Data Science Master's Degree

No. Elmhurst University welcomes applications from students who hold an undergraduate degree in any major from a regionally accredited institution.

Yes. A basic course in statistics (with a grade of C or better) and prior coursework in programming or equivalent professional experience are prerequisites for the program.

Our prerequisites may be completed with satisfactory completion of a short LinkedIn Learning course prior to starting classes with us. For example:

• Statistics Foundations 1, 2, and 3
• Programming Foundations: Fundamentals

Prerequisites also can be completed as part of the student’s undergraduate coursework or transferred from another college or university.

Learn more about admission requirements.

Yes. We offer a five-course graduate certificate in Data Science. Click on the link to learn more.

Fall A

• MDS 546 Methods of Exploratory Data Analysis
• MDS 575 Generative and Applied AI (for students competing MDSA core in one year)

Fall B

• MDS 523 Data Warehousing

January

• MDS 561 Data Governance and Project Management (for students completing MDSA core in one year)

Spring A

• MDS 534 Introduction to Predictive Modeling

Spring B

• MDS 560 Advanced Enterprise Analytics

Summer

• Elective (e.g. MDS 572 Time Series Forecasting)

Fall A

• MDS 575 Generative and Applied AI (for students completing the full two-year MDSA program)

Fall B

• Elective (e.g. MDS 535 Programming Environments for Modern Data)

January

• MDS 561 Data Governance and Project Management (for students completing the full two-year MDSA program)

Spring A

• Elective (e.g. MDS 564 Advanced Machine Learning Applications)

Spring B

• Final Elective (e.g. MDS 576 Data Science Capstone)

Unlock the power of organizational data with this foundational course in enterprise data management. Designed for future data architects and analysts, this course explores how modern businesses store, secure, and access data. Learn to compare relational, hierarchical, and network database systems, and master key concepts in distributed database design, physical storage, and AI-powered querying. Topics also include compression, encryption, and enterprise-level data security.

Prerequisites: None

Explore the core techniques of predictive modeling in this foundational course, designed for future data scientists, business analysts, and AI practitioners. You’ll learn how to transform raw data into valuable insights that support organizational decision-making. Key topics include data analytic thinking, decision trees, classification models, unsupervised machine learning, and text analytics. You’ll understand how predictive models intersect with business strategy to drive results, while gaining experience in applying these models in practical, real-world scenarios.

Prerequisites: None

Gain hands-on experience with the most in-demand programming tools and environments that power today’s data-driven and AI-enabled systems. This immersive course introduces you to languages such as Python and cloud services such as Databricks and Microsoft Azure, helping you build the foundational coding skills to manipulate, visualize, and analyze massive datasets. You’ll also explore modern programming best practices, open-source tools, and automated environments used by data professionals in real-world scenarios. Designed for learners looking to thrive in today’s fast-paced, data-rich industries, this course is a gateway into building flexible, scalable, and intelligent data solutions.

Prerequisites: MDS 523 and MDS 534

Master the art and science of turning data into insight with this hands-on course in exploratory data analysis (EDA). Learn how to prepare, visualize, and explore data using statistical techniques and visual storytelling tools like Tableau, Power BI, Python libraries, and interactive dashboards. Discover how to uncover hidden patterns, detect outliers, and make compelling data-driven arguments that lead to actionable business or research decisions.

Prerequisites: None

Elevate your data science skill set with this deep dive into advanced analytics and machine learning. This course is built for students aiming to apply AI-driven decision-making in complex business and scientific settings. Explore powerful techniques like feature engineering, logistic and multilinear regression, PCA, segmentation, clustering, neural networks, and genetic algorithms, taught through hands-on projects using Python and leading software tools.

Prerequisite: MDS 534

Step into the future of enterprise-level analytics with this advanced course on the integration of AI, automation, and data science within modern business ecosystems. You’ll explore cutting-edge platforms and tools such as NLP, ML Ops pipelines, and Robotic Process Automation (RPA) used to streamline data workflows and enable real-time decision-making. This course prepares you for strategic roles in enterprise analytics, IT consulting, and digital transformation.

Prerequisite: MDS 534

Learn how to lead data science projects from inception to strategic execution with a strong emphasis on data governance, compliance, and Agile project management. This course equips you with tools to manage ethical data use, implement FAIR (Findable, Accessible, Interoperable, Reusable) data standards, and oversee high-impact data initiatives. Through case studies and a final project presentation, you’ll gain real-world experience crafting persuasive, data-backed strategies for senior leadership.

Prerequisite: MDS 534

Delve into high-performance machine learning methods and AI-driven solutions used in today’s most advanced data environments. Topics include deep learning, natural language processing (NLP), computer vision, model tuning, and ML operations (ML Ops). The course follows the CRISP-DM framework and combines theory with hands-on coding in Python and commercial tools. Through real-world case studies, you’ll design and deploy models that solve complex problems across industries.

Prerequisite: MDS 534

Fuel your curiosity with this course on specialized topics designed to keep you on the frontier of data science. It’s a chance to explore niche, emerging, or career-specific areas of interest while gaining technical fluency in industry-relevant tools.

Prerequisite: MDS 534 or Program Director’s consent

Equip yourself with the forecasting skills used by financial analysts, operations managers, and data scientists to predict trends and drive strategy. This in-depth course covers ARIMA, exponential smoothing, dynamic models, and newer AI-enhanced forecasting methods. You’ll use open-source tools like R and Python to build and evaluate predictive models that track key business, economic, or environmental indicators. Whether you’re modeling sales, demand, or resource allocation, this course helps you deliver forecasts that matter.

Prerequisite: MDS 534

Step into the world of generative AI, where machines create content, code, art, and solutions that mimic human intelligence. This course offers a broad, practical introduction to tools like ChatGPT and other generative AI platforms. No technical background is required, just curiosity and a desire to innovate. You’ll explore use cases, ethics, prompt engineering, AI product design, and implementation strategies across fields such as marketing, education, healthcare, and business. Perfect for anyone eager to integrate AI into their professional toolkit.

Prerequisites: None

Demonstrate your mastery of data science by tackling a capstone project that brings together everything you’ve learned—from data wrangling and modeling to visualization and strategic storytelling. You’ll work on a real-world data challenge, applying advanced techniques in machine learning, statistical analysis, and business communication to produce a project that showcases your full range of capabilities. Designed to help you build a professional portfolio and prepare for high-impact roles in analytics and AI.

Prerequisite: MDS 564 or Program Director’s consent

Turn your ideas into impactful research. This first phase of the thesis track provides dedicated time for students to explore a data-driven problem under the mentorship of a faculty director. You’ll develop your research question, evaluate existing methodologies, and begin crafting the structure of your thesis project—all while building a portfolio piece that aligns with industry or academic career aspirations.

Prerequisite: Program Director’s consent

Bring your research to life with this final thesis course, where you’ll complete your applied or theoretical data science project. Students synthesize program-wide knowledge—from machine learning to data storytelling—to design, test, and present a solution to a real-world problem. The result is a professional-caliber capstone that showcases your expertise and sets the stage for post-graduate success.

Prerequisite: MDS 581