MS263: Data Analysis Techniques in Marine Science

Final project timeline

• April 16 - Project description (2 page written document describing scientific question and plan for analysis, no code)
• May 7 - Project draft (graded only on reproducibility, feedback given on all aspects)
• May 14 - Individual meetings with instructor & project feedback
• May 21 - Final project due, presentations (finals week)

Final project description

Project components:

The final project git repository should contain:

1. README.md file describing:

• Steps needed to run the analysis code on another computer
• Location of data and acknowledgement of source
• Dependencies
• Location of data in repository, or how to access data

2. Python tools

• A .py file (or files) that containing useful functions for analysis

3. Jupyter notebook (like a paper, but with code showing analysis)

• See this Mastering Markdown guide for tips on formatting markdown cells with headers, links, images and equations. This markdown syntax can also be used to format your README.md file.
• The Jupyter notebook should have the following sections:
  • Introduction
  • Description of methods and data sources
  • Results (including code, figures and descriptions)
  • Conclusions
  • Ideas for future work
  • References

Submission format:

• Git repository (see above)
• Presentation: 15 minutes + time for questions. Same sections as notebook except for code. References do not need to be in separate slide.

Grading criteria:

1. Reproducibility – another person should be able to run your code, and understand how it works [10 points]

• Detailed readme file (see above) [4 points]
• Git repository and Jupyter notebook contain all necessary components (see above) [4 points]
• Git repository is organized and does not contain unnecessary files (hint: use a .gitignore file ) [2 points]

2. Depth of scientific analysis [10 points]

• Raw data plotted to show variability in space and/or time [1 points]
• Visual comparisons show relationships between different variables [2 points]
• Calculations go beyond raw data (examples: quality control, subsets of data created, variables combined in model calculations) [4 points]
• Appropriate statistical analysis performed to address hypotheses (note: credit given for identifying correct approach, not statistical significance) [3 points]

3. Presentation: Professional delivery, uses effective graphics, and does not use unnecessary text. [5 points]
4. Demonstration that the topic is important (i.e. something that others will care about) [5 points]
5. Completeness of description of the scientific question being investigated [5 points]
6. Clarity of writing, logical progression of ideas [5 points]
7. Proper citation of outside references from peer-reviewed literature [5 points]
8. Clarity and relevance of information presented in figures [5 points]