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We applied text data mining techniques from machine learning to position (job) descriptions posted on a university job search site, Bureau of Labor Statistics (BLS) standard U.S. job descriptions, course descriptions, and curricula descriptions. Our work compared Term Frequency-Inverse Document Frequency (TD-IDF) to Latent Semantic Indexing (LSI) and found that TD-IDF was preferred in this application. We used TD-IDF to measure the extent of coherence among the collections of our documents. We then leveraged those measurements to developed novel approaches to assist students and curricula designers in answering these questions: (1) for students, given an interest in specific jobs, which degrees and courses are most relevant; (2) for students, given courses that have been taken, which jobs are most likely to result in initial interviews; (3) for curricula designers, how aligned are degree programs with specific groups of jobs (for example, with STEM jobs); (4) for TM curricula designers, to what extent do current and proposed degrees address different job opportunities. Other similar applications are possible by composing our Python and JMP code. Our work could be extended by providing open source implementation of the algorithms. [ABSTRACT FROM AUTHOR]