The Florida State University College of Arts and Sciences Interdisciplinary Data Science program offers select undergraduate majors the option of accelerating their studies and getting a head start on graduate school. Combined bachelor's/master's pathways allow prospective students to substitute specific graduate coursework for undergraduate classes and count up to four courses toward both bachelor’s and master’s degrees, which may save them time and money. Completing graduate coursework before undergraduate graduation equips students with advanced skills needed for better internships and employment opportunities.
APPLICATION GUIDELINES
Students interested in a combined pathway should register their interest. Applications will be sent to interested students once they meet requirements to qualify.
REQUIREMENTS
Students are eligible if they have a 3.2+ GPA overall and a 3.2+ GPA in upper-division coursework. Students must have completed at least 90 credit hours of undergraduate coursework (60 for Honors students.) Transfer students must have completed at least two semesters and 24 credits at FSU. Additionally, students must have completed MAC 2312 - Calculus II, STA 2122 – Introductory Statistics, and an object-oriented programming language, preferably Python. The average GPA for prerequisites must be no less than 3.2.
Eligible students also may benefit by applying undergraduate scholarships and financial assistance toward a portion of the graduate coursework tuition and fees being counted toward both degrees. In some cases, combined pathway students will be able to shorten their overall time in school.
Interested students should register their interest at the same time, or as soon as possible after, they are admitted to their major to determine if they are eligible. Accepted combined pathway students will enroll in the graduate courses typically during their final two terms as an undergraduate student.
SEPARATE GRADUATE APPLICATION
Students accepted into a combined pathway will still be required to apply separately to the master’s degree program. Students should begin this process during the first semester of their senior year.
Although students completing a combined pathway are typically on track for admission to the master’s program, students must still meet all application requirements. Visit the admission requirements page to learn more about what materials will be needed, including recommendations, personal statements, resume, and transcripts. Note that the graduate program also has a required deadline for applying and submitting all required materials.
Combined pathway students must average a 3.0 GPA or higher in the four shared courses in order for their work to count toward the master’s degree.
Combined pathway students not accepted into a master’s program may still use their shared credit hours toward bachelor’s degree requirements.
SELECT THE PATHWAY THAT BEST FITS YOUR PLANS
BS in Computer Science (BS-CS to MS-IDS)
CREDIT HOURS COMBINED
| BS-Computer Science | 120 credit hours |
| MS in Interdisciplinary Data Science | 30 credit hours |
| Total for both degrees | 150 credit hours |
SHARED COURSEWORK
Students in the Combined BS-CS/MS IDS Pathway can get a jump-start on a master’s degree in Interdisciplinary Data Science by taking up to four graduate courses that will count toward both their graduate and undergraduate degree requirements. Combined pathway students must average a 3.0 GPA or higher in the shared courses in order for their work to count toward a master’s degree.
The graduate classes are more advanced than the undergraduate classes that they are replacing and will provide additional information needed to meet the standard skills and knowledge required by the university and expected by employers hiring master’s graduates.
Students in the Combined BS-CS/MS IDS Pathway can substitute the following graduate courses for four undergraduate courses:
CAP 5768 - Introduction to Data Science (3). Prerequisites: Graduate standing in science or engineering, or permission of the instructor. Some familiarity with basic concepts in linear algebra and probability theory. Some basic knowledge of algorithm designs and some experience with Python or Java programming. This course will serve as an introduction and overview of the fundamentals of data science. Specific topics will include an overview of data management fundamentals, information retrieval, introductory machine learning concepts and frameworks, basic data visualization architectures, an overview of architectures of large-scale data management and analytical systems, and distributed computational paradigms.
CAP 5771 - Data Mining (3). Prerequisite: ISC 3222 or ISC 3313 or ISC 4304C or COP 3330 or COP 4530 or instructor permission. This course enables students to study data mining concepts and techniques, including characterization and comparison, association rules mining, classification and prediction, cluster analysis, and mining complex types of data. Students also examine applications and trends in data mining.
CIS 5379 - Computer Security Fundamentals for Data Science (3). Prerequisite: CGS 3465. This is an introduction to computer security course, targeted towards graduate students in data science. This course covers a broad range of topics within computer security, such as cryptographic algorithms, security protocols, network authentication, and software security.
CAP 5769 - Advanced Topics in Data Science (3). Prerequisite: COP 4530 (Computer Science undergraduate students); or IDC 4104 and graduate standing in science or engineering majors; or instructor permission. Familiarity with basic linear algebra, probability, algorithms, some Python or Java skills.: This course will emphasize practical techniques for working with large-scale, heterogeneous data. Specific topics covered will include fundamentals of data management, data models, data cleaning, fusion, information retrieval, statistical modeling, machine learning, deep learning, data pipelines, visualization, "big data" management systems, distributed computational frameworks and paradigms and tools. The goal is to provide advanced theoretical foundations, hands-on experience and train students to become capable data scientists, develop their analytical skills, provide them experience with real-world systems.
Please click here to request a review of your transcript for admission to the combined pathway.
BA in Computer Science or Computer Programming and Applications (BA-CS or BA-CPA to MS-IDS)
CREDIT HOURS COMBINED
| BA-Computer Science or BA-Computer Programming and Applications |
120 credit hours |
| MS in Interdisciplinary Data Science | 30 credit hours |
| Total for both degrees | 150 credit hours |
SHARED COURSEWORK
Students in the Combined BA-CS/MS IDS or BA-CPA/MS IDS Pathways can get a jump-start on a master’s degree in Interdisciplinary Data Science by taking up to four graduate courses that will count toward both their graduate and undergraduate degree requirements. Combined pathway students must average a 3.0 GPA or higher in the shared courses in order for their work to count toward a master’s degree.
The graduate classes are more advanced than the undergraduate classes that they are replacing and will provide additional information needed to meet the standard skills and knowledge required by the university and expected by employers hiring master’s graduates.
Students in the Combined BA-CS/MS IDS Pathway can substitute the following graduate courses for four undergraduate courses:
CAP 5768 - Introduction to Data Science (3). Prerequisites: Graduate standing in science or engineering, or permission of the instructor. Some familiarity with basic concepts in linear algebra and probability theory. Some basic knowledge of algorithm designs and some experience with Python or Java programming. This course will serve as an introduction and overview of the fundamentals of data science. Specific topics will include an overview of data management fundamentals, information retrieval, introductory machine learning concepts and frameworks, basic data visualization architectures, an overview of architectures of large-scale data management and analytical systems, and distributed computational paradigms.
CAP 5771 - Data Mining (3). Prerequisite: ISC 3222 or ISC 3313 or ISC 4304C or COP 3330 or COP 4530 or instructor permission. This course enables students to study data mining concepts and techniques, including characterization and comparison, association rules mining, classification and prediction, cluster analysis, and mining complex types of data. Students also examine applications and trends in data mining.
CIS 5379 - Computer Security Fundamentals for Data Science (3). Prerequisite: CGS 3465. This is an introduction to computer security course, targeted towards graduate students in data science. This course covers a broad range of topics within computer security, such as cryptographic algorithms, security protocols, network authentication, and software security.
CAP 5769 - Advanced Topics in Data Science (3). Prerequisite: COP 4530 (Computer Science undergraduate students); or IDC 4104 and graduate standing in science or engineering majors; or instructor permission. Familiarity with basic linear algebra, probability, algorithms, some Python or Java skills.: This course will emphasize practical techniques for working with large-scale, heterogeneous data. Specific topics covered will include fundamentals of data management, data models, data cleaning, fusion, information retrieval, statistical modeling, machine learning, deep learning, data pipelines, visualization, "big data" management systems, distributed computational frameworks and paradigms and tools. The goal is to provide advanced theoretical foundations, hands-on experience and train students to become capable data scientists, develop their analytical skills, provide them experience with real-world systems.
Please click here to request a review of your transcript for admission to the combined pathway.
BS in Scientific Computing (BS-SC to MS-IDS)
CREDIT HOURS COMBINED
| BS-Scientific Computing | 120 credit hours |
| MS in Interdisciplinary Data Science | 30 credit hours |
| Total for both degrees | 150 credit hours |
SHARED COURSEWORK
Students in the Combined BS-SC/MS IDS Pathway can get a jump-start on a master’s degree in Interdisciplinary Data Science by taking up to four graduate courses that will count toward both their graduate and undergraduate degree requirements. Combined pathway students must average a 3.0 GPA or higher in the shared courses in order for their work to count toward a master’s degree.
The graduate classes are more advanced than the undergraduate classes that they are replacing and will provide additional information needed to meet the standard skills and knowledge required by the university and expected by employers hiring master’s graduates.
Students in the Combined BS-SC/MS IDS Pathway can substitute four of the following graduate courses for four undergraduate courses:
ISC 5305 - Scientific Programming (3). Prerequisites: working knowledge of one programming language (C++, Fortran, Java), or instructor permission. This course focuses on object-oriented coding in C++, Java and Fortran 90 with applications to scientific programming. Discussion of class hierarchies, pointers, function and operator overloading and portability. Examples include computational grids and multidimensional arrays.
ISC 5307 - Scientific Visualization (3). Prerequisites: CGS 4406, ISC 5305, or instructor permission. The course covers the theory and practice of scientific visualization. Students learn how to use state-of-the-art visualization toolkits, create their own visualization tools, represent both 2-D and 3-D data sets, and evaluate the effectiveness of their visualizations.
ISC 5315 - Applied Computational Science I (4). Prerequisites: ISC 5305; MAP 2302; or instructor permission. This course provides students with high-performance computational tools necessary to investigate problems arising in science and engineering, with an emphasis on combining them to accomplish more complex tasks. A combination of coursework and lab work provides the proper blend of theory and practice with problems culled from the applied sciences. Topics include numerical solutions to ODEs and PDEs, data handling, interpolation and approximation, and visualization.
ISC 5318 - High-Performance Computing (3). Prerequisites: ISC 5305 or equivalent or instructor permission. This course introduces high-performance computing, a term which refers to the use of parallel supercomputers, computer clusters, as well as software and hardware in order to speed up computations. Students learn to write faster code that is highly optimized for modern multi-core processors and clusters, using modern software-development tools and performance analyzers, specialized algorithms, parallelization strategies, and advanced parallel programming constructs.
ISC 5228 - Monte Carlo Methods (3). Prerequisites: ISC 5305; MAC 2311, 2312. This course introduces probabilistic modeling and Monte Carlo methods (MCMs) suitable for graduate students in science, technology, and engineering. Students learn discrete event simulation, MCMs and their probabilistic foundations, and the application of MCMs to various fields. In particular, Markov chain MCMs are introduced, as are the application of MCMs to problems in linear algebra and the solution of partial differential equations.
Please click here to request a review of your transcript for admission to the combined pathway.
BS in Statistics (BS-Stat to MS-IDS)
CREDIT HOURS COMBINED
| BS-Statistics | 120 credit hours |
| MS in Interdisciplinary Data Science | 30 credit hours |
| Total for both degrees | 150 credit hours |
SHARED COURSEWORK
Students in the Combined BS-Stat/MS-IDS Pathway can get a jump-start on a master’s degree in Interdisciplinary Data Science by taking up to four graduate courses that will count toward both their graduate and undergraduate degree requirements. Combined pathway students must average a 3.0 GPA or higher in the shared courses in order for their work to count toward a master’s degree.
The graduate classes are more advanced than the undergraduate classes that they are replacing and will provide additional information needed to meet the standard skills and knowledge required by the university and expected by employers hiring master’s graduates.
Students in the Combined BS-Stat/MS-IDS Pathway can substitute the following graduate courses for four undergraduate courses:
STA 5207 - Applied Regression Methods (3). Prerequisite: One of STA 2122, STA 4322, or STA 5126. This course discusses topics such as general linear hypothesis, analysis of covariance, multiple correlation and regression, response surface methods.
STA 5066 - Data Management and Analysis with SAS (3). Prerequisite: Previous background in statistics at least through linear regression or instructor permission. This course introduces SAS software in lab-based format. SAS is the world’s most widely used statistical package for managing and analyzing data. The objective of this course is for students to develop the skills necessary to address data management and analysis issues using SAS. This course includes a complete introduction to data management for scientific and industrial data and an overview of SAS statistical procedures.
STA 5067 - Advanced Data Management and Analysis with SAS (3). Prerequisite: STA 5066. This course presents additional methods for managing and analyzing data with the SAS system. It covers as many of the following topics as time permits: advanced data step topics, manipulation of data with Proc SQL, the SAS Macro Facility, simulation with the data step and analyses with Proc IML.
STA 5166 - Statistics in Applications I (3). Prerequisite: MAC 2313. This course introduces topics such as comparison of two treatments, random sampling, randomization and blocking with two comparisons, statistical inference for means, variances, proportions and frequencies, and analysis of variance.
Please click here to request a review of your transcript for admission to the combined pathway.