Online Master's in Data Science Curriculum

Data collection and interpretation is everywhere in today’s fast-moving world. Almost every industry benefits from the knowledge and expertise of data scientists.

If you want to make it as a data science professional, an advanced degree in the field can provide you with an array of useful skills that may prepare you for rewarding careers. When you complete a well-structured data science curriculum, you have the opportunity to build exposure to such areas as data mining, programming, coding, and analysis.

Maryville University Online MS in Data Science Curriculum

We collaborated with industry leaders in data science to design a graduate-level program that reflects the field’s most sought-after skills. That means you’ll complete a 36-credit-hour curriculum focused on everything from machine learning and mathematics to predictive modeling and programming. Your coursework is also project-based — which means you’ll complete hands-on projects that allow you to see the practical application of your studies.

Data Science Courses

24 credits required of DSCI 500+ level and 12 credits of DSCI, MATH, Actuarial Science, or Computer Science 500+ level or above.

Show / Hide Course Descriptions

DSCI 501	Math Modeling	3 Credits
The course focuses on business applications including finance, statistics, and mathematical modeling. It covers Microsoft Excel skills and Visual Basic Applications. A variety of real-life problems will provide the context for developing spreadsheet proficiency, including functions and formulas, statistical analysis, numerical solutions, optimization, and graphical output. This is a "hands-on" course that is supplemented by guest lecturers and various team projects.
DSCI 502	R Programming	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 you develop effective problem solving and collaboration skills. Note: This course is for graduate students only.
DSCI 503	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 and collaboration skills. Note: This course is for graduate students only. Related Courses: DSCI 303
DSCI 504	SQL	3 Credits
This course covers practical issues in relational database systems, such as creating databases, updating data, retrieving data, and saving data in databases. Project-based learning is used to help you develop effective problem solving and collaboration skills. Note: This course is for graduate students only.
DSCI 507	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. Note: This course is for graduate students only. Related Courses: DSCI 307
DSCI 508	Machine Learning	3 Credits
This course provides an introduction to machine learning. Topics include supervised learning, machine learning algorithms, learning theory, reinforcement learning and adaptive control, neural networks, and applications of machine learning to data mining, autonomous navigation, and web data processing. Related Courses:DSCI-408 Prerequisite:DSCI-503
DSCI 512	Predictive Modeling	3 Credits
This course introduces you 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 you develop effective problem solving and collaboration skills. Related Courses:DSCI-412 Prerequisite:DSCI-502
DSCI 613	NoSQL Database	3 Credits
This course covers no-relational database on a large scale. Topics include MongoDB, Cassandra, Redis, HBase and Neo4j. Project-based learnings are used to help students develop effective problem-solving skills and effective collaboration skills.
DSCI 614	Text Mining	3 Credits
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 and collaboration skills. Related Courses: DSCI-314 Prerequisite: DSCI-508
DSCI 617	Big Data Analytics	3 Credits
This course targets data scientists and data engineers. It covers programming with RDDs, tuning and debugging Spark applications, Spark SQL, Spark streaming, 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 and collaboration skills. Related Courses: DSCI-417 Prerequisite: DSCI-508

DSCI 618	Experimental Design	3 Credits
This course covers principles of experiments and basic statistics using SAS. 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. Note: This course is for graduate students only. Cross-listed: DSCI 318
DSCI 619	Deep Learning	3 Credits
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 and collaboration skills. Related Courses: DSCI-419 Prerequisite: DSCI-508
DSCI 598	Capstone Project	3 Credits
The Capstone Project course is for the students to apply the knowledge acquired during the Data Science program to a company project involving actual data in a realistic setting. Students will engage in the entire process of solving a real-world data science project, from collecting and processing actual data to applying suitable and appropriate analytic methods to the problem.
DSCI 624	Data Visualization	3 Credits
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 and R 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 report-quality graphs and presentations. Students will learn how to use tools like R Notebooks so that their analysis follows the reproducible research paradigm. Finally, students will learn to create simple web applications in R Shiny to build reporting and analytics dashboards. This course will be project based. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class. Related Courses: DSCI 324
DSCI 625	Blockchain	3 Credits
This course explores the fundamentals of the public, transparent, secure, immutable and distributed database called blockchain. Topics include applications to crypto currencies, such as keys, addresses, wallets, transactions, the blockchain mining and consensus, and network. Blockchains can be used to record and transfer any digital asset not just currency. Its potential impact on financial services, government, banking, contracting and identity management will also be discussed. This course will be project based. By the end of the class, the students will have a portfolio of analytical work completed inside and outside of class.
MATH 505	Statistical Modeling I	3 Credits
Credits: Three (3) This course focuses on model development, interpretation, understanding assumptions and evaluation of competing models. Topics include basics of statistical learning, generalized linear models, time series models, principal components analysis and decision trees. Note: This course is for graduate students only. Cross-listed: MATH 405
MATH 570	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). MATH 570 and MATH 571 (along with Calculus) cover all of the learning objectives contained in Examination P (Probability) of the Society of Actuaries. Note: This course is for graduate students only. Cross-listed: MATH 370
MATH 571	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. Related Courses: MATH 371 Prerequisite: MATH 570
MATH 572	Mathematical Statistics	3 Credits
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 tests and ANOVA; inferences for single samples; inference for two samples; inferences for proportion and count data; and advance estimation methods including Moment, percentile matching, Maximum Likelihood, Bayesian. This course emphasizes the applications of the theory to statistics and estimation. This is a calculus-based 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. Note: This course is for graduate students only. Cross-listed: MATH 372* Prerequisite:* MATH 571

To ensure the best possible educational experience for our students, we may update our curriculum to reflect emerging and changing employer and industry trends.

Ready for your next step?

Get Admission Requirements

What are some common skills and competencies taught in an M.S. in Data Science curriculum?

A typical master’s degree in data science can provide you with a comprehensive understanding of the ever-changing landscape of data collection and interpretation. Typically, you’ll have the chance to specialize within the degree program, tailoring your experience to individual career interests and goals.

Some of the skills gained through a data science curriculum may include:

Computer coding and programming. Several programming languages are particularly relevant for students pursuing data science careers. Master these programming languages, and you can interpret data, track trends, and make predictions, all valuable skills in a wide array of industries. An in-depth understanding of specific programming languages, such as Python, SQL, SAS, and R, is covered in the data science curriculum.
Data mining. With so much information available, it’s important for you to be able to decide which data is useful. Data mining skills allow professionals in the field of data science to extract and predict patterns, even in large data sets. When you hone your data mining skills, you can use this information to make predictions and recommendations to inform strategy and decisions for your business or organization.
Machine learning. Adaptability is key in today’s technological world. Companies often call on data scientists to train machines to understand data and take appropriate actions. Machine learning allows computer programs to adapt to new circumstances through experience, making processes more efficient.
Want a deeper look at machine learning? Maryville Online offers a professional certificate in machine learning that can help you begin to build a graduate-level understanding of this in-demand field in just five courses.
Experiential learning. The technological and data landscape is quickly and constantly evolving. When you study from a master’s-level data science curriculum, you can learn through doing, allowing you to adapt to a field where new advances emerge daily. By developing the dynamic ability to shift and grow based on current trends and hands-on experience, you can keep your skills relevant even as technology undergoes changes.
Statistics and mathematics. Develop a basic understanding of statistics and mathematics through your data science curriculum, and explore how these subjects apply to data science as a whole. Learn to use mathematical theorems and principles in your work, and you can build a professional advantage through your understanding of statistics and calculus.

What are some common master’s in data science courses?

An advanced data science curriculum can provide you with practical skills that prepare you for real-world success. The coursework itself combines core coursework with electives chosen based on areas of interest, allowing for a truly personalized degree. Some of these courses include:

SAS Programming. Focus on the development of programs that analyze data through a statistical model in SAS programming (also known as statistical analysis system programming). Learn skills that involve managing databases, interpreting raw data and trends, and updating database systems. When you complete an SAS programming course, you’ll have the chance to learn the ins and outs of this language, as well as discover practical applications in a variety of organizations.
Big Data Analysis. Every day, companies and organizations collect a massive amount of information. That data is then filtered, interpreted, and made accessible. While programming skills often provide the technical know-how, big data analysis can allow you to become a data science expert to parse information and present your trends and findings to key stakeholders. In a big data analysis course, you can learn strategies for examining data and using it to make informed, data-driven recommendations and decisions.
Want to learn more about big data? Maryville Online offers a professional certificate in big data designed to help you begin to build a graduate-level understanding of this in-demand field in just five courses.
Statistical Design. A firm grasp of statistics, mathematics, and design should give graduates with advanced data science degrees a strong foundation in the interpretation process. A deep understanding of statistical design allow you to make stronger predictions and suggestions based on the output of various databases and programs. For this reason, statistical design is an important component of an advanced data science curriculum.
Deep Learning. Deep learning is a form of machine learning that focuses on creating a digital neural network — much like our own brains — which can then interpret and learn from wide swaths of data. Skills in deep learning can help you devise machines that are able to learn and adapt quickly. Follow project-based assignments to gain an in-depth understanding of how neural networks apply to artificial intelligence, learn the applications of deep learning, and see how you can develop your own deep learning neural network.
Predictive Modeling. Test your problem-solving and communication skills as you apply statistical learning to contemporary business problems. Through the study of various models, such as linear and tree-based models, you can learn to design a predictive model and forecast outcomes.

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

If you’re excited by the possibilities presented through data science, consider an online Master of Science from Maryville University.

This advanced degree, earned entirely through an easy-to-navigate online learning platform, can equip you with invaluable tools in today’s digital economy. When you choose to pursue your master’s in data science, you can build the skills to seek employment in a variety of sectors and industries.

Take the next step toward a rewarding future and see what our online M.S. in Data Science can do for you.

Online M.S. in Data Science Curriculum

Maryville University Online MS in Data Science Curriculum

Ready for your next step?

What are some common skills and competencies taught in an M.S. in Data Science curriculum?

What are some common master’s in data science courses?

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