Python as an analysis tool, not only a programming language: import CSV and Excel files, clean data, handle missing values, run statistics, produce charts and keep analyses reproducible, including for geology, biology and environmental science.
Who it is for : Students, researchers, scientists, engineers.
13 course(s) shown
Learn programming with Python: variables, conditions, loops, functions, files and first useful programs, with no prior knowledge.
Learn to import CSV and Excel files, clean data with Pandas, analyze it and produce charts with Matplotlib, in Jupyter notebooks.
Handle numerical arrays with NumPy: vectorized calculations, indexing, statistics and processing of measurement data.
Import, clean, filter, group and merge datasets with Pandas, from CSV and Excel files.
Create clear charts and figures for your analyses and publications: curves, histograms, scatter plots, formatting.
Work in notebooks: run code, document an analysis, share and reproduce your results.
Statistics, tests, optimization, interpolation and signal processing with SciPy, on examples from the sciences.
Analyze geological data: samples, coordinates, statistics and simple maps, with cleaning and visualization.
Analyze experimental and biological data: cleaning, statistics, group comparisons and visualization.
Analyze environmental measurement series: cleaning, statistics, trends and visualization.
Automate your calculations and the analysis of test and measurement data: import, processing, statistics and charts.
Import laboratory or field data, clean it, handle missing values, run statistics and produce reproducible figures with Python, Pandas, SciPy and Matplotlib.
Discover the principles of machine learning: data preparation, simple models, evaluation and interpretation with Python.
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