers, compactness of closed intervals, the countability compact property of closed intervals, continuous functions, completeness of the real number line. Topics in complex analysis will cover complex numbers, complex plane, Cauchy Reimann equations, and Cauchy’s Integral Theorem, the concept of the periodic function, Fourier series, Fourier transformation.

Optimization is an essential and important technique for solving problems in many disciplines. Topics covered in the course will be Introduction to Modeling, Linear Programming, The Simplex Method, Network Models, Integer Programming, and NonLinear Programming. Modern, realworld examples motivate the theory throughout the course.

Credits: Three (3) Prerequisite: MATH 316. A first introduction to abstract algebra through group theory with an emphasis on concrete examples. The course will introduce groups, subgroups, homomorphisms, quotients groups and prove foundational results including Lagranges theorem, Cauchys theorem, orbitcounting techniques and the classification of finite
Data Science Minor (Required)

Prerequisite: MATH117. The course develops the core concepts and skills in statistical inference and computational techniques through working on realworld data. The course is to introduce the foundation of data science to entrylevel students who have not previously taken statistics or computer science courses.

Prerequisite: MATH117. Students receive basic training in Microsoft Excel. A variety of reallife math models will provide the context for developing spreadsheet proficiency, including functions and formulas, pivotal tables, statistical analysis, numerical solutions, optimization and graphical output. Other areas to be covered include database applications and basic application programming techniques.

This course covers practical issues in data analysis and graphics such as programming in R, debugging R code, Jupyter Notebook, cloud computing, data exploration, and data visualization. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.

This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib and Seaborn) and modeling libraries (Statsmodels and Scikitlearn). Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. CrossListed: DSCI503

This course is for students who want to enhance their SQL skills through exploring realworld examples. Topics covered include but are not limited to patternmatching using regular expressions, analytical functions, and common table expressions. Students are expected to be able to construct advanced SQL queries to retrieve desired information from the database and solve realworld problems Crosslisted: DSCI504

This is an introductory course in machine learning intended primarily for students majoring or minoring in Mathematics, Data Science or Actuarial Science. This course may also be useful for those using predictive modeling techniques in business, economics or research applications. The main focus of this course is to understand the basic operations and applications of what we currently call machine learning. This course will cover material from several sources. A few main topics that will be covered include: how machine learning differs from traditional programming techniques, data manipulation and analysis, some basic coding skills and an introduction to some of the tools available for data scientists. Specific application techniques will include the following (as time permits): data acquisition, classification, regression, overfitting, supervised and unsupervised training, normalization, distance metrics, kmeans clustering, error calculation, optimization training, treebased algorithms (including random forests), frequent item sets and recommender systems, sentiment analysis, neural networks, genetic algorithms, visualizations, and deep learning (including an introduction to convolutional neural networks and generative adversarial networks). CrossListed: DSCI508
Electives

This course introduces students to basic discrete mathematics concepts. Topics include logic, elementary number theory methods, number systems, sets, functions and relations, counting and probability, theories of graphs and trees, and analysis of algorithm efficiency.

This course is required for Secondary Education students specializing in math. It is also taken by math majors who are interested in geometry or who want to gain experience in writing proofs before they attempt more advanced math courses. Topics include triangles congruence, polygons, Pythagorean Theorem, formal/informal proofs, coordinate systems, conic sections, and transformations.

This course focuses on model development, interpretation, understanding assumptions and evaluation of competing models. Topics include the basics of statistical learning, linear models, and time series models. This course covers a majority of the learning objectives for the Society of Actuaries (SOA) examination SRM (Statistics for Risk Modeling).

This class is an introduction to the SAS programming language. Topics include reading, exporting, sorting, printing, and summarizing data; modifying and combining data sets; writing flexible code with the SAS macro facility; visualizing data; and performing descriptive and basic statistical analyses such as Chisquare tests, TTests, ANOVA, and regression. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. CrossListed: DSCI507

This course covers text analytics, the practice of extracting useful information hidden in unstructured text such as social media, emails and web pages using Python. Topics include working with corpora, transformations, metadata management, term document matrices, word clouds, and topic models. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.

This course covers principles of experiments and basic statistics using R. Topics include analysis of variance, experimental designs, analysis of covariance, mixed model, categorical data analysis, survey data analysis, sample size and power analysis, and model comparison. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.

This course is intended for students with introductory experience in SQL, R, and Excel. In this course, students will learn how to connect SQL Server to tools like Excel, R, and Tableau, and how to leverage the database engine to manipulate large amounts of data for data analysis tasks. Students will learn how to create analytical plots in Excel and R and how to follow best practices for creating reportquality graphs and presentations. Students will learn to use R Markdown so that their analysis follows the reproducible research paradigm. Finally, students will learn to build reporting and analytics dashboards in Shiny and Tableau. This course will be projectbased. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class.

This course introduces students to fundamental statistical learning techniques that can be applied to realworld business problems. Topics include generalized linear models, treebased models, clustering methods, and principal components analysis. It trains students to understand key steps and considerations in building predictive models, selecting a best model, and effectively communicating the model results. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. Crosslisted: DSCI512

This course targets data scientists and engineers. It covers programming with RDDS, Tuning and debugging Spark, Spark SQL, Spark steaming and machine learning with MLlib. It provides students the tools to quickly tackle big data analysis problems on one machine or hundreds. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.

This course is an introduction to deep learning with an emphasis on the development and application of advanced neural networks. It covers convolutional neural networks, recurrent neural networks, generative adversarial networks, and deep reinforcement learning. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills.

This course provides a foundational understanding of blockchain to students. The idea of what blockchain is, why it is needed, and the problems it solves is covered. An overview of how blockchain technology works and is developed is covered as well as the structure of these technologies. The potential as well as the limitations of blockchain is reviewed as well as how these limitations can be overcome.

This course introduces students to fundamental features of Java programming language. Topics include data types, control flow and loops, objects, classes, encapsulation, inheritance and polymorphism.

This is an introduction to computer programming in C++ language. The course covers structural programming concepts, simple data types and algorithms in addition to basic C++ syntax, operators, control structures, arrays, pointers, function parameter passing, and object programming. Projects are required for coding techniques, program design, and debugging.

This course covers the practical application and usage of database systems. An emphasis is placed on relational databases, but nonrelational databases are introduced as well. Topics include database design and architecture, the SQL language, data storage, database programming, and NoSQL databases. Prerequisite: COSC 130, COSC 150, or DSCI 303

This course studies the design and implementation of data structure and algorithms. Topics include applications of data structures such as stacks, queues and linkedlists, analysis of algorithms, and algorithmic tools and techniques, including sorting and searching methods. Requires substantial object programming projects to solve real world problems using these data structure and algorithms.
To ensure the best possible educational experience for our students, we may update our curriculum to reflect emerging and changing employer and industry trends. Undergraduate programs and certificates are designed to be taken at a parttime pace. Please speak to your advisor for more details.
Ready for your next step?
What skills and competencies are taught in college math courses?
Online bachelor’s degree programs in mathematics can help you develop a robust range of useful skills. In addition to providing knowledge and an understanding of complex mathematical concepts and applications, math degrees are designed to help you build skills like:
 Analytical thinking. When you earn your math degree, you build strong analytical skills that can help you gather information, synthesize it to extract meaning, communicate your findings, and leverage your analysis to solve problems.
 Quantitative thinking. Learn to analyze and find meaning in even the largest, most complex data sets as you develop strong quantitative skills. This competency involves the ability to work with numbers, from generating data to analyzing it systematically, which can help you thrive in both theoretical and applied settings.
 Statistics. When navigating a large data set, knowledge of statistics can help you discover trends, make predictions, summarize findings, and understand the limitations and potential of the data available. Statistics skills are useful in a variety of fields, including data science, government, politics, medicine, nonprofits and NGOs, public health, technology, retail, and marketing, so a deep understanding of statistics can go a long way when you’re on the job market.
 Mathematical application. Go beyond theoretical components of mathematics and learn how the numbers and formulas apply to the real world. Applied mathematics skills can open the door to a wide variety of professional opportunities. Discover how to leverage your knowledge in fields such as medicine, meteorology, cosmology, finance, technology, ecology, engineering, travel, sports, and more — math skills can apply in some form to almost any industry.
What courses can I expect to find in my bachelor’s in mathematics curriculum?
When you choose to study mathematics, there are plenty of specialized paths for you to choose from. So you can typically focus on the area of math that interest you most. At Maryville University, our online B.S. in Mathematics gives you the chance to deepen your understanding of math and numbers through courses like:
 Foundations of Mathematics. Foundational coursework in math introduces you to the mathematical theories and proofs applied in calculus. In this course, you can build your understanding of topics like logic, calculus theory, mathematical inductions, real number properties, and functions. In addition to preparing you for advanced topics in math, these skills are applicable to many professional settings and useful across an array of industries.
 Applied Differential Equations. In applied differential equations courses, you can gain competency in calculus and differential equations. The coursework explores topics such as firstorder differential equations, linear equations of higher order, and nonlinear differential equations. Scenarios inspired by real life can help you discover how to apply your learning through computers, mathematical models, and other tools.
 Probability. The study of probability is important if you need to make sense of uncertainty and randomness in data during your studies or career. By learning to discern patterns, examine the distribution of random variables, and calculate percentiles, you can leverage your knowledge of probability to recommend decisions and solve problems.
 Math Modeling. Mathematical models — such as graphs, diagrams, and charts — represent situations or systems in the real world. The application of this topic spans across a plethora of professional uses. In this course, you get the chance to work with and study models that test hypotheses, represent linear time, analyze and compare data, determine averages, and make accurate estimations. You can also learn how to determine which mathematical models are an appropriate fit for your goals and apply them across an array of fields, including business, marketing, economics, social science, and biological science.
 Machine Learning. Machine learning is the science of training machines and computers to learn through algorithms, data processing, or other means. The machine learning course allows you to study topics like supervised learning, learning theory, adaptive control, neural networks, and machine learning applications. These concepts can be applied professionally to selfdriving cars, navigation software, user experience of social media, natural language processing, and fraud detection, among many other areas — so you can prepare to pursue a hightech career in an exciting field.
Learn more about our online bachelor’s degree in mathematics.
If you’re excited about a future in mathematics, your online degree can help prepare you for any number of exciting professional outcomes.
At Maryville University, our college math courses are designed to help you pursue a rewarding future and enhance your quantitative, analytical, statistical, theoretical, and applied math skills. More importantly, we help you connect what you learn to realworld applications, so you can see firsthand how your knowledge can lead to professional success.
Our online Bachelor of Science in Mathematics is a comprehensive, flexible academic program with courses that you can complete from anywhere, on your time. Learn more about how our online Bachelor of Science in Mathematics program can help you achieve your goals.
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