Uploaded April 2021 | Updated September 2026, 2 weeks ago
The structure of Data Science project is one of primary. keys to project success in the business. You can not put your project files just randomly on any location in your project root folder. If you want that you Data Science (incl. Machine Learning, Deep Learning, or even Data Analysis) project be scalable, reproducible, and easy to collaborate to, then you should keep some project structural rules. This will let you distribute your project improvements works across your team members and to organize actions of development in more organized way.
With this video I introduce one of good example, how your Data Science project could be structured. The content of the video includes small steps, what I mentioned:
0:00 - Intro and the Idea of the video
0:40 - Project Licences
2:17 - Makefile
3:13 - README.md
3:59 - Data folder
6:23 - Models
7:19 - Jupyter Notebooks
8:18 - References
9:35 - Project Reports
10:57 - requirements.txt file
11:40 - setup.py file
12:53 - source (src folder) with vizualisation
14:52 - Conclusions and Thank you
Some references used in this video:
About Project Licencing: towardsdatascience.com/a-data-scientists-guide-to-open-source-licensing-c70d5fe42079
About Makefile: medium.com/@davidstevens_16424/make-my-day-ta-science-easier-e16bc50e719c
Example of Makefile: github.com/drivendata/cookiecutter-data-science/blob/master/%7B%7B%20cookiecutter.repo_name%20%7D%7D/Makefile
About README.md file: towardsdatascience.com/how-to-write-an-awesome-readme-68bf4be91f8b
I hope this content was useful for you. Subscribe the channel to get more fresh and useful content for your Data Science and Machine Learning/Deep Learning projects!
Vytautas.
#pythonproject #readmefile #datasciencebusiness
The structure of Data Science project is one of primary. keys to project success in the business. You can not put your project files just randomly on any location in your project root folder. If you want that you Data Science (incl. Machine Learning, Deep Learning, or even Data Analysis) project be scalable, reproducible, and easy to collaborate to, then you should keep some project structural rules. This will let you distribute your project improvements works across your team members and to organize actions of development in more organized way.
With this video I introduce one of good example, how your Data Science project could be structured. The content of the video includes small steps, what I mentioned:
0:00 - Intro and the Idea of the video
0:40 - Project Licences
2:17 - Makefile
3:13 - README.md
3:59 - Data folder
6:23 - Models
7:19 - Jupyter Notebooks
8:18 - References
9:35 - Project Reports
10:57 - requirements.txt file
11:40 - setup.py file
12:53 - source (src folder) with vizualisation
14:52 - Conclusions and Thank you
Some references used in this video:
About Project Licencing: towardsdatascience.com/a-data-scientists-guide-to-open-source-licensing-c70d5fe42079
About Makefile: medium.com/@davidstevens_16424/make-my-day-ta-science-easier-e16bc50e719c
Example of Makefile: github.com/drivendata/cookiecutter-data-science/blob/master/%7B%7B%20cookiecutter.repo_name%20%7D%7D/Makefile
About README.md file: towardsdatascience.com/how-to-write-an-awesome-readme-68bf4be91f8b
I hope this content was useful for you. Subscribe the channel to get more fresh and useful content for your Data Science and Machine Learning/Deep Learning projects!
Vytautas.
#pythonproject #readmefile #datasciencebusiness
