@@ -118,7 +118,9 @@ For this aim, I perfomed the following step:
...
@@ -118,7 +118,9 @@ For this aim, I perfomed the following step:
- Loaded the dataset from the Website Sentinelles
- Loaded the dataset from the Website Sentinelles
- Converted weekly data to dates and grouped it by epidemiological year (from September 1 to August 31)
- Converted weekly data to dates and grouped it by epidemiological year (from September 1 to August 31)
- Calculated the total incidence per epidemiological year
- 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**.
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.
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.
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@@ -126,15 +128,10 @@ In this project, I analyzed atmospheric CO₂ concentration data from the Mauna
...
@@ -126,15 +128,10 @@ In this project, I analyzed atmospheric CO₂ concentration data from the Mauna
The analysis included:
The analysis included:
- Importing the dataset from the Scripps CO₂ Program
- Importing the dataset from the Scripps CO₂ Program
- Visualizing the raw CO₂ time series to show seasonal oscillations and long-term trends
- Visualizing the raw CO₂ time series to show seasonal oscillations and long-term trends
- Performing seasonal decomposition to isolate and analyze monthly variations
- Performing seasonal decomposition to isolate and analyze monthly variations
-
- Building a linear regression model to estimate the long-term trend
- Building a linear regression model to estimate the long-term trend
-
- Forecasting CO₂ levels up to the year 2025
- Forecasting CO₂ levels up to the year 2025
- Computing yearly statistics (minimum, maximum, mean CO₂ per year)
- Computing yearly statistics (minimum, maximum, mean CO₂ per year)
All steps were carried out using Python 3 in a Jupyter notebook.
All steps were carried out using Python 3 in a Jupyter notebook.
