Jupyter Notebook & Google Colab
Work in notebooks: run code, document an analysis, share and reproduce your results.
- Level
- Beginner
- Duration
- 3–4 hours
- Audience
- Students, Researchers, Scientists…
- Format
- In person
- Location
- Marrakech
Import laboratory or field data, clean it, handle missing values, run statistics and produce reproducible figures with Python, Pandas, SciPy and Matplotlib.
Python applied to research data: import experimental or field measurements, clean the data, handle missing values, run statistics, produce clear figures and keep analyses reproducible. Examples from geology, biology and environmental science.
Tools : Python NumPy Pandas SciPy Matplotlib Jupyter
01 — Python and Jupyter / Colab
Working environment and language basics.
02 — Scientific data with NumPy and Pandas
Data structures, import, selection.
03 — Data cleaning and quality
Missing values, outliers, formats.
04 — Statistics with SciPy
Descriptive statistics, comparisons, simple tests.
05 — Scientific figures
Readable charts for a paper or a thesis.
06 — Reproducibility
Organize and document an analysis so it can be redone and shared.
No programming experience is mandatory. Basic statistics notions are useful.
80% practice / 20% theory
📍 Marrakech — in-person training in a training room provided by a partner school. The exact address is given for each session.
Dates to be confirmed
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Work in notebooks: run code, document an analysis, share and reproduce your results.
Import, clean, filter, group and merge datasets with Pandas, from CSV and Excel files.
Statistics, tests, optimization, interpolation and signal processing with SciPy, on examples from the sciences.