GEA1000 - Quantitative Reasoning with Data

Data Literacy Pillar:
For students admitted in Cohort AY2021/22 and later
The Data Literacy Pillar aims to provide students with the quantitative reasoning skills needed to make sense, and ask questions, of numerical data that addresses real-world issues across different domains. These skills include competencies in evaluating claims, and in data collection/sampling, cleaning, analysis and interpretation of large datasets.

GEA1000 Quantitative Reasoning with Data is the default course for this Pillar. It does not require advanced mathematics nor statistics. Instead, it emphasizes experiential learning through interesting real-world datasets. Thus, it is suitable for a general readership. Students however may instead choose to read a more advanced course, depending on their major of study, mathematical competency and interest. Advanced courses include ST1131 Introduction to Statistics and Statistical Computing, DSA1101 Introduction to Data Science, and BT1101 Introduction to Business Analytics. Certain programmes may also have their own prescribed courses to satisfy the Pillar.

Quantitative Reasoning Pillar:
For students admitted in Cohort AY2020/21 and earlier
GEA1000 is the successor course of GER1000. Thus, you will have to read GEA1000 for this Pillar if you have not read and passed GER1000.

GEA1000 - Quantitative Reasoning with Data

Course Description. This course aims to equip undergraduate students with essential data literacy skills to analyse data and make decisions under uncertainty. It covers the basic principles and practice for collecting data and extracting useful insights, illustrated in a variety of application domains. For example, when two issues are correlated (e.g., smoking and cancer), how can we tell whether the relationship is causal (e.g., smoking causes cancer)? How can we deal with categorical data? Numerical data? What about uncertainty and complex relationships? These and many other questions will be addressed using data software and computational tools, with real-world datasets.

Learning Outcomes. At the end of the course, students will learn, and gain competency in:

1. Evaluating/critiquing claims of a quantitative nature;
2. Practical data handling skills through the use of software;
3. Interpreting and sense-making of datasets, both small and large, through exploratory data analysis; and
4. Formulating meaningful questions, and employing simple statistical tests and models to find insightful answers

Delivery Mode. Blended learning. Students will watch pre-recorded video lectures and attend a 3-hour face-to-face tutorial fortnightly.

Assessment Mode. Online quizzes, class tutorials, project work, mid-term test, final exam

Other Courses.

The links to other courses are found here (ST1131, DSA1101, BT1101, IE1111R, DSE1101).

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