M.S. in Data Science

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M.S. in Data Science

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Course Offerings

MDS 523 Data Warehousing
Topics include an integrated and detailed comparison of relational, hierarchical, and network data base systems. Database design and physical storage requirements, including distributed data-base 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 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 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 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 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 535 Programming Language and Environment
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 imperative, object-oriented languages, such as C++, Java, Python, MatLab, 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 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 564 Advanced Data Mining and Analytics
This course emphasizes the application of the primary topics covered in MDS 546 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
The course will have the student apply concepts learned in the other required courses for the master’s so as to demonstrate how information is ‘mined’ using techniques that employ analytical concepts, probability theory, statistics, mathematical modeling and various programming skills.

Electives
Students must complete two graduate-level electives and may choose courses from any graduate program at Elmhurst.

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