We have developed innovative courses to support our degree programs in computational science. These classes generally have an ISC course prefix. In addition, department faculty teach courses in computational science listed with different departments across campus.
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Below is a complete list of undergraduate courses taught by DSC faculty:
Note: Not all courses are offered each semester. Check the FSU Registrar's Class Search Snapshots for availability.
Elective Courses for non-majors
- ISC 1057 - Computational Thinking (3)
Note: Satisfies Quantitative and Logical Thinking requirement. - ISC 2310 - Introduction to Computational Thinking in Data Science with Python (3)
Core Courses and Seminar Classes
- ISC 3222 - Symbolic and Numerical Computations (3)
- ISC 4220 - Continuous Algorithms for Science Applications (4)
- ISC 4221 - Discrete Algorithms for Science Applications (4)
- ISC 4223 - Computational Methods for Discrete Problems (4)
- ISC 4232 - Computational Methods for Continuous Problems (4)
- ISC 4304 - Programming for Scientific Applications (4)
- ISC 4931 - Junior Seminar in Computational Science (1)
- ISC 4932 - Senior Seminar in Computational Science (1)
- ISC 4943 - Practicum in Computational Science (varies)
Collateral Courses
- ISC 3313 - Introduction to Scientific Computing (3)
Note: Satisfies Computer Skills Competency requirement.
Elective Courses
- DIG 3725 - Introduction to Game and Simulator Design (3)
- ISC 4245C - Data Mining (3)
- ISC 4302 - Scientific Visualization (3)
- ISC 4304C - Programming for Scientific Applications (3)
- ISC 4420 - Introduction to Bioinformatics (3)
- ISC 4933 - Computational Evolutionary Biology (3)
- ISC 4933 - Computational Space Physics (3)
- ISC 4933 - Data Science Meets Health Science (3)
- ISC 4933 - Genome Sequencing and Analysis (3)
- ISC 4933 - Geometric Morphometrics (3)
- ISC 4933 - Inferences in Conservation Genetics (3)
- ISC 4933 - Integral Equation Methods (3)
- ISC 4933 - Selected Topics In Computational Science (varies)
- ISC 4933 - Verification and Validation in Computational Science (3)
- ISC 4933 - Survey of Numerical Partial Differential Equations (3)
- ISC 4971 - Honors in the Major Program (3)
Special Topics
Last Offered Spring 2024
Schedule & Location: M W F 10:40-11:30, 499 DSL
Have you ever wondered why fires spread and grow in size so quickly or how smoke plumes can travel thousands of kilometers? These behaviors are governed by fuel properties, atmospheric conditions, topography, and more. This course introduces physics-based and data-driven models in fire science, and investigates the sensitivity and uncertainty of these models. We will discuss computational tools including cellular automata, level set methods, and data-driven methods for discovering equations from measured data. Finally, we will explore techniques for analyzing both simulated and measured fire data.
Please contact
This course will focus on the applied data science pipeline of data acquisition, data processing and integration, data modeling and analysis, and validation and delivery, commonly used in the Health industry. Topics include data normalization, scientific visualization, multivariate regression, and Artificial Neural Networks (dense, convolutional, recurrent, and adversarial). The examples and projects of this course contain 1D to 4D health data of electrocardiogram sequences, X-ray, Magnetic resonance imaging (MRI), and functional MRI images.
ISC 4933/ISC 5935. Computational Probabilistic Modeling (3). Prerequisites: MAC 2312 - Calculus II, MAS 3105 – Applied Linear Algebra, and STA 4442/5440: Introduction To Probability or STA 4321/5323: Introduction to Mathematical Statistics, or the permission of the instructor. In this course, students are introduced to probabilistic programming and modeling for modern data science and machine learning applications. Algorithms for predictive inference are covered from a theoretical and practical viewpoint with an emphasis on implementation in Python. Topics include an introduction to probability and learning theory, graph-based methods, machine learning with neural networks, dimensionality reduction, and algorithms for big data. [source]
Offered Fall 2022
Schedule & Location: MWF 10:40-11:30, 499 DSL
In this course, students will explore the different elements that determine the outcome of infectious disease in humans, using COVID-19 as a case study. Starting with a very basic model that includes susceptible, infected, and recovered individuals, students will gradually incorporate factors that may affect the outcome of an outbreak, such as masking/quarantining, gain and loss of natural and vaccine-based immunity, and changing virulence/strains. After summarizing data already collected during the recent pandemic, students will inform the model to determine the conditions under which different outcomes may occur. Students will summarize their results graphically and present their findings.
No prerequisites or programming experience required. The course is designed to be accessible to all students, regardless of background or major.
Bioinformatics provides a quantitative framework for understanding how the genomic sequence and its variations affect the phenotype. Designed for biologists and biochemists seeking to improve quantitative data interpretation skills, and for mathematicians, computer scientists and other quantitative scientists seeking to learn more about computational biology. Lab exercises reinforce the classroom learning.
Upcoming Events
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October 2024
- Colloquium with Zhe He (2024-10-02) (Wed Oct 02, 03:30 PM)
- Colloquium with Zecheng Zhang (2024-10-09) (Wed Oct 09, 03:30 PM)
- Colloquium with Martin Schmidt (2024-10-16) (Wed Oct 16, 03:30 PM)
- Colloquium with Ben Adcock (2024-10-18) (Fri Oct 18, 03:30 PM)
- Colloquium with Peter Hoeflich (2024-10-23) (Wed Oct 23, 03:30 PM)
Latest News
- Alumni Spotlight: David Robinson
- FSU to host international astrophysics conference May 20-24
- Sachin Shanbhag - College of Arts and Sciences Faculty Spotlight
- Researcher Catherine Hancock & Professor Kevin Speer Co-Author Paper in Nature
- Fall 2023 Newsletter - Emeritus Professor continues high level research
- Liam White - College of Arts and Sciences Student Spotlight
- Scientific Computing doctoral grad awarded exclusive SEED Grant
- Nole Edge explores the science behind wildfires with Daryn Sagel and Bryan Quaife