MES 503 Research Methods Lab

Welcome to MES 503 Research Methods Lab

Fall 2024

No matter what brought you to marine sciences, you'll be dealing with numbers -- lots of them.

Our Goal

By the end of this course, you'll have the skills to:

  1. Design research projects
  2. Collect and analyze data
  3. Use practical and marketable tools in data science
  4. Plus, you'll leave with excellent reference materials (memorization is pointless, imo).

Philosophy

Unlike most coursework, grad school is all about self-learning, trial and error, and questioning everything.

So, if you ask me a question, my most likely response will be: "Look it up."

I won't give you the answers outright -- there are some things you need to discover on your own.

How to "Look It Up"

  • Check the lab manual: Thoroughly read the week's content and activity instructions.
  • Ask your peers: Science is collaborative. Maybe they caught something you missed. Note the honor code.
  • Use Google: Focus on Stack Overflow, Reddit, and R forums for programming and stats questions.
  • Come to my office hours: Bring specific questions after trying the above steps.
  • Avoid AI for now.

Additional Tips for Success

  • Look at your data: This is crucial for every analysis.
  • Make mental connections: Associate new concepts with what you already know.
  • Break things: Experiment with your code to understand its limits.
  • Talk it out: Discuss concepts with peers and professors.
  • Practice regularly: Reinforce your learning outside of lab time.

Lab Manual Structure and Functionality

This lab manual serves as your primary resource for MES 503. Its structure and features are designed to facilitate your learning and research process throughout the course.

Organization

The manual is organized into weekly chapters, each containing:

  • Lab content
  • Activities
  • Assignments (where applicable, to be submitted for grading)

R Integration

A key feature of this manual is its integration with R, which will become evident as we progress through the course. For example:

# Sample R code
data <- c(1, 2, 3, 4, 5)
mean_value <- mean(data)

This allows for easy visualization and testing of code concepts.

Technical Note

This manual is built using R Markdown and Quarto, demonstrating R's capability beyond data analysis. As you advance in the course, you'll gain appreciation for R's versatility in both analysis and documentation.

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