QUEST Rolls out New 3-Credit Data Analytics Elective

As a program that is constantly evolving and improving, the QUEST Honors Program at the University of Maryland has distinguished itself through its unique, rigorous, and practical classes.

QUEST students are required to take two elective classes from a constantly evolving list, pre-approved by the Curriculum Review Committee. Regarding QUEST electives, Kylie King, Program Director of QUEST, says: “We try to stay on top of current trends in education and the business world as a whole and adjust to make sure our students have the skills they need.”

Thus, in the Fall of 2015, after acquiring feedback from students, alumni, and faculty, the Quality Guild realized that QUEST students’ abilities to analyze quantitative data were not where they should be and therefore, decided to pilot a new 1-credit course: Applied Quantitative Analysis. Beginning this Fall 2016, the course will be offered as BMGT438A/ENES 478A: Applied Quantitative Analysis, a more comprehensive and extensive 3-credit class that counts as a QUEST elective.

Clair Devaney, Q25, took the class when it was one credit and definitely thinks expanding the course makes it more appealing and beneficial. “Definitely only reached the tip of the iceberg,” she says, “but it was a great way to expose oneself and inspire a deeper dive into data analytics in the future.

So what’s the change from 1 credit to 3 credits? Now, students will not only expand their knowledge base and be able to get more in-depth, but they will also perform an in-depth project with a QUEST corporate partner.

Possible topics and learning objectives outlined in the syllabus thus far include:

  • Collect appropriate data (considering research questions, type of data, etc.).
  • Describe data collection process (consider assumptions of sampling and sample design).
  • Accurately describe data
  • Clean data for analysis
  • Evaluate alternative analysis methods
  • Select and justify the most appropriate method(s) for analysis
  • Consider assumptions of selected method
  • Draw valid conclusions based on analysis
  • Apply conclusions to problem at-hand and provide recommendations

Kylie King, who is also the instructor for the course, tells interested students: “We are using a flipped-classroom model so be sure you are ready to watch videos and read a little bit before class. In class, we will use R to analyze data. I try to break this down so that it is easy to follow and not intimidating.”

 

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