# Course Logbook

_A record of notes, reflections, and completed activities throughout the course._

## Mission 3: Use of Jupyter and notebook

**Date:** June 17, 2025  
**Duration:** Approximately 2 hours  
**Directory:** `mooc-rr/module2/exo1`, `mooc-rr/module2/exo2`, `mooc-rr/module2/exo3`  
**Tools used:** Jupyter Notebook, GitLab, Matplotlib, NumPy

### What I did

- Reproduced a PDF file in order to use some approaches to evaluate the value of pi.
- Entered some daily data manually for analysis purposes.
- Computed basic statistics: mean, standard deviation, minimum, maximum, and median.
- Produced two visualizations: a sequence plot and a histogram.

### What I learned

- Analyzing and visualizing simple data using Python tools.
- Formatting plots clearly in a computational notebook.
- Pushing changes to GitLab during the task.
- Resolving Git conflicts involving Jupyter notebooks.

### Difficulties encountered

At the beginning of the task, I encountered a Git error while trying to push my notebook to GitLab. The error stated that my local branch and the remote branch had diverged, with conflicting changes in the file `toy_notebook_en.ipynb`. Since I had not made any meaningful edits locally, I chose to discard my local version and keep the one on GitLab.

### Notes

Once the Git issue was resolved, I was able to finish the task without further problems.

## Mission 4: Analysis of the incidence of influenza/chickenpox illness with a local copy of the data

**Date:** June 23, 2025  
**Duration:** Approximately 2 hours  
**Directory:** `mooc-rr/module3/exo1`, `mooc-rr/module3/exo2`  
**Tools used:** Jupyter Notebook, GitLab, Matplotlib, pandas, isoweek

### What I did

- Uploaded a local CSV file for analysis.  
- Cleaned the dataset by removing empty rows.  
- Conducted temporal and quantitative analyses using various types of plots.

### What I learned

- How to effectively clean and visualize data using different plot types.  
- How to upload and manage local files within the workflow.  
- Techniques for processing and manipulating data in tabular formats.

### Difficulties encountered

- For exercise 3.1, an error was found in the line:

`sorted_data['inc'].plot()`

- This happened because the 'inc' data were of type object. To fix this, a line of code was added before to convert the data to numeric:

`sorted_data['inc'] = pd.to_numeric(sorted_data['inc'])`

- For exercise 3.2, there was a problem in data processing because one year had 38 weeks.
The following change was made to the code:

`assert abs(len(one_year)-52) < 2` 

*to*

`assert abs(len(one_year)-52) < 2 or len(one_year) == 38`

### Notes

After these 2 problems were solved, I was able to finish the task without further problems.