Maryville University Online Bachelor’s in Data Science Curriculum

In the modern business landscape, data drives strategy and informs almost every decision — from healthcare to marketing, from government to finance. And there’s more data being produced than ever.

Increasingly, organizations rely on data science experts to analyze this information and use it to make better decisions. By enrolling in a data science bachelor degree online, your data science curriculum can help you build the in-demand skills and competencies to transform data into formats that future employers value.

With an in-depth understanding of how to organize and interpret data, you can enter a quickly growing, potentially lucrative field.

Maryville University Online BS in Data Science Curriculum

Maryville’s online Bachelor of Science in Data Science comprises 128 credit hours and includes coursework in general education, the data science major, general electives, and an optional concentration in actuarial science. With our online data science degree, you’ll do more than explore statistical theory, mathematical methods, and the latest data technologies. You’ll complete hands-on projects that allow you to see the practical application of your studies.

Data Science Courses

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DSCI 200	Foundations of Data Science	3 Credits
Prerequisite: MATH-117. The course develops the core concepts and skills in statistical inference and computational techniques through working on real-world data. The course is to introduce the foundation of data science to entry-level students who have not previously taken statistics or computer science courses.
DSCI 201	Math Modeling – Excel	3 Credits
Prerequisite: MATH-117. Students receive basic training in Microsoft Excel. A variety of real-life 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.
MATH 251	Calculus III	3 Credits
Prerequisite: MATH-152. This is the third course of the calculus sequence. Topics include vector-valued 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.
DSCI 302	Introduction to R	3 Credits
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.
DSCI 303	Introduction to Python	3 Credits
This course covers data types, statements, expressions, control flow, top Python core libraries (NumPy, SciPy, Pandas, Matplotlib and Seaborn) and modeling libraries (Statsmodels and Scikit-learn). Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. Cross-Listed: DSCI-503
DSCI 304	Introduction to SQL	3 Credits
This course is for students who want to enhance their SQL skills through exploring real-world examples. Topics covered include but are not limited to pattern-matching 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 real-world problems Cross-listed: DSCI-504
DSCI 307	SAS Programming	3 Credits
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 Chi-square tests, T-Tests, ANOVA, and regression. Project based learning is used to help students develop effective problem solving skills and effective collaboration skills. Cross-Listed: DSCI-507
MATH 316	Applied Linear Algebra	3 Credits
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.

MATH 370	Probability I	3 Credits
This is the first in a sequence of two one-semester 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.
MATH 371	Probability II	3 Credits
This is the second in a sequence of two one-semester 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.
MATH 372	Mathematical Statistics	3 Credits
This is the second in a sequence of two one-semester 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.
DSCI 318	Experimental Design	3 Credits
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.
DSCI 408	Machine Learning	3 Credits
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, k-means clustering, error calculation, optimization training, tree-based 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). Cross-Listed: DSCI-508
DSCI 412	Predictive Modeling	3 Credits
This course introduces students to fundamental statistical learning techniques that can be applied to real-world business problems. Topics include generalized linear models, tree-based 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. Cross-listed: DSCI-512
DSCI 417	Big Data Analytics	3 Credits
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.

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 part-time pace. Please speak to your advisor for more details.

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What skills and competencies are taught in data science courses?

Most bachelor’s programs in data science equip students with foundational skills they can apply in a wide variety of fields. Whether you want to pursue a career in business, social science, engineering, computer science, or any number of industries, earning your data science bachelor degree online can help you learn the skills to contribute and lead in your organization.

Bachelor’s in data science programs cover topics like:

Data analysis. When presented with raw information, professionals trained in data science will know how to transform it into valuable insights that benefit businesses and organizations. Through these insights, data scientists can lead their teams toward efficiency improvements, increases in profit margins, quality advancements, enhancements in customer experience, and much more.
Programming. Big data requires advanced tools. For this reason, your data science curriculum will typically help you become proficient in several programming languages used to compile and synthesize data. Common programming languages of focus include Python, SQL, and R. These languages have many practical applications in computer science, and you can leverage them to advance your career across a variety of fields.
Predictive modeling. Throughout your data science bachelor’s program, you also can expect learn about the principles of predictive modeling. The skills you build in these courses can help you extrapolate information to discover insights and create intelligent forecasts.
Computer science, statistics, and economics. Data science is a unique field with application across disciplines. It stands at the intersection of business, statistics, artificial intelligence, machine learning, and more. So it’s paramount that data science courses prepare graduates with a breadth of knowledge in computer science, statistics, and economics.

What data science courses can I expect to take at the bachelor’s level?

The curriculum for data science degrees varies from university to university, but programs tend to feature a heavy focus on topics like programming, modeling, mathematics, and computer science. Here are some of the courses you can expect to take in your data science curriculum:

Data Science. Develop your foundation in the core concepts of data science by manipulating real-world data and models. Enhance your skills in areas such as statistical inference and computational techniques. Then, apply these skills in your advanced coursework or your career.
Probability. In your probability courses, you’ll have the chance to study important mathematical concepts like conditional probability, percentiles, distributions, and a number of applicable theorems. Coursework in probability typically builds on knowledge gained in required mathematics classes.
Experimental Design. Develop your comprehensive understanding of experimental design, an important skill for all scientific fields. You’ll have the opportunity to explore topics like types of designs, sample size, model comparison, and survey data analysis. This course also features an experiential element that incorporates project-based learning, so you can enhance your problem-solving and collaboration skills.
Predictive Modeling. Learn to apply your knowledge to real-world business problems through predictive modeling. Topics you’ll have the opportunity to study in this course include types of models and principal components analysis. Discover how to build predictive models and effectively communicate your findings.
Mathematics and Modeling. A comprehensive understanding of data science requires a strong foundation in mathematics. In this course, you’ll have the chance to build your skills in math modeling by using database programs such as Microsoft Excel, advanced calculus, and applied linear algebra. Learn how to leverage databases for graphical output, and discover applications for partial derivatives and multiple integrals. Plus, you can study linear algebra topics such as vector spaces and subspaces, matrix algebra, and linear transformations.
Statistics. If you want to excel as a data scientist, you’ll need to be proficient in mathematical statistics and statistical modeling concepts. In mathematical statistics courses, instructors convey the concepts of inference, statistics, and the primary methods of estimation — and how data scientists can apply each in real-world settings. In this course, you can discover how to develop and interpret statistical models, as well as recognize and understand assumptions. Topics covered include hypothesis and model testing, estimation, data forecasting, and confidence intervals.
Machine Learning. At Maryville, our undergraduate data science curriculum includes this course covering the hot topic of machine learning. Here, you can discover how to develop smart algorithms that can automate complex processes by analyzing large volumes of data. Topics include regression splines, generalized additive models, tree-based models, support vector machines, principal components analysis, and clustering methods. And project-based learning can help you develop effective problem-solving skills and become a better collaborator in your organization.

Learn more about the B.S. in Data Science curriculum.

Are you interested in expanding your technical expertise in the fast-growing field of data science? Start here. At Maryville, we designed our online B.S. in Data Science to help you build your understanding of data analysis, programming, predictive modeling, and other skills.

When you earn your online data science degree, you can expand your digital and analytical technical expertise. Discover how our data science bachelor degree online can help prepare you for the process of pursuing a career in data science.