Applying for the MS in Data Science and Analytics

While all of the information about applying is available on many of the University of Charleston (College of Charleston) websites, we wanted to pull together a summary of many of the components students need to handle when applying.

Overall Information

The first application deadline is in February each year with the first courses being available to students in Summer Session II (July). Once you apply and your application is processed, you will be notified of the outcome with additional information on studying and taking the entrance exam. More information on the full admission requirement can be found here.

Preparing for the Program

Our program is designed to accept students from diverse academic backgrounds (ranging from traditional liberal arts to STEM disciplines). The best way to prepare for the entrance exam and the program is to study core programming, statistics, and general quantitative skills.

FAQ

  1. How can I get more information about the MS in Data Science and Analytics?

    The best way to get more information is to fill out an inquiry form at https://ucsccofc.tfaforms.net/217719.

  2. Where do I got to apply for the program?

    Please visit and submit your application to the Graduate School at the University of Charleston (College of Charleston): http://gradschool.cofc.edu/admission-information/

  3. Am I a good fit for the program?

    We welcome applicants from all majors. Qualified applicants will have some background and aptitude in either computing and/or mathematics (statistics). Data Science is different from these two fields, but these fields provide a lot of the foundational skills necessary.

  4. Are there assistantship opportunities available?

    Yes. All applicants and current students may apply for a limited number of research assistant positions. The deadlines for these positions are the same as the application deadline for the program. In order to be considered for an assistantship, students/applicants must complete an essay as well as all other application materials by the posted deadlines. Link to assistantship application: https://goo.gl/forms/nz65L5twmPDtlzGu1. The official policy guiding the selection process can be found at https://igards.github.io/education/masters_data_science_analytics/policies/graduate_assistantships/.

  5. What recommendations do you have to gain these initial skills needed to enter the program?

    There are a number of great free introductory online courses that can be recommended. In no particular order and not meant to be exhaustive:

    https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/

    https://www.edx.org/course/introduction-to-programming-using-python

    https://ocw.mit.edu/courses/mathematics/18-05-introduction-to-probability-and-statistics-spring-2014/

  6. Is the test administered through the University of Charleston, or is it a test I would take outside and have my scores sent in?

    The test is administered by the University of Charleston after your application has been official submitted and reviewed. Flexible timing and online options are available.

  7. Could I learn a little more about how the coursework is set up? Do we learn skills/concepts that we practice by applying to our own area of interest, or is it set up in a more structured format?

    The formal coursework is centered on machine learning, artificial intelligence, statistical learning, data organization and management, distributed computing, and an area of interest defined by selected pre-approved electives. There exists a large amount of freedom in how students select their electives and their practicum. The core pillars of data science are represented by the required courses.

  8. What is the practicum and is it required?

    The practicum provides students with the opportunity to gain real world experience working with our industry partners. It is a required component of the curriculum, where teams of students work with a practicum company to identify, define, scope, and analyze a relevant data science and analytics problem. All groups are additionally supported and supervised by MS in Data Science and Analytics faculty. Following an initial hypothesis, students typically engage in data acquisition, exploratory data analysis, feature extraction, model development and evaluation, as well as oral and written communication of results. Class schedules are set so that students can work onsite one to two days per week. Students devote 15 hours a week to practicum on average. Projects may be paid or unpaid.

    All students are required to complete the practicum or the thesis option.

  9. I am a potential employer and I would like more information on how I can recruit graduates into my business. Where can I get more information?

    More information can be found at https://igards.github.io/industry/

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