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GEA1000 Course Review

Introduction

  • Full name: GEA1000 Quantitative Reasoning with Data
  • Target audience: NUS Year 1 CDE / FoS Students
  • Purpose of the course: To train students to interpret and evaluate data in a critical and informed manner.
  • Notes Structure: View the GEA1000 Notes & Cheatsheets
    1. GEA1000-Notes: My personal study notes for the course
    2. GEA1000-Cheatsheets: One-page summaries prepared for quick revision before the final

I took this course in AY24/25 Semester 2 to fulfill my degree requirement.

Course Content

Overview of Topics Covered

  1. Getting Data
    • Exploratory Data Analysis
    • Sampling
    • Variables and Summary Statistics
  2. Categorical Data Analysis
    • Rates
    • Association
    • Two rules on rates
    • Simpson's Paradox
    • Confounders
  3. Dealing with Numerical Data
    • Univariate Data
    • Bivariate Data
  4. Statistical Inference
    • Confidence Interval
    • Hypothesis Testing

Depth and Balance of Coverage

As a common interdisciplinary course, GEA1000 places a stronger emphasis on conceptual understanding and statistical reasoning rather than programming or implementation. Most of the practical work is done using Excel and Radiant, instead of industry-standard tools like Python.

While this makes the course very accessible to students from all backgrounds, those who already have experience with Python, pandas, or formal data science pipelines may find the technical depth to be somewhat limited.

Teaching Style and Materials

Teaching Style

Tutorial

This course has once-every-two-weeks tutorials. My tutor was Mr. Seah Zong Long. His teaching was clear and met the expectations of the course, though personally I did not have particularly high expectations for this module. The tutorials mainly focused on going through concepts and practicing the use of Excel and Radiant for data analysis.

Finals

The final exam was not very difficult. As long as you pay close attention to the context of each question, understand the meaning behind the formulas, and memorize the standard procedures for calculations, the paper is very manageable.

Course Book

Textbook: The textbook is written by the GEA1000 teaching team themselves, and it is actually quite clear, structured, and beginner-friendly. It aligns closely with the lecture content and is sufficient for exam preparation.

Learning Experience

For me, this is a fairly average common course at NUS. Since I had already learned SC1015 at NTU, I found the content of GEA1000 to be more theory-based than necessary. Personally, I would have preferred to see more emphasis on Python and pandas for real data handling.

Workload and Time Management

  • Level of Difficulty: 5/10
  • Tips for Future Students: The final exam only allows a one-page cheatsheet, so you should start preparing it early. Otherwise, you can also directly use mine 😄

Conclusion

Overall, GEA1000 is a very typical common curriculum course. It may not be particularly exciting for students with prior data or programming experience, but it does serve its purpose in building basic statistical literacy for Year 1 students across different faculties.

Since it is a graduation requirement, my advice is simple: just get it done can already.

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