Academic Year 2021 - 2022
Semester 2
This module 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 data sets.
This module is taught via sectional teaching. It occurs every 2 weeks
Weekly quizzes are given and there are multiple attempts allowed. A lot of us scored full marks for this part of the assessment. We had to complete a midterm, project and final.
I really lost patience and hated this module with a passion. This module is too trivial for me so I totally did not care about this module. I was concurrently taking ST2334 and I felt that a lot of material taught in GEA1000 was repeated and not rigorous which lost my attention and appreciation for the module. Midterms went disastrous for me because of an invigilator who keeps distracting me by turning on her mic over zoom to call people out to turn on their camera every 2 minutes which led me to constantly lose my train of thoughts and redid the question. Hence I was really dissatisfied with my performance over the midterms. For the project, I pretty much carried most of the time because apparently I am the only one who knows what is going on. I even at the last minute, spotted a very glaring error and had to correct them on the spot. I pretty much gave up for the finals but it is not that hard as well. After the midterms, I was mentally prepared to SU this stupid module.