One unit of credit equals four semester hours.
MDS 523 Data Warehousing
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.
MDS 534 Data Mining and Business Intelligence
Business intelligence represents a conceptual framework for decision support. It combines analytics, data warehouses, applications and methodologies to facilitate the transformation of data into meaningful and functional information. The major objective of business intelligence is to enhance the decision-making process at all levels of management. Data mining is a process that utilizes statistical analysis, probability theory, mathematical modeling, artificial intelligence and machine learning techniques to extract useful information and subsequent knowledge from large data repositories, commonly referred to as “big data.” This course examines a number of emerging methods proven to be of value in recognizing patterns and making predictions from an applications perspective. Students will be provided the opportunity for hands-on experimentation using software and case studies.
MDS 535 Programming Languages and Environments
This course covers the application of appropriate high-level programming languages for expressing software design patterns used for extracting and processing big data. These high-level languages include Hadoop, Python and R, along with the associated libraries and language pragmatics for framework and patterns (e.g. map-reduce) relevant to processing massive amounts of data. Query languages, spreadsheet macro languages and web-client scripting languages are also studied in the context of data mining.
MDS 546 Quantitative Methods
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, statistics and mathematical modeling such as probability distributions, cumulative probability distributions, descriptive statistics, hypothesis testing, correlation analysis, linear regression, multivariate regression and mathematical model design. The course then proceeds to examine modern tools for conducting analyses using these quantitative methods on both small-scale and large-scale datasets. Case studies from a variety of settings are used to develop students’ abilities to successfully apply the techniques learned in this course to practical circumstances that often, because of the ambiguities involved, present limitations to the power of these mathematical tools. Topics from this course also provide the foundation for some subjects covered in the analytical methods course and the data mining and business intelligence course.
MDS 549 Data Mining Project
Each student completes a project incorporating the practical application of several of the program’s data mining techniques to one or more data sets chosen by the student or provided by the instructor. In addition to the correct use of the techniques and interpretation of the results, emphasis is placed on the student’s ability to gauge the resultant impact on the organization’s business intelligence processes and procedures. Prior to the submission of the final project, students submit a proposal describing the application and the data mining tools to be utilized.
MDS 556 Analytical Methods
This course builds upon the foundation established in the quantitative methods course to develop the advanced analytical methods required for in-depth applications of data science. Topics covered include advanced techniques in statistics and mathematical modeling such as exploratory data analysis, logistic regression and stochastic models; modern techniques for network analysis such as measures of network centrality, hierarchical and other clustering techniques, and models of network growth; and special topics drawn from subjects such as graph theory, game theory and linear algebra. Techniques for visual presentation of data analysis will also be covered. Course topics will be introduced from both a theoretical framework and through the use of case studies in applied settings.
MDS 560 Business Intelligence for Enterprise Value (for Graduate Certificate in Entperise Optimization program)
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 covered include: 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 including Tableau, SAP Predictive Analytics, and IBM Watson. Students will conduct the steps of an analytical workflow on a selected business process and create a project plan to drive value.
MDS 564 Advanced Data Mining and Analytics
This course emphasizes the application of the primary topics covered in MDS 534 Data Mining and Business Intelligence and MDS 556 Analytical Methods within large case studies while learning to choose the appropriate programming language(s), software design pattern(s), and/or software tools, which are covered in CS 535 Programming Models and Environments. In these case studies students utilize data mining tools where appropriate and utilize advanced techniques in statistics and mathematical modeling for supporting conclusions and decisions. Students utilize software tools to visually present conclusions and decisions. Case studies are chosen from a wide spectrum of problem domains.
MDS 576 Research Methods in Data Science
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.
Students must complete two graduate-level electives from Elmhurst College.
Elmhurst College reserves the right to modify courses, schedules and program format without advance notice to students.