What Are the Best Books for Learning Data Analysis in Physics with Python?

In summary, a good reference book for data analysis techniques for science, particularly in physics, with programming applications and error analysis coverage is recommended. The book "Data Reduction and Error Analysis for the Physical Sciences" by Philip R. Bevington and D. Keith Robinson is a good resource for learning about data analysis and techniques such as Monte Carlo simulations. It is also useful for those working with large datasets, such as telescope arrays.
  • #1
PhotonSSBM
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I'm looking for a good reference book on data analysis techniques for science (namely physics) that has a lot of programming applications and some decent error analysis coverage.

Right now I'm analyzing large datasets of telescope arrays and would like something to learn from as this is my first big research project I've undertaken. So far I've mainly been working in python on a cluster running scripts to reorganize/consolidate the data and need to be able to do more stuff like monte carlo simulations, etc.

Thanks in advance.
 
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  • #2
For starters I can recommend
http://astro.cornell.edu/academics/courses/astro3310/Books/Bevington_opt.pdf
 

Related to What Are the Best Books for Learning Data Analysis in Physics with Python?

What is data analysis?

Data analysis is the process of collecting, organizing, and interpreting data to gain insights and make informed decisions. It involves using statistical and computational methods to identify patterns, trends, and relationships within a dataset.

What is error analysis?

Error analysis is the process of identifying and quantifying the errors or uncertainties present in a dataset or measurement. It involves evaluating the accuracy and precision of data and understanding the sources of error.

How are data analysis and error analysis related?

Data analysis and error analysis are closely related as error analysis is a crucial step in the data analysis process. By understanding and accounting for errors, the accuracy and reliability of the data analysis results can be improved.

What are some common methods used in data analysis?

Some common methods used in data analysis include descriptive statistics, inferential statistics, data visualization, and machine learning. These methods can help identify patterns, trends, and relationships within a dataset.

Why is data analysis important?

Data analysis is important because it allows us to make sense of large and complex datasets, identify trends and patterns, and make informed decisions based on data-driven insights. It is used in various fields such as science, business, healthcare, and education to improve processes and outcomes.

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