Graduate Study who complete the five courses for credit will receive the graduate certificate in data science, or they may complete an additional five courses to earn a master’s degree in data science.
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.
Course Requirements MDS 523 Data Warehousing MDS 534 Data Mining and Business Intelligence MDS 546 Quantitative Methods MDS 549 Data Mining Project One elective from any graduate program at Elmhurst If a student chooses to complete the master’s in data science, additional coursework will include MDS 535, MDS 556, MDS 564, MDS 576 and one additional graduate elective at Elmhurst.
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.
Course Offerings One unit of credit equals four semester hours. 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 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
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
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