# Undergraduate Courses 2022-23

Search Result of DASC : 13 found

- CHEM 4160Cheminformatics3 Credit(s)Prerequisite(s)CHEM 2110 AND (CHEM 2409 OR MATH 2351 OR DASC 2010)DescriptionBasics of Chemometrics; Database mining (CSD and PDB); Multivariate data sets, statistics and data reduction; Data analysis and visualization; Molecular representation and chemical descriptors (e.g. SMILES / SMARTS patterns); Application to structure-based drug design.
- DASC 2010Calculus for Data Analytics in Science3 Credit(s)Prerequisite(s)MATH 1014 OR MATH 1020 OR MATH 1024Exclusion(s)MATH 2011, MATH 2023DescriptionA concise introduction to multivariable calculus using numerical computing software. Fundamental concepts from multivariable calculus with emphasis on applications and calculations using software. Topics from vectors, curves and parametric equations, differentiation in several variables, applications in approximation, optimization and gradient descent algorithm, and integration in several variables.
- DASC 2020Linear Algebra for Data Analytics in Science3 Credit(s)Prerequisite(s)MATH 1014 OR MATH 1020 OR MATH 1024Exclusion(s)MATH 2111, MATH 2121, MATH 2131, MATH 2350DescriptionThis course provides an introduction to linear algebra using numerical computing software. Topics include systems of linear equations, matrix algebra and determinants, eigenvalue and eigenvector, eigenvalue decomposition, singular value decomposition (SVD) and least square problems.
- DASC 2110Object-oriented Programming for Data Analytics in Science3 Credit(s)Prerequisite(s)COMP 1021Exclusion(s)COMP 2012, COMP 2012HDescriptionIntroduction to the fundamental concepts and techniques of object-oriented programming, including classes, objects, heritage, polymorphism, modularization and associations, etc.
- DASC 2210A Survey on Big Data in Science and Society1 Credit(s)DescriptionA survey on data collection and applied data science in different areas of science and in the society as a whole. Examples and case studies will be taken from physics, chemistry, life science and ocean science as well as other disciplines that are heavily data driven. Graded DI, PA or F.
- DASC 2220Statistics and Probability for Data Analytics in Science3 Credit(s)Prerequisite(s)COMP 1021 AND (MATH 1014 OR MATH 1020 OR MATH 1024)Exclusion(s)MATH 2411, IEDA 2520, IEDA 2540, ISOM 2500, LIFS 3150DescriptionThe course is designed for students who are new to data science and provides them with a basic training of probability and statistics. Some ideas and principles of data analytics with probability and statistics are covered. In addition to the knowledge of data, students will learn some data analytical skills and have hands-on experience of processing and analyzing real datasets by using a software like R or Python.
- DASC 3120Data Structures for Data Analytics in Science3 Credit(s)Prerequisite(s)DASC 2110Exclusion(s)COMP 2012, COMP 2012HDescriptionThis is a continuation of DASC 2110. It covers the object-oriented view of data structures, including linked list, queues, and stacks; and algorithms including recursion, sorting, and searching. Students are expected to understand and use these techniques to handle data.
- DASC 3230Statistical Modeling for Data Analytics in Science3 Credit(s)Prerequisite(s)DASC 2220DescriptionConcepts and techniques in the field of statistical modeling will be introduced. Materials include the models used to explore different sorts of data for the discovery of predictive models and knowledge. The course will include models such as linear regression models, logistic regression models, support vector machines, decision trees, random forest, ensemble models, nearest neighbor, clustering, as well as spatial models, time-series models, quantile regression models, and network models. R or Python will be covered. Students will also gain hands-on experience with data analytical tools from various applications in sciences.
- DASC 3240Data Visualization in Science3 Credit(s)Prerequisite(s)DASC 2220Exclusion(s)COMP 4462DescriptionData visualization is the graphical representation of applied data science. It can also provide us with a powerful way to communicate data-driven findings, motivate analyses, and detect flaws in an infographic or dashboard. This course illustrates how to use the techniques of data visualization and discovery tools to explore, visualize and analyze data. By the end of the course, students will be able to utilize tools and packages in R or Python to enhance their skills on science communication.
- DASC 3250Numerical Methods for Data Analytics in Science3 Credit(s)Prerequisite(s)COMP 1021 AND (DASC 2020 OR MATH 2111 OR MATH 2121 OR MATH 2131 OR MATH 2350)Exclusion(s)MATH 3312, MECH 4740, PHYS 3142DescriptionThis course introduces numerical methods for various data analytic tools. Topics include numerical algorithms for linear systems, eigenvalues and eigenvectors, nonlinear equations, interpolation and approximation, numerical integration and solution of ordinary differential equations, fundamental theory and techniques of constrained and unconstrained optimizations, fundamental techniques and software for machine learning. Examples are taken from various applications in both physical and life sciences.
- DASC 4300Capstone Project for Data Analytics in Science3 Credit(s)DescriptionStudents are expected to complete a project in an area of their study tracks under the guidance of a faculty member. It provides an opportunity for students to integrate those mathematical and data analytics techniques they learned to analyze a data intensive problem in science. For DASC students in their fourth year of study only.
- DASC 4400Data Analytics in Information Science3 Credit(s)Prerequisite(s)DASC 3250DescriptionThis course introduces various case studies drawn from different areas of data and information sciences to illustrate the use of data analytics as a problem-solving tool. Topics include recommendation systems, data from social media and FinTech. Each application integrates mathematical models, numerical techniques and computer implementations into a coherent perspective.
- OCES 3301Data Analysis in Ocean Science3 Credit(s)Prerequisite(s)(MATH 2411 OR LIFS 3150 OR DASC 2220) AND (COMP 1021 OR COMP 1029P)DescriptionThe goal of the course is to learn basic numerical analysis tools needed to compute and practice simple techniques for analyzing oceanographic data. These analysis include basic statistics, curve fitting, and simple graphics. Students will learn these techniques through examples from ocean data sets. Students will learn to conduct these analyses with Python or R and are not assumed to have experience in programming.