Course offerings reflect the 2023-2024. One unit of credit equals four semester hours.

Topics include an integrated and detailed comparison of relational, hierarchical and network database systems. Database design and physical storage requirements, including distributed database design and related management issues, are discussed. High-level query languages using artificial intelligence techniques are reviewed along with other topics such as database compression, encryption and security.

Prerequisite: none.

In this course, systematic approaches for extracting useful information from data to enhance organizational decision-making processes are introduced. Topics include fundamentals of data mining and machine learning such as clustering, association rules, and basic predictive models. Strategies for generating business intelligence and creating business value from these techniques are explored, grounded in the CRISP data science protocol. Students will have opportunities for hands-on experimentation using software and case studies. This course provides a foundation for many other courses offered in the data science program.

Prerequisite: MDS 546 or Program Director’s consent.

This course covers the application of appropriate high-level programming languages for extracting and processing modern forms of data, such as data at large scale, within the Hadoop and Spark environments. These high-level languages include imperative, object-oriented languages such as PIG, HIVE and Scala. The languages will be presented in support of largescale data processing relying on the map-reduce paradigm. Additional libraries that support data mining and machine learning will be explored.

Prerequisites: MDS 523 and MDS 534.

The ability to move data along the continuum from information to insight to action requires a strong foundation of skills in various quantitative methods. This course begins with a systematic and integrated overview of concepts from probability theory and statistics such as probability distributions, descriptive statistics, hypothesis testing, correlation analysis, linear regression, multivariate regression and analysis of variance. Students are introduced to modern software tools for conducting analyses using these quantitative methods. Case studies from a variety of settings are used to illustrate ways these topics lead to modern practice in data science. Topics from this course provide a foundation for subjects covered in subsequent MDS courses.

Prerequisite: Prior knowledge of basic statistics.

This course builds upon the foundation established in earlier courses to develop the advanced analytical methods required for in-depth applications of data science in general and machine learning in particular. Topics covered include feature engineering, variations of multilinear and logistic regression, principal component analysis, advanced approaches to clustering and segmentation, classification modeling algorithms, and biological methods such as neural networks and genetic algorithms. Course topics will be introduced from both a conceptual perspective and through use of case studies in applied settings. Students implement concepts from the course using open source software such as Python as well as a variety of commercial data science software packages.

Prerequisite: MDS 534.

This course provides a business-oriented framework for the data scientist to identify, prioritize and perform data analytical projects that drive business value and enhance competitive advantage. The course examines the data ecosystem, both external and internal to the enterprise, as well as business processes and networks upon which analytical projects can be used to reduce organizational risk and drive the creation of economic value. Topics emphasize the application of machine learning to marketing, sales and customer data exploration, supply chain data exploration, operations data exploration, financial data exploration and project management methods to convert information gain into business value. A variety of business process data sets will be examined using analytical tools. Students will conduct the steps of an analytical workflow on a selected business process and create a project plan to drive value.

Prerequisite: MDS 534 or Program Director consent.

Building upon knowledge gained in MDS 560, students in this course learn to apply Enterprise Project Management concepts to the development and deployment of data science projects designed to create enterprise value. Students execute and manage the development and deployment of a data science solution into an organization’s business processes. Special emphasis is placed on the production of both project management and data science deliverables using an iterative Agile development process. The course also covers alternative methods to deploy data science models, achieving business process transformation and attaining competitive advantage through information gain. The course is highly interactive as students complete project management assignments working individually and in teams using collaboration software.

Prerequisite: MDS 560.

In this course the student learns to apply the concepts of robotics, reinforcement learning and automation to transform and improve the competitiveness of the enterprise. Students learn the fundamental concepts of robotic control and receive hands-on experience using a reinforcement learning system as applied to both projects and continuous business processes. The course emphasizes the analysis of case studies focusing on the execution of the project life cycle to digitalize and automate business processes.

Prerequisite: MDS 560.

This course explores advanced and emerging applications of machine learning and broader data science methods. Topics include advanced feature engineering, model hyperparameter tuning, text analytics, natural language processing, image analytics, and deep learning. Technical topics are considered in the larger CRISP data science framework, with its emphasis on creating value in an applied setting. Case studies from a variety of problem domains are used to illustrate concepts and their applications. Students implement concepts from the course using open source software such as Python as well as a variety of commercial data science software packages.

Prerequisite: MDS 556.

This course provides opportunities for students to study specialized topics in data science. Examples include time series forecasting using specialized libraries in the R programming language and data science applications of linear algebra.

Prerequisite: MDS 534 or Program Director’s consent.

This course serves as the capstone for the program. Students will apply concepts learned and skills developed in the other required courses to complete a major project that demonstrates the full range of their data science knowledge and capabilities.

Prerequisite: MDS 564 or Program Director’s consent.

This course, offered as an independent study under the guidance of a thesis director, is the first of two courses required to complete a thesis option in the data science master’s program. This course fulfills one of the two MDS elective requirements.

Prerequisite: Program Director’s consent.

This course, offered as an independent study under the guidance of a thesis director, is the second of two courses required to complete a thesis option in the data science master’s program. This course replaces MDS 576 as the program capstone.

Prerequisite: MDS 581.


Students must complete two graduate-level electives at Elmhurst.

Elmhurst University reserves the right to modify courses, schedules and program format without advance notice to students.

James Kulich, Ph.D.

Professor, Department Chair of Computer Science & Information Systems; Program Director, M.S. in Data Science and Analytics
Department of Computer Science and Information Systems

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