This analysis pretends to explain the student´s behavior while using a VLE. Learning digitally has been proven of tremendous help in keeping up with the necessary skills in the 21st century. It allows education to be more accessible, personalized and immersive. This project pretends to explore if applying Data Analysis to the student´s digital footprint allows us to better understand their behavior and act sooner to make better decisions that help them reach goals.
Many thanks to the data publicly provided by Open University 1 . You can access the Data Dictionary here.
Bearing that in mind, your job is to:
- Tell them what it is (with context).
- Show them what it looks like in action.
- Show them how they use it.
- Tell them any other relevant details.
Your readers will most likely view your README in a browser so please keep that in mind when formatting its content:
- Use proper format when necesary (e.g.:
import pandas as pd). - Categorize content using two or three levels of header beneath.
- Make use of emphasis to call out important words.
- Link to project pages for related libraries you mention. Link to Wikipedia, Wiktionary, even Urban Dictionary definitions for words of which a reader may not be familiar. Make amusing cultural references.
- Add links to related projects or services.
Last but not least, by writing your README soon you give yourself some pretty significant advantages. Most importantly, you’re giving yourself a chance to think through the project without the overhead of having to change code every time you change your mind about how something should be organized or what should be included.
Self-explanatory names are best. If the name sounds too vague or unrelated, it may be a signal to move on. It also must be catchy. Images, Logo, Gif or some color is strongly recommended.
Alpha, Beta, 1.1, Ironhack Data Analytics Final Project, etc... It's OK to write a sentence, too. The goal is to let interested people know where this project is at.
Having a one-liner that describes the pipeline/api/app is useful for getting an idea of what your code does in slightly greater detail.
Python, Pandas, Scipy, Scikit-learn, etc. Indicate the technological nature of the software, including primary programming language(s), main libraries and whether the software is intended as standalone or as a module in a framework or other ecosystem.
Why does it exist? Frame your project for the potential user. Compare/contrast your project with other, similar projects so the user knows how it is different from those projects. Highlight the technical concepts that your project demonstrates or supports. Keep it very brief.
Requeriments, prerequisites, dependencies, installation instructions.
Parameters, return values, known issues, thrown errors.
└── project
├── __trash__
├── .gitignore
├── .env
├── requeriments.txt
├── README.md
├── main_script.py
├── notebooks
│ ├── notebook1.ipynb
│ └── notebook2.ipynb
├── package1
│ ├── module1.py
│ └── module2.py
└── data
├── raw
├── processed
└── results
Do not forget to include
__trash__and.envin.gitignore
Next steps, features planned, known bugs (shortlist).
Credits, alternatives, references, license.
Getting help, getting involved, hire me please.
Here you have some repo examples:
- Mamba (OCR-Translator-Assistant)
- Art Classification
- OSNet-IBN (width x 1.0) Lite
- Movie Founder
- Convolutional Neural Network to detect Pneumonia
- Brain tumor detection project
- Policy-Gradient-Methods
Here you have some tools and references:
Footnotes
-
Kuzilek J., Hlosta M., Zdrahal Z. Open University Learning Analytics dataset Sci. Data 4:170171 doi: 10.1038/sdata.2017.171 (2017). ↩