TODO: Be sure to replace the badge links to your own personal project links so they're up-to-date!
TODO: Write a good description for your data science project here.
If you want to highlight important ideas or concepts, be sure to play with the styling of this text.
Remember to create in-line links for any sources you're referencing, especially for any data sources you're using - whether it's from Kaggle, Reddit, the UCI Machine Learning Repository, Awesome Datasets on Github, or somewhere else entirely.
Be sure to never commit sensitive or large files/directories to your project repository. This is doubly true in the case of datasets, even if they are integral to your pipeline.
A better way to approach depicting the layout of your datasets is to upload zipped, curated datasets while visually displaying your overall project hierarchy.
A basic project hierarchy tree structure for this current repository is shown below.
ds-project-template-repository
│ README.md
│ LICENSE
│ .gitignore
│
└───datasets
│ │
│ └───external
│ │
│ └───interim
│ │
│ └───processed
│
└───models
│
└───notebooks
│ │ 01-exploratory-data-analysis.ipynb
│ │ 02-intermediate-data-processing.ipynb
│ │ 03-predictive-data-modeling.ipynb
│
└───production
│ │
│ └───data
│ │
│ └───models
│ │
│ └───visualizations
│
└───references
│
└───reports
│ │
│ └───figures
│
└───structures
│
TODO: List any external dependencies for your project here, including relevant in-line links as needed.
General dependencies, such as NumPy and Pandas, are listed below.
TODO: Replace these statements with your own credits and acknowledgements, or remove this section entirely!
Thanks to the Make School community of students and professionals seeking to learn software engineering and data science for real-world applications.
Special thanks to Alan Davis, Mike Kane, and Milad Toutounchian for teaching me modern data science and computer science skills to be able to both teach others and make an impact in the field of applied biology (my own personal domain-of-interest).
Credits to the Cookie Cutter project for inspiring me to create a similar structural tool for learning and training data science fellows at Make School to formulate better and more enriched projects without wasting too much time on hierarchical setup.
The content of this project itself and the source code used to format and display that content are both licensed under the MIT license.
TODO: Replace my name and in-line professional media account below with your own relevant info!
This project is constructed and maintained by Aakash Sudhakar.