This course aims to give students an overview of the global trend of big data analytics and how big data analytics can be used in various disciplines. Students will learn big data analytics applications, and their values and influence in various disciplines through case studies. This course also encourages students to exchange their views from different disciplines through Interdisciplinary Group Discussions. On the practical side, students will acquire basic analytic, computational and software skills as well as gain hands-on experience in analyzing, interpreting, critiquing, visualizing and drawing conclusions from quantitative data in the context of various disciplines. After completing this course, students will develop the capability to evaluate the benefits and threats of big data analytics from an interdisciplinary perspective, and at the same time be able to apply generic quantitative methods together with computational and software skills to solve discipline-specific problems.

Analytic skills and computer skills are among the top essential attributes that employers seek on a candidate’s resume. This course aims to equip students with these skills that will be essential for future workplaces. This course adopts a project-based approach. Student will identify a daily life problem and go through the data analytic process in projects. Via the projects, students will learn how to collect data, arrange data into meaningful patterns, analyse data to provide useful insights, and present data in a clear and persuasive way so as to support sound reasoning and use data to convince their audience. Students will also learn related analytic skills, practical skills and software tools throughout the data analytic process. In all, on the knowledge side, this course aims to give students an overview of the role and importance of data in real life situations; introduce to students the basic data analytic methods; help students recognize the value and limitations of quantitative data; and raise students’ concern over the security and ethical issues in data handling. On the practical side, this course aims to equip students with practical and software skills to make use of quantitative data effectively to support sound reasoning in daily life and workplaces. After completing this course, students will be able to identify data requirements, use software tools to collect data, process and analyse data, visualize and present data for effective communication.

Students can learn how to make smart decisions through playing games. In every lecture, students will first play some games, explore different strategies in the games and see how well their strategies work. After that, they will be explained how different real life situations, including serious situations in economics, politics, business and even wars can be analysed similarly. Students will be exposed to a glimpse of Game Theory where they will learn how to analyse real life situations, make decisions, find the best response, and deal with dilemmas, etc. Further analysis includes studying greedy strategies, fairness, and efficiency, etc. After completing this course, students will be able to model simple real life situations, perform quantitative analysis and make rational decisions using Game Theory.

This course is intended to introduce the basic concept of computer algorithms and to teach several computational methods for quantitative reasoning. This course adopts a practical approach. Students are given tasks to acquire, process, and interpret data in different daily scenarios, and they will be guided to write computer codes to complete these tasks. They will use Matlab, a computer language simple enough for absolute beginners, yet widely adopted in various industries including machine learning, data analysis, and algorithmic trading in finance. The software is also used to generate plots and charts for data visualization and presentation. The focus of this course is the application of algorithmic methods and computational skills to solve daily life problems requiring quantitative reasoning.

No matter what the major of a student is, in his/her studies he/she will meet situations in which he/she needs to handle data. Actually, handling data can be simple, easy, and playful when it is done by computer software. This course introduces some simple methods of data analysis using computer software for small and large amounts of data. The students will use computer to carry out the procedures so that they can forget the formulae; the emphasis is on interpretation, explanation, argument, and reasoning. Only minimal theory behind the methods will be introduced for their interests.

This course aims to introduce students a basic understanding of quantitative reasoning behind making smart decisions to maximize our benefit in different situations, and to apply quantitative methods to understand and possibly resolve related current affairs. This course also focuses on applying mathematical tools to solve real-world problems. By exploring underlying mathematical idea, students will learn how to identify the objects to be maximized (or minimized) and clarify the notion “best” under various situations. One major tool to solve these problems is graph consisting of only vertices and edges, but other tools will also be introduced briefly. Interactive learning activities in the form of quizzes and group discussions, etc, form important parts in this course.

This course aims at unveiling the mask of data science using the simplest computing tool—Excel. Starting with a few well-known business and government cases, students will learn how data scientists identify patterns in data to build smart cities and make recommendations to their customers that finally turn into business value. The objective of this course is to build a bridge between non-tech people and data scientists by introducing how the data scientists identify real world problems in the specific areas of finance, healthcare, human resources, marketing, and government operations that can be solved by data; how they build a recommendation system to predict what their customers want; and how they inform strategic decisions using data visualization tools. This course also motivates students without programming and mathematical background to learn data science using Excel, and emphasizes on drawing insights from data analysis and delivering the message to the audience effectively.

This course aims to provide students with the practical tools that they need to make stock investment decisions on their own, and to inform the students that, as a stock investor, they actually own an equally divisible share of a business, which means the ownership of one share is the same as owning every share because the value is completely proportional. This course also demonstrates how a better understanding of the numbers on financial statements, together with the ability to conduct their own due diligence, are essential to assessing the true picture of the company. Industry variation in the financial statements will also be examined. This course also brings to the student’s awareness that the usefulness of the numbers on the financial statements can be enhanced if they are transformed into meaningful relationships—financial ratios in essence.