From b06457b6ae5024a613acd8e19e185de1b60d8270 Mon Sep 17 00:00:00 2001 From: f8dc60cab5180566667b00ce62a51ae7 Date: Tue, 24 Jun 2025 19:07:56 +0000 Subject: [PATCH] Update logbook.md --- journal/logbook.md | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/journal/logbook.md b/journal/logbook.md index df88e80..a4cc581 100644 --- a/journal/logbook.md +++ b/journal/logbook.md @@ -118,7 +118,9 @@ For this aim, I perfomed the following step: - Loaded the dataset from the Website Sentinelles - Converted weekly data to dates and grouped it by epidemiological year (from September 1 to August 31) - Calculated the total incidence per epidemiological year - + +## Exercise for evaluation in pair + Besides as part of *Mission 4*, I created a computational document by choosing **Subject 1: CO₂ concentration in the atmosphere since 1958**. In this project, I analyzed atmospheric CO₂ concentration data from the Mauna Loa Observatory, known as the **Keeling Curve**, covering the period from 1958 to the present. @@ -126,15 +128,10 @@ In this project, I analyzed atmospheric CO₂ concentration data from the Mauna The analysis included: - Importing the dataset from the Scripps CO₂ Program - - Visualizing the raw CO₂ time series to show seasonal oscillations and long-term trends - - Performing seasonal decomposition to isolate and analyze monthly variations -- - Building a linear regression model to estimate the long-term trend -- - Forecasting CO₂ levels up to the year 2025 - - Computing yearly statistics (minimum, maximum, mean CO₂ per year) All steps were carried out using Python 3 in a Jupyter notebook. -- 2.18.1