Undergraduate Courses 2024-25

A 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.

This course aims to equip students with the necessary knowledge and techniques in linear algebra to enable them to study linear least squares optimization and machine learning. Topics include vector and matrix operations, linear systems, matrix inverse, decomposition, eigendecomposition, and singular value decomposition. These techniques will be applied to topics including regression, classification, model validation, inversion, regularization, constrained least squares problem, clustering, and dimensionality reduction. Numerical computation is used throughout the course as a learning tool. No previous knowledge of linear algebra is assumed.

Introduction to the fundamental concepts and techniques of object-oriented programming, including classes, objects, heritage, polymorphism, modularization and associations, etc.

A 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.

The 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.

This 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.

This course introduces fundamental principles and techniques of statistical modeling for uncovering patterns and making predictions from data. Various statistical learning methods routinely used in the fields of data science and machine learning will be covered, including linear regression, classification, random forest, support vector machines, dimension reduction, clustering, graphical models, and neural networks. The course contents include both theoretical concepts and Python coding demonstrations to help students develop a solid understanding of the core principles and gain practical experience in algorithm implementation. Through conceptual and hands-on explorations, students will acquire essential knowledge and skills for careers in a society that is increasingly driven by data and machine intelligence.

Data 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.

This 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.

This course provides students with an overview of the field of Artificial Intelligence (AI) focusing on machine learning ML) and its applications. It introduces several basic AI/ML Python software packages with emphasis on using existing AI techniques provided by the Python packages to solve problems in science. Throughout the course, students will gain hands-on experiences of using modern AI/ML software via solving practical problems. Students without the prerequisites but possess relevant knowledge may seek instructor's approval for enrolling in the course.

This course provides students with a solid understanding of relational databases and introduces how to write structured query language (SQL) statements for effective data analytics. They will explore essential tasks like filtering, sorting, aggregating, manipulating, cleaning, and visualizing data using SQL. Additionally, students will discover how to integrate SQL seamlessly with popular business intelligence (BI) tools such as Tableau, Power BI, Microsoft Excel, and other relevant tools. By the end of the course, students will possess the necessary skills to apply SQL in various data analytics scenarios.

Students 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.

This 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.