Python for Science / Basics
Python for Science / Basics is a complete set of tutorials to learn the basics of Python programming for science purpose.
The GitHub repository of this project : Python for Science / Basics
Programming is not a goal
Writing very fast sentences in a new language is not enough to claim mastery of that language. If you are unable to write a sequence of sentences that make sense and address a specific issue, then the efforts to learn the language will be in vain.
Because scientists generate, process, and need to visualize data, learning a new programming language is just a step to help them make faster analyses and automate certain tasks.
Warning
You should always write code assuming that someone else may read it, and understand it !
What a scientist needs ?
Scientists require data analysis for several reasons:
Interpretation: Data analysis helps scientists make sense of the vast amounts of data they collect during experiments or observations. It allows them to extract meaningful information, patterns, and trends from raw data.
Hypothesis Testing: Scientists often formulate hypotheses based on their observations or theories. Data analysis allows them to test these hypotheses rigorously by examining the evidence in the data.
Validation: Data analysis is crucial for validating research findings. By analyzing data using statistical methods, scientists can determine the reliability and significance of their results.
Visualization: Data analysis often involves data visualization techniques to represent complex data in a visual format. Visualization makes it easier for scientists to communicate their findings effectively and identify trends or outliers in the data.
Publication: Rigorous data analysis is a fundamental aspect of scientific publications. Peer-reviewed journals require researchers to provide detailed analyses of their data to support their conclusions and ensure the reproducibility of their findings.
What is a good programmer ?
A “good programmer” typically refers to someone who demonstrates proficiency in writing efficient, reliable, and maintainable code to solve problems effectively. Some characteristics of a good programmer include:
Strong problem-solving skills: They can analyze problems and devise effective solutions using their programming knowledge and logical thinking.
Proficiency in programming languages: They have a strong command of one or more programming languages and understand their syntax, semantics, and best practices.
Ability to write clean and maintainable code: They write code that is easy to understand, well-organized, and properly documented, making it easier for others to read and maintain.
Attention to detail: They pay attention to small details in their code, such as variable names, formatting, and error handling, to ensure correctness and reliability.
Testing and debugging skills: They know how to test their code thoroughly, identify bugs, and debug issues efficiently to ensure the quality and reliability of their software.
Python for Science
Basics for Science
Signal processing
http://lense.institutoptique.fr/mine/python-recuperation-de-donnees-dans-un-fichier/
http://lense.institutoptique.fr/mine/python-convertir-une-image-en-matrice/
http://lense.institutoptique.fr/mine/python-comparatif-calcul-matriciel-et-calcul-iteratif/