grade	term	code	subject
2	1	3349.201A*	Understanding Computational Sciences
		3349.203*	Theory and Practice in Computational Sciences 1
		3349.205A	Foundation and History of Computational Sciences
	2	3349.204*	Theory and Practice in Computational Sciences 2
		3349.206	Computational Science Models and Data 1
3	1	881.319	Numerical Linear Algebra
		3349.308	Introduction to Scientific Visualization
		3341.353*	Data Introduction to Scientific Computing (Established at Department of Mathematical Sciences)
		4190.204	Data Structures
	2	881.320	Introduction to Numerical Analysis
		4190.313	Linear and Non-linear Computation Models
		4190.407	Algorithms
		3349.309*	Data Science
4	1	108.417	Language and Information Processing
		4190.417	Computer Animation
	2	108.417	Language and Information Processing
		3349.406	Applied Computational Sciences
		3349.401	Topical Research in Computational Sciences
		3349.404*	Capstone Research in Computational Sciences

*(bold text) Be required subject to major subjects
3349.404* Capstone Research in Computational Sciences Both semester are open

code	credits
3349.201A	3-3-0
This course is designed to provide the beginners general backgrounds and techniques by solving diverse topics relevant to computational sciences. Computational science is broadly used to compute immense calculations and/or numerical solutions arising from natural sciences, engineering and social sciences using computer and mathematics. The contents include 1) several methods to find analytic and numerical solutions of differential equations, 2) transformation and inverse transform of acquired data, 3) visualization of data and calculations, and 4) MPI (Message Passing Interface) for supercomputing. Prerequisite courses: calculus or similar mathematics

code	credits
3349.203	3-3-0
This course is designed to provide the beginners general backgrounds and techniques by solving diverse topics relevant to computational sciences. Computational science is broadly used to compute immense calculations and/or numerical solutions arising from natural sciences, engineering and social sciences using computer and mathematics. The contents include 1) several methods to find analytic and numerical solutions of differential equations, 2) transformation and inverse transform of acquired data, 3) visualization of data and calculations, and 4) MPI (Message Passing Interface) for supercomputing. Prerequisite courses: calculus or similar mathematics

code	credits
3349.204	3-3-0
This course aims to understand in-depth theory on numerical programming for computational sciences, and high-level programming skill using Python will be introduced. Students will learn program design principles, such as analysis, optimization, and design patterns. In order to achieve it, integration technique for Python and C will be introduced by practice. Also, extension modules of Python will be introduced to implement and visualize what students have learned.

code	credits
3349.205A	3-3-0
This course offers the calculus which is necessary to study the computational sciences as well as the origin and history for those whom has little experience in this area. Topics include Development of interpolation theory, Development of numerical differentiation and integration, Development of numerical methods for solving nonlinear equations, Development of numerical methods for linear systems, Development of numerical methods for ordinary differential equations, Development of numerical methods for partial differential equations and Development of numerical optimization.

code	credits
3349.309	3-3-0
There are huge number of under-utilized data because data is too big and we do not know how to approach the problems. Data science is a new approach to so called big data problem. Unlike traditional computer science that emphasize theory of computation, data science is focused more on problem solving. Techniques such as data collecting and cleaning, data mining, data visualization are covered.

code	credits
3349.404	3-3-0
Each student selects a contemporary topic in computational sciences and technology, which arises in real phenomena such as industries and laboratories; designs a method of computational solutions; proceeds to attain it’s solutions. Finally, student should complete and present his/her report and/or paper.

code	credits
3349.401	3-3-0
This course offers the topics arising in recent computational sciences and technology and applications. As a result, students should complete their reports for what they have done.

code	credits
3349.206	3-3-0
This course offers the theories and methods for processing the massive data measured from natural sciences, engineering and social sciences. Topics include: (1) data fitting such as least-square method, (2) data transform such as Fourier transform, (3) architectures such as interpolation and extrapola- tion, (4) data optimization such as multidimensional search, (5) data filtering to find meaningful data and (6) time-series such as forecast.

code	credits
3349.308	3-3-0
Examination of scientific visualization is a critical portion of the analysis and interpretation of numerical simulations. This course offers a wide variety of methods used for scientific visualization. Output data from the scientific computations appears in various kinds of formats including fluid flow, distribution, trajectory, and image. In order to understand and analyze these data effectively, numerous existing visualization techniques are used. In this course students will learn how to simply present data, and how to convert data into various kinds of formats. The topic also includes the techniques for animating time-dependent data set. In addition, this course will cover the methods for visualizing multi-dimension field data in order to learn characteristics and effective representations of scalar, vector, and signed-distance fields. The methods for generation and visualization of grids will also be covered. At last, introduction to image processing from the aspect of LDR and HDR will be given.

code	credits
3349.406	3-3-0
A survey of scientific computing methods, emphasizing programming methods, interpretation of numerical results, ad checks for validation and verification. Many applications on computational mathematics, computational physics, computational chemistry, computational biology are studied.