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MH3511 - Data Analysis with Computer

Lecture Clarity:
(4/5)
Content Relevance:
(3/5)
Content Difficulty:
(2/5)
Overall Workload:
(2/5)
Team Dependency:
(3/5)

Course Summary​

MH3511 is essentially practical statistics. It focuses on applying statistical techniques using R programming to real-world datasets. If you're also taking MH3500 (Statistics), you'll notice a lot of overlap. You can think of MH3500 as the theory and MH3511 as the practice. It's highly recommended to take both together as they complement each other very well and deepen your understanding of statistics.

The module covers applying statistical methods with R programming to analyze real datasets. The lectures are clear and straightforward, with Dr. Yue Mu explaining concepts in a beginner-friendly way. She is approachable and happy to answer questions after class.

Workload​

This is a relatively chill module with a light workload - approximately 1.5 to 2 hours per week spent mostly watching lectures (at 1.5x speed) and occasionally attending labs. Lab sessions reinforce lecture content with hands-on practice, and attendance gives bonus marks which can make a difference in a bell curve setting.

Because the assessments aren't very difficult, it can be harder to stand out — so if you want a high grade, you'll need to score near-perfect in all components.

Projects​

There are four main assessments:

  1. Quizzes (Short Tests): Open-book programming tests using R. You'll be given stats-related problems to solve with R code. No need to memorize syntax, but understanding the lecture content well is important as the quiz is time-limited.

  2. Written Report (Group Project): The most open-ended part of the module. You'll choose your own dataset and research question, and apply techniques taught in lectures to analyze it. This is your best chance to stand out and score well. Finding a good dataset and question that aligns with the lecture content is key, as you'll be graded on how effectively you apply what you've learned.

  3. Final Exam: Straightforward and fair. As long as you've followed the lectures and labs, you should be well prepared.

  4. Lab Attendance: Provides bonus marks.

Tips to Do Well​

  1. R programming knowledge isn't a prerequisite - the module is beginner-friendly if you've done Python before.

  2. Attend lab sessions for bonus marks.

  3. For the group project, focus on finding a dataset and research question that allow you to clearly demonstrate the techniques learned in class.

  4. If possible, take MH3500 (Statistics) alongside this module for better understanding of the theoretical concepts.

  5. Watch all lectures and follow along with the practical examples to ensure you can complete the quizzes within the time limit.

  6. This module is more about traditional statistics and hypothesis testing rather than machine learning, so it's particularly relevant for those interested in research or statistical analysis in industry.

Written by BAK