Introduction

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 data sets.

~ NUSMods

Overview

During my time, this module was known as GER1000 and was taught in a similar way to H2 maths and ST2334 which I have taken during my university days.

Project

There was also a list of projects that we can take on, during my year it was the following topics:

  1. Fried food and death
  2. Low Impact sports bones
  3. Music on Cancer and pain
  4. Weekend catch-up sleep is a lie.
  5. Decision Fatigue

After we decided on a topic, we have to answer some questions

  1. Section 1
    1. What is/are the objectives and hypothesis/hypotheses of the study?
    2. In short, what is the main finding in the primary source?
    3. Who were the subjects studied? How many subjects were there? What sort of sampling scheme was employed?
    4. What were the eligibility criteria for inclusion in this study? Why were certain subjects excluded?
    5. What type of study is this? Is this a controlled experiment, or an observational study? If it’s the former, please answer questions in section 2(i) after completing section 1; if it’s the latter, please answer questions in section 2(ii) after completing questions in section 1. Answers to the questions should be based only on the articles provided. We want to see critical thinking, but do not expect you to possess domain specific knowledge of the topic.
  2. Section 2(i): Do this portion if the study is a controlled experiment.
    1. What characteristics were recorded before the start of the experiment?
    2. What interventions did the treatment and control groups receive?
    3. What are the main outcome measurements? How and when were they assessed?
    4. How were the subjects assigned to control and treatment groups? (E.g., state the method used to generate the random allocation sequence, type of randomisation, details of any restriction such as blocking and block size).
    5. Was treatment assignment blind to the subjects? How about the assessors?
  3. Section 2(ii): Do this portion if the study is an observational study.
    1. Is this a cohort study, case-control study, or some other design?
    2. Describe the data collection process, including periods of recruitment, exposure, and follow up.
    3. What are the main outcome measurements? How and when were they assessed?
    4. What confounders were controlled for by the researchers?

We were working in groups of 5 to 6 people.

Note: Do note that some students are only in the course to CS/CU it.

Exams

The final exam was a MCQ quiz about the statistics and the content which are taught. The content of the module was not very difficult. The format made the papers even easier as we can solve it by elimination as well. However, this resulted in a very skewed bell curve.

  • Expected Grade: A
  • Actual Grade: A-

Tutorials

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