Undergraduate Courses 2025-26

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DASC

Data Analytics in Science

DASC 2010
Calculus for Data Analytics in Science
3 Credit(s)
Prerequisite(s)
MATH 1014 OR MATH 1020 OR MATH 1024
Exclusion(s)
MATH 2011, MATH 2023
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain fundamental concepts and techniques of differential and integral calculus for functions of multiple variables.
- 2.
  Use computer software to calculate arc lengths of curves, as well as gradients and directional derivatives.
- 3.
  Apply computer software to solve optimization problems.
- 4.
  Evaluate the accuracy of results generated by software, including generative AI tools.
DASC 2020
Applied Linear Algebra for Least Squares Optimization and Machine Learning
3 Credit(s)
Prerequisite(s)
(MATH 1014 OR MATH 1020 OR MATH 1024) AND (COMP 1021 OR COMP 1023)
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain fundamental linear algebra concepts and techniques, including vector and matrix operations, determinants, matrix inverse, decomposition, eigendecomposition, and singular value decomposition.
- 2.
  Apply linear algebra techniques to solve least squares optimization problems in machine learning, including regression, classification, inversion, constrained problems, regularization, and model validation.
- 3.
  Organize and communicate the results of linear algebra computations in the context of machine learning applications, including visualizing data and identifying patterns and trends.
- 4.
  Design and analyse machine learning algorithms including dimensionality reduction techniques using singular value decomposition.
DASC 2110
Object-oriented Programming for Data Analytics in Science
3 Credit(s)
Prerequisite(s)
COMP 1021 OR COMP 1023
Exclusion(s)
COMP 2012, COMP 2012H
Description
Introduction to the fundamental concepts and techniques of object-oriented programming, including classes, objects, heritage, polymorphism, modularization and associations, etc.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain core programming concepts and produce effective programs to solve data analytics problems.
- 2.
  Explain Object-Oriented Programming (OOP) concepts, including the principles of classes, objects, encapsulation, inheritance, polymorphism, and abstraction, and produce effective programs based on these principles to solve data analytics problems.
- 3.
  Implement applications based on the graphical user interface (GUI) by leveraging OOP techniques and GUI libraries.
DASC 2210
A Survey on Big Data in Science and Society
1 Credit(s)
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Describe some examples in the field of Data Science.
- 2.
  Describe the data analytics workflow.
- 3.
  Assess how data science affects various aspects of science and society.
- 4.
  Apply suitable data analytics tools and algorithms to solve problems.
DASC 2220
Statistics and Probability for Data Analytics in Science
3 Credit(s)
Prerequisite(s)
(COMP 1021 OR COMP 1023) AND (MATH 1014 OR MATH 1020 OR MATH 1024)
Exclusion(s)
MATH 2411, IEDA 2520, IEDA 2540, ISOM 2500, LIFS 3150
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain fundamental statistical concepts, differentiate between population and sample, and apply different sampling techniques.
- 2.
  Demonstrate the ability to clean and prepare datasets for analysis using common tools such as Microsoft Excel, and understand the necessary steps in data processing.
- 3.
  Construct and analyze key descriptive statistics to summarize and describe datasets.
- 4.
  Construct and analyze confidence intervals for population parameters, explaining their significance in statistical inference, and formulate and test hypotheses using appropriate statistical methods.
- 5.
  Use Microsoft Excel functions related to statistics (e.g., AVERAGE, STDEV, COUNTIF, regression tools) to analyze data and perform statistical calculations.
- 6.
  Apply statistical methods to real-world problems, effectively using statistical reasoning to inform decision-making in various contexts.
DASC 3120
Data Structures for Data Analytics in Science
3 Credit(s)
Prerequisite(s)
DASC 2110
Exclusion(s)
COMP 2012, COMP 2012H
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Describe and implement various data structures.
- 2.
  Describe and implement various searching and sorting algorithms.
- 3.
  Apply data structures and algorithms to solve data analytics problems effectively and efficiently.
DASC 3230
Statistical Modeling and Machine Learning
3 Credit(s)
Prerequisite(s)
DASC 2020 AND DASC 2220
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain the assumptions, working principles, and trade-offs of data analytics techniques including regression, classification, dimension reduction, and clustering.
- 2.
  Execute basic data processing, model training, inferential and predictive analyses with the Python programming language.
- 3.
  Assess the performance of machine learning models based on the characteristics of the problem and dataset.
- 4.
  Design and implement complete data analysis workflows for real-world problems, including preprocessing, model selection, validation, and interpretation of results.
DASC 3240
Data Visualization in Science
3 Credit(s)
Prerequisite(s)
DASC 2220
Exclusion(s)
COMP 4462
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain the fundamental concepts of exploratory and explanatory data visualization.
- 2.
  Execute data processing for visualization using various data formats in R or Python.
- 3.
  Implement effective data visualizations using R or Python by considering design principles such as color usage, layout, format, and font.
- 4.
  Interpret and critically analyze various graphs.
- 5.
  Apply visualization techniques, including static figures, interactive figures, animations, and web applications, to effectively visualize data across various scientific disciplines.
DASC 3250
Numerical Methods for Data Analytics in Science
3 Credit(s)
Prerequisite(s)
(COMP 1021 OR COMP 1023) AND (DASC 2020 OR MATH 2111 OR MATH 2121 OR MATH 2131 OR MATH 2350)
Exclusion(s)
MATH 3312, MECH 4740, PHYS 3142
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain theories and key ideas of basic numerical algorithms for matrix computations, nonlinear equations, differential equations, interpolation, regression, integration and machine learning.
- 2.
  Apply numerical algorithms to solve computational problems related to data analysis, including solving linear and nonlinear system, dimensionality reduction, interpolation, regression, integration, classification and optimization.
- 3.
  Evaluate and analyze applicability and computational performance of basic numerical algorithms in data analysis applications.
DASC 4010
Practical Artificial Intelligence in Science
3 Credit(s)
Prerequisite(s)
(COMP 1021 OR COMP 1023) AND DASC 2020 AND DASC 2220
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain basic knowledge about the field of artificial intelligence (AI) and machine learning (ML), including the development of the field, search algorithms, probabilistic reasoning, supervised learning, and unsupervised learning.
- 2.
  Apply state-of-the-art Python AI/ML packages such as linear regression, logistic regression, support vector machine, principal component analysis, random forest, and neural networks to solve realistic problems.
- 3.
  Design frameworks for using AI techniques to solve specific scientific problems including hyperparameter tuning with validation dataset, data balance, bias and variance trade.
- 4.
  Implement Python programs to tackle scientific problems through class projects.
DASC 4020
Structured Query Language for Data Analytics
3 Credit(s)
Prerequisite(s)
COMP 1021 OR COMP 1023
Exclusion(s)
ISOM 3260, COMP 3311
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Describe the database design principles and produce effective database designs.
- 2.
  Implement effective SQL queries for data cleaning, retrieval, manipulation, and aggregation.
- 3.
  Apply data models in practical data analytics workflows.
DASC 4300
Capstone Project for Data Analytics in Science
3 Credit(s)
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Identify and describe a data-intensive field.
- 2.
  Develop a suitable data collection method.
- 3.
  Implement a comprehensive dashboard for performing descriptive analytics.
- 4.
  Apply suitable data-driven models for analysing data to identify problems and opportunities.
- 5.
  Evaluate data-driven insights, findings, and recommendations effectively through clear, structured, and professional oral presentations.
DASC 4400
Data Analytics in Information Science
3 Credit(s)
Prerequisite(s)
DASC 3250
Description
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.
Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.
  Explain mathematical theories, concepts and principles applied to the study of information science
- 2.
  Apply a logical and practical approach to execute tasks and solve problems in information science
- 3.
  Communicate mathematical concepts and methods effectively to a range of audiences, both orally and in writing

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