The Master's in Applied Data Science program prepares students to collect, analyze, and report on big data effectively and ethically. Our rigorous curriculum combines these in-demand data scientist skills and knowledge with opportunities for their real-world application. With a five-to-one student-to-faculty ratio, you’ll get personalized attention, and gain networking and field experience with leading data-driven companies.

About The Program

The Applied Data Science graduate program is a two-year 36-credit program, which includes 11 full-semester 3-credit courses, two required practicums (one being a full-semester 3-credit course), industrial seminar series in the first three semesters and industrial workshops during the January interterm. The curriculum offers a blend of interdisciplinary theory and practical application of skills through courses, such as Exploratory Data Analysis, Applied Machine Learning, and Data Visualization. Our students gain experience applying both Python and R to develop solutions for our corporate partners as a part of the rigorous curriculum.

In the final semester, students will implement their skills and obtain industry experience with a paid practicum working full-time as part of a data science team at one of our corporate partners, or at another company or organization of their choice. The practicum course will be completed under Data Science faculty supervision and is a required component of the program.  At the end of the program, students would have gained the industry knowledge, technical skills, and real-world experience required to start their successful careers.

Students can expect a very well-rounded education from the data science program. Whether it be in algorithms, statistics or general programming, you’ll have all the things you’ll need to be successful as a data scientist.

Adam Lashley
M.S. in Applied Data Science Student

What do Students and Alumni Say?

Career Pathways

  • Researcher
  • Data Scientist
  • Machine Learning Engineer
  • Applications Architect
  • Data Architect
  • Business Intelligence Developer
  • Statistician
  • Data Analyst

Exceptional Return on Investment

Affordable Tuition
Affordable Tuition

Fall 2022 Applications Now Available

Are you ready to pursue an in-demand, high-paying career changing the world through big data? Start your application today! Apply Now

Real-World Experience

Our curriculum is developed with our corporate partners and industry experts as the applications of big data evolve. Students will get hands-on experience with real data sets from a variety of industries, and complete a paid, full-time practicum, often with our partners or an organization of their choice.

Corporate Partners

Allen Institute for Brain Science

LexisNexis Risk Solutions Groups

Novetta

HPCC Systems

Riff Analytics

Advisory Board

See the executives and professionals committed to enhancing knowledge and practice of Data Science and Business Analytics for our program as part of the M.S. Applied Data Science Advisory Board

Courses

The state-of-the-art curriculum blends theory and practice. Over four semesters students in the small-cohort program are guided by our values of academic rigor, dedication, practical relevance, and teamwork.

The bootcamp aims to equip all students entering the graduate program with introductory skills and knowledge needed to conduct further coursework in the program. This will help students from diverse backgrounds to have a common knowledge base as a cohort. Topics will include review of Python and R programming, common data science tools, resources and platforms, operating systems, database concepts and systems, among others. The Bootcamp is offered during the week of new graduate student orientation. It is a 3-day intensive course.

Exploratory data analysis in the context of knowledge discovery, including the use of data visualization software. Practical skills for working with large datasets including common methods for gathering, organizing, and reshaping structured and unstructured data, with an introduction to related methods in R, and some examples in Python.

Fundamentals of algorithms and measures of performance. Taught in Python, the course includes an exploration of efficient algorithms for sorting and retrieving data, graph algorithms and combinatorial optimization, dynamic programming, randomized algorithms and approximation algorithms.

A statistics course focusing on descriptive and inferential statistics, with topics on linear regression, confidence intervals and hypotheses testing, including probability theory and modern approaches such as resampling, with all methods illustrated in R and a focus on methods relevant for data science using industrial datasets.

Project-based course with a coverage of supervised and unsupervised learning and an emphasis on working with real industrial data. Bayesian analysis and other specific learning paradigms including regression, clustering, random forests, support vector machines, kernel methods, and neural networks.

Seminar series which hosts professionals and executives as guest speakers from a variety of industrial domains. Each weekly or biweekly seminar covers topics and applications to diverse problems in business via applications of various data science techniques.

MS Applied Data Science Faculty & Staff

Nikita Bagley Data Science Program Coordinator
Office / Division / Concentration Applied Data Science Graduate Program
Burcin Bozkaya Director of Applied Data Science Graduate Program
Professor of Data Science
Office / Division / Concentration Applied Data Science Graduate Program Natural Sciences Division Data Science
Bernhard Klingenberg Adjunct Instructor of Statistics
Office / Division / Concentration Applied Data Science Graduate Program Natural Sciences Division Statistics Data Science
Matthew Lepinski Associate Professor of Computer Science
Office / Division / Concentration Applied Data Science Graduate Program Natural Sciences Division Computer Science Data Science
Ann Masterman Executive Director of New Student and Graduate Admissions
Office / Division / Concentration Admissions Applied Data Science Graduate Program
Patrick McDonald Professor of Mathematics
Office / Division / Concentration Applied Data Science Graduate Program Natural Sciences Division Mathematics Applied Mathematics Data Science
Tiago Perez Assistant Professor of Data Science
Office / Division / Concentration Applied Data Science Graduate Program Natural Sciences Division Data Science
Jack Reilly Associate Professor of Political Science
Office / Division / Concentration Social Sciences Division Political Science Public Policy Applied Data Science Graduate Program Quantitative Social Science Urban Studies
Tania Roy Assistant Professor of Human Centered Computing
Office / Division / Concentration Applied Data Science Graduate Program Natural Sciences Division Computer Science Data Science
Tyrone Ryba Associate Professor of Bioinformatics
Office / Division / Concentration Natural Sciences Division Biology Applied Data Science Graduate Program
Andrey Skripnikov Assistant Professor of Applied Statistics
Office / Division / Concentration Natural Sciences Division Data Science Statistics Applied Data Science Graduate Program

Accreditation

New College of Florida is accredited by the Southern Association of Colleges and Schools Commission on Colleges to award Bachelor’s degrees and Master’s in Applied Data Science degrees.

Contact the Commission on Colleges at 1866 Southern Lane, Decatur, Georgia 30033-4097, telephone 404-679-4500, at www.sacscoc.org for questions about the accreditation of New College of Florida.

Catalog & Academic Calendar

Graduate Catalog 2021-2021

Graduate Academic Calendar 2021-2022