Master of Science in Applied Data Science

Master of Science in Applied Data Science

New College offers a Master’s Degree in Applied Data Science, under the Classification of Instructional Programs (CIP) code 11.0104. The degree requires 36 credit hours of graduate work. Students complete 11 courses at 3 credit hours each, one practicum at 3 credit hours, and one non-credit earning course (the Industry Workshops).

The Applied Data Science Master's Program at New College of Florida is a cohort-based program that emphasizes project-based learning, often carried out in small teams. Alongside collaborative work, the curriculum places strong emphasis on mathematical rigor and computational mastery across all stages of the data lifecycle, including management, visualization, analysis, interpretation and communication of data. All graduate students are required to enroll full-time beginning in the fall semester. There are no part-time or spring enrollment options for the degree program. However, students who need to complete prerequisite coursework—such as Introduction to Computer Programming or Linear Algebra—may be provisionally admitted in the spring or fall semester. These students can complete the necessary courses at New College before transitioning into graduate coursework.

Admissions Factors

The following admission factors will be considered for applicants to the Master of Science in Applied Data Science program:

1. Graduate Application for the Master of Science in Applied Data Science program, including a cover letter that addresses prior experiences with data analysis and motivation for the program, and a CV that includes acquired skills
2. Academic record (all post-secondary transcripts), with documentation of a bachelor’s degree from an accredited US college or university (or the foreign equivalent, as determined by a NACES-member transcript evaluation service)
3. Two letters of recommendation, of which at least one must speak to the academic background, preparation and accomplishments of the applicant relevant to data science
4. Recent employment and/or academic experience (including fellowships, internships, research positions)
5. GRE, GRE Subject, or GMAT scores, if provided. (None of these exams are required.)
6. Students with academic records from non-US colleges or universities should arrange for professional evaluation (and translation, if necessary) of their transcripts by a NACES-member service.
7. Students who are not US citizens or US Permanent Resident Aliens, and whose first language is not English, must provide proof of English proficiency. Typically, recent scores (within the past two years) will be required, as follows:
   1. Test of English as a Foreign Language (TOEFL): score of 83 or better on the TOEFL IbT, or 560 on the Paper-Based TOEFL; or
   2. International English Language Testing System (IELTS): score of 6.5 or better; or
   3. Recent records (within the past two years) of successful academic or professional work in a setting where English is the primary language in use may be considered as a substitute for the testing requirement.

Graduate Admission Selection Committee

The Admission Selection Committee for the Master of Science in Applied Data Science Program is charged with reviewing candidate application files and selecting students to be offered admission to the Master of Science in Applied Data Science Program.

The Committee is comprised of three faculty members teaching in the Applied Data Science Program:

1. The Director of the Applied Data Science Program, who chairs the Committee
2. Two other members of the Applied Data Science faculty, selected by the Director each year

Selection for an offer of admission to the Program requires the following:

1. Each member of the Committee has reviewed the candidate’s file.
2. Each member of the Committee has considered the candidate’s course work and/or any information regarding relevant job experiences, with regard to demonstrated skills involving computer programming, mathematics and statistics.
3. Each member of the Committee has certified that the candidate satisfies the minimum admission requirements.
   1. If any member of the Committee believes an applicant does not meet the minimum requirements, admission can only be offered on a provisional basis, through unanimous consent of the Committee.
   2. Provisional admission may also be extended on a case-by-case basis when
      1. students need additional preparation in mathematics or computer programming
      2. the bachelor’s degree is still in progress at the time of review and the candidate can reasonably be expected to provide official transcripts and fulfill all requirements a week before the start of the fall semester
      3. applicants agree to come to New College one or two semesters prior to the start of the fall semester to take classes in Introduction to Computer Programming, Calculus I, Linear Algebra, Introductory Statistics or any other data-science related course where the committee has identified a weakness in the candidate's preparation for the master's program

Degree Requirements

Total Semester Credit Hours Required: 36 semester credit hours (SCH)

• Passing all credit and non-credit courses in the first and second year of the program as listed in the Academic Program
• A minimum 3.00 cumulative grade point average (GPA) by the end of the program

Passing grades for graduate courses are A, A-, B+, B, B-, C+, C, C- and S (=Satisfactory). Failing grades are F and U (=Unsatisfactory).

Applied Data Science Faculty

Bernhard Klingenberg, Ph.D., University of Florida; Professor of Statistics, Director of Applied Data Science
Interests: Statistical Modeling, Categorical Data Analysis, Statistics Education, R, Mobile & Web Apps for Education

Rohan Loveland, D. Phil., University of Oxford; Assistant Professor of Computer Science
Interests: Machine Learning, Anomaly Detection

Tyrone Ryba, Ph.D., Florida State University; Associate Professor of Bioinformatics
Interests: Genome Regulation, Cancer Biology, Data Visualization

Gil Salu, Visiting Assistant Professor of Computer Science
Interests: Enterprise Systems, Cloud Architecture

Andrey Skripnikov, Ph.D., University of Florida; Associate Professor of Applied Statistics
Interests: Sports Analytics, Econometric Time Series Analysis, Statistical Learning

Toby Wade, Ph.D., London School of Economics; Assistant Professor of Statistics and Data Science, Director of Artificial Intelligence and Crypto
Interests: Quantitative Finance