https://github.com/memgonzales/lyrid-training
Compilation of the activities and projects given to probationary Lyrids of the Center for Complexity and Emerging Technologies (COMET) as partial requirement for promotion to cohort membership
https://github.com/memgonzales/lyrid-training
data-analytics data-cleaning data-science data-visualization jupyter-notebook
Last synced: 12 months ago
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Compilation of the activities and projects given to probationary Lyrids of the Center for Complexity and Emerging Technologies (COMET) as partial requirement for promotion to cohort membership
- Host: GitHub
- URL: https://github.com/memgonzales/lyrid-training
- Owner: memgonzales
- Created: 2021-03-26T08:56:25.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-22T15:28:39.000Z (over 3 years ago)
- Last Synced: 2025-03-13T15:35:44.043Z (over 1 year ago)
- Topics: data-analytics, data-cleaning, data-science, data-visualization, jupyter-notebook
- Language: Jupyter Notebook
- Homepage:
- Size: 25.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Training for Probationary Lyrids
![badge][badge-jupyter]
![badge][badge-python]
![badge][badge-pandas]
![badge][badge-numpy]
![badge][badge-scipy]
![badge][badge-plotly]
This is a compilation of the activities and projects given to probationary Lyrids of the Center for Complexity and Emerging Technologies (COMET) as partial requirement for promotion to cohort membership.
**The projects in this compilation feature interactive maps and HTML markup. Since GitHub does not support dynamic displays for notebooks, it is necessary to download the projects to render the elements and manipulate the data visualizations.**
## Task
The Term 2, Academic Year 2020-2021 training for the probationary Lyrids is inspired by the Tidy Tuesdays project of the R community. As an introduction to the practice of data science, they are assigned a weekly selection of datasets and tasked to create Jupyter notebooks that detail the step-by-step processes of data preparation, descriptive and exploratory data analysis, and data visualization.
The datasets that I worked on for this project are as follows:
Week No. | Dataset | Source
--- | --- | ---
1 | Novel Coronavirus 2019 Dataset | Kaggle
2 | 2018 Food Consumption and CO2 Emissions | Tidy Tuesdays (Github)
The complete specifications for the deliverables can be found in the document Lyrid-Training-Deliverables.pdf.
## Built Using
The Term 2, Academic Year 2020-2021 project consists of Jupyter notebooks, with the following Python libraries and modules used:
Libraries/Modules | Description | License
--- | ---| ---
re | Provides regular expression operations similar to those found in Perl | Python Software Foundation License
json | Provides functions for encoding and decoding JSON (JavaScript Object Notation) format | Python Software Foundation License
pandas | Provides functions for data analysis and manipulation | BSD 3-Clause "New" or "Revised" License
numpy | Provides a multidimensional array object, various derived objects, and an assortment of routines for fast operations on arrays | BSD 3-Clause "New" or "Revised" License
scipy | Provides efficient numerical routines, such as those for numerical integration, interpolation, optimization, linear algebra, and statistics | BSD 3-Clause "New" or "Revised" License
matplotlib | Provides functions for creating static, animated, and interactive visualizations | Matplotlib License (BSD-Compatible)
pywaffle | Provides functions for generating waffle charts | MIT License
plotly | Provides functions for creating front-end charts and visualizations for machine learning and data science | MIT License
Special instructions for packages that are not automatically bundled with an Anaconda installation (e.g., plotly) can be found in the pertinent notebooks.
*The descriptions are taken from their respective websites.*
## About COMET
*Taken from the official website: https://comet.dlsu.edu.ph/*
The Center for Complexity and Emerging Technologies (COMET) is an interdisciplinary research laboratory under the Advanced Research Institute for Informatics, Computing, and Networking (AdRIC) at De La Salle University, Manila, Philippines. Its mission is to enhance understanding of emergent phenomena in real-life systems, sociotechnical systems, and human-computer interactions. It develops computational models which are then used in the design of interactive tools, information systems, and interaction techniques.
## Author
- **Mark Edward M. Gonzales**
mark_gonzales@dlsu.edu.ph
gonzales.markedward@gmail.com
[badge-jupyter]: https://img.shields.io/badge/jupyter-%23FA0F00.svg?style=flat&logo=jupyter&logoColor=white
[badge-python]: https://img.shields.io/badge/python-3670A0?style=flat&logo=python&logoColor=white
[badge-pandas]: https://img.shields.io/badge/pandas-%23150458.svg?style=flat&logo=pandas&logoColor=white
[badge-numpy]: https://img.shields.io/badge/numpy-%23013243.svg?style=flat&logo=numpy&logoColor=white
[badge-scipy]: https://img.shields.io/badge/SciPy-%230C55A5.svg?style=flat&logo=scipy&logoColor=%white
[badge-plotly]: https://img.shields.io/badge/Plotly-%233F4F75.svg?style=flat&logo=plotly&logoColor=white