cookiecutter data science

We have hosted the application cookiecutter data science in order to run this application in our online workstations with Wine or directly.

Quick description about cookiecutter data science:

A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking about bikeshedding the indentation aesthetics or pedantic formatting standards, ultimately, data science code quality is about correctness and reproducibility. It's no secret that good analyses are often the result of very scattershot and serendipitous explorations. Tentative experiments and rapidly testing approaches that might not work out are all part of the process for getting to the good stuff, and there is no magic bullet to turn data exploration into a simple, linear progression.

Features:

• Collaborate more easily on analysis
• Learn from your analysis about the process and the domain
• Feel confident in the conclusions at which the analysis arrives
• Use a package to load variables automatically
• AWS CLI configuration
• Be conservative in changing the default folder structure

Programming Language: Python.
Categories: