Skip to content

Online Master of Data Science: Curriculum

Curriculum Details

35 TOTAL CREDITS REQUIRED

The online Master of Data Science degree from Calvin University applies a Christian perspective to the technical skills needed in today’s business world. The program requires the completion of up to fourteen total courses and is offered both full-time and part-time to fit your personal schedule. Courses are taught by data science experts in a fully online classroom and deliver the expertise to critique data-powered technology for human flourishing.

Required Courses (29 credits)

An introduction to the principles and practices of effective data visualization as an essential skill for learning from data and for communicating with others, both within and outside of an organization. Students will combine principles from statistics, psychology and computer science to design and create visualizations and use them to communicate with and about data to stakeholders and others. Prerequisites: Intro Programming. Upon completion of the course, students will be able to create, interpret, critique, and refine visualizations of data.
INFO 603 maybe be taken as a replacement course.

An introduction to ethical frameworks and their applications in the context of data science. This course will help students reflect on the moral dimensions of the practice of data science in relation to what we believe, what we do, and what sorts of people we want to be. Ethical issues such as fairness, privacy, justice, transparency, and why we should try to be morally good will be considered both theoretically and practically (by applying them to issues that arise in the practice of data science and in contemporary social life more generally). Students will reflect on both historical and contemporary sources and consider what difference Christian faith makes to the theory and the practice of morality.

Upon completion of the course, students will be able to:

  • Describe key features of the major ethical frameworks (deontology, consequentialism, and virtue ethics).
  • Use ethical frameworks to describe and evaluate moral decision-making in data science.
  • Identify key risks involved in the practice of data science and methods to reduce those risks.
  • Reflect on ethical guidelines produced by one or more of the professional organizations related to data science (e.g., Association of Computing Machines, American Statistical Association).

An introduction to collecting, managing, and processing large data sets. This course focuses on the core skills and concepts needed to pull data from a range of sources both inside and outside of organizations; to filter, transform, and combine data sets in preparation for data cleaning and analysis; and to construct quantitative summaries and basic visualizations. Prerequisites: Intro Programming.

Upon completion of the course, students will be able to:

  • Perform basic and advanced data management tasks.
  • Create basic numerical and graphical summaries of data.

INFO 601 maybe be taken as a replacement course.

Organizations leverage increasingly large collections of data about people, products, and processes. Predictive analytics provides tools to discover patterns and anticipate trends in such data. This course introduces the foundational principles of predictive analytics and provides a survey of a wide range of predictive methods, both battle-tested classics and emerging high-capacity deep learning models. Prerequisites: Intro Programming.

Upon completion of the course, students will be able to:

  • Perform basic and advanced data management tasks.
  • Create, interpret, critique, and refine visualizations of data.
  • Select predictive analytics methods appropriate to the data and task from among the courses surveyed in the course.
  • Interpret the outcomes of predictive analytics techniques surveyed in the course.

INFO 602 maybe be taken as a replacement course.

An introduction to the use and implementation of neural network (deep learning) models. Students will select machine learning algorithms appropriate to a given task. Then they will train, tune, and assess them using standard software packages, and assess the suitability of a machine learning model for a given task. Applications may include text classification, image classification, and price prediction. Prerequisites: DATA 572 or equivalent background in supervised machine learning.

Upon completion of the course, students will be able to:

  • Select and implement machine learning algorithms using standard software packages.
  • Use stochastic gradient descent with backpropagation to train a neural network.
  • Assess the suitability of a machine learning model for a given task.

This course takes a practical approach to the fitting, assessment, interpretation, and presentation of statistical models, introducing students to a broad range of applied regression methods via case studies with real datasets. Topics include data preparation and visualization, the generalized linear model framework, and specific examples of frequently used models (such as multiple regression, regression for count and binary data, generalized additive models, and models to account for hierarchical structure or dependence). Prerequisites: Intro Statistics.

Upon completion of the course, students will be able to:

  • Obtain data and prepare it for statistical analysis via regression models.
  • Select an appropriate regression model to answer a variety of types of questions.
  • Use statistical software to fit regression models, drawing conclusions in context from the output.
  • Assess the quality of a model in terms of goodness of fit to the data, reasonableness of underlying model assumptions, and usefulness to answer the primary questions of interest.
  • Communicate results of data analysis clearly and accurately, using the skills above.

An introduction to matrix algebra methods useful in data science and their implementation in software. Topics include representing data using matrices and higher dimensional arrays, transforming data in these formats, matrix decompositions, and applications of linear algebra in data science.

Upon completion of the course, students will be able to:

  • Create and transform matrices and arrays in software.
  • Construct and use matrix representations of data.
  • Describe and use appropriate matrix decompositions to learn from data.
  • Use linear algebra to support statistical and/or machine learning algorithms.

This course introduces students to software design concepts and practices commonly used to develop, deploy, and maintain production-quality data science applications. Topics include project management, software and data design, configuration management, and system deployment. Concepts, techniques, and tools are integrated within a term-long, team project. Prerequisites: Intro Programming.

Upon completion of the course, students will be able to:

  • Explain and use software engineering techniques for data science projects, including software development, management, and deployment techniques.
  • Explain and use data engineering techniques, including both relational and non-relational database systems, and data workflow management tools.
  • Explain and use software configuration management tools in the context of a remote, asynchronous team setting.
  • Participate effectively in a team-oriented, data science project.

Healthy and successful organizations are structured in alignment with their strategy, have strong, values-based cultures, and are able to adapt to change. This course will enhance an understanding of life inside organizations through an interdisciplinary examination of common organizational practices such as organizational structure, culture differentiation and internal integration, culture typologies, as well as leadership attributes and styles. The course is designed to increase effectiveness in dealing with multiple aspects of organizational change—by understanding conditions that may require it, increasing awareness of multiple ways that organizations change, managing change, receiving and participating in it, and understanding approaches and responses to change.

This is the first course in a two-course sequence in which students will complete a masters-level data science project. This project experience will give students the opportunity to apply concepts and techniques learned in several masters courses by developing a significant data application. The work will include necessary library research, design and prototyping, implementation, analysis, and deployment. The student will submit regular progress reports to a supervising faculty member and submit a final report (in DATA 698) on the project’s status for evaluation. Prerequisites: DATA 545, DATA 675, at least 20 hours in the MDS program.

Upon completion of the course, students will be able to:

  • Complete the design, implementation, analysis, and testing of a significant data project.
  • Manage the conduct and progress of this project.
  • Communicate technically through written reports and oral presentations.

A continuation of DATA 696. The student will submit regular progress reports to a supervising faculty member and submit a final report on the project’s status for evaluation. Prerequisites: DATA 696.

Upon completion of the course, students will be able to:

  • Complete the design, implementation, analysis, and testing of a significant data project.
  • Manage the conduct and completion of this project.
  • Communicate technically through written reports and oral presentations.
  • Explain the role of data science in their chosen vocation.

Electives (≥6 credits)

An introduction to the support of organizational information technology cloud computing and services. This course covers cloud computing platforms, services, and their use in information technology administration using both open source and commercial platforms. Students will demonstrate their skills through system implementation labs and team projects. Prerequisites: TBD.

Upon completion of the course, students will be able to:

  • Evaluate and implement cloud computing technologies for service hosting.
  • Operate Microsoft’s Active Directory environment with Group Policy.
  • Write Powershell scripts to configure and maintain PCs, servers, and services.
  • Configure and maintain end-user services through containerization technologies.
  • Deploy common information technology services via various cloud platforms.

An introduction to the principles of causal inference and statistical design. Students will learn principles that can guide the collection of data and the creation and interpretation of models. Prerequisites: DATA 545.

Upon completion of the course, students will be able to:

  • Create plausible causal diagrams representing possible causal relationships among variables.
  • Use causal diagrams to decide which variables to include or exclude from a model, given a particular question of interest.
  • Estimate causal affects given a causal diagram, a model, and data.
  • Explain how and why randomization can be used as a feature of study design.

An introduction to optimization from a statistical perspective, with emphasis on computation and problem-solving. Computer software will be used to explore practical examples. Prerequisites: DATA 555.

Upon completion of the course, students will be able to:

  • Explain how standard optimization algorithms (e.g., gradient descent, simulated annealing) work and what situations may lead them to fail.
  • Implement optimization algorithms optimization algorithms in a high level programming language.
  • Select and tune optimization algorithms within statistical and machine learning software.
  • Diagnose and treat problems with optimization algorithms.

An introduction to simulation for statistics and data science, with emphasis on computation and practical examples. Computer software will be used to explore practical applications. Prerequisites: Intro Programming, DATA 545 or 502.

Upon completion of the course, students will be able to:

  • Simulate random samples from discrete and continuous random variables.
  • Use parametric and non-parametric bootstrap to estimate the uncertainty of derived quantities.
  • Implement Monte Carlo and MCMC algorithms for several kinds of problems.

Advanced studies in machine learning. Topics include supervised and unsupervised learning, with applications to natural language processing and generation of text and images. Students will additionally study the social, ethical, and religious issues that arise in automated processing and production of language and images. Prerequisites: DATA 676.

Upon completion of the course, students will be able to:

  • Map real-world text analysis and generation problems into a form that can be solved using deep learning models.
  • Implement basic data manipulation operations in language processing.
  • Assess language models using appropriate metrics.
  • Discuss the social, ethical, and religious issues that arise in automated processing and production of language and images.

An introduction to the structures necessary to implement a database management system. Topics include data models (including hierarchical, network, and relational data models), normal forms for data relations, data description languages, and query facilities. An introduction to existing database management systems is given. Prerequisites: Intro Programming.

Upon completion of the course, students will be able to:

  • Explain, compare and contrast the following models and techniques and use them to design and implement a relational database: entity-relationship modeling, relational modeling, functional dependencies, and normalization.
  • Design and implement a relational query using the following techniques: relational algebra and calculus, SQL, and optimizing design and queries.
  • Design and implement a database application using the following tools and techniques: stored procedures, database APIs, transaction processing, object-relational mapping with MS Entity Framework.
  • Explain, compare and contrast relational and post-relational systems.
  • Design and implement a non-relational database application.

This course focuses on geographic information systems (GIS) and the art and science of mapping for spatial analysis. Map-design techniques and visual communication using GIS vector and raster data forms will be explored, as well as a variety of methods for analyzing spatial relationships. Topics include those of the physical world and landscape, social justice, poverty, and a significant end-of-semester project. This course has a lecture and lab component, and lab work will give practical experience to students using the ArcGIS suite. Students will complete a GIS project tailored to their disciplinary interest and also explore religious faith in professional GIS life.

This course introduces advanced themes in Geographic Information Systems including spatial database design, spatial algorithms, implementation and design, and advanced GIS applications including designs for community development and service tailored to individual students’ industrial application.

Request More Information

Thank you for your interest in Calvin University. We are here to answer all of your questions. Submit this form and an admissions counselor will get in touch with you as soon as possible.