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https://github.com/gmorlima/awesome-tera-dsml
A curated list of Tera Data Science & Machine Learning for Business Bootcamp
https://github.com/gmorlima/awesome-tera-dsml
List: awesome-tera-dsml
Last synced: about 1 month ago
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A curated list of Tera Data Science & Machine Learning for Business Bootcamp
- Host: GitHub
- URL: https://github.com/gmorlima/awesome-tera-dsml
- Owner: gmorlima
- Created: 2017-12-03T00:47:23.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2017-12-02T23:27:46.000Z (almost 7 years ago)
- Last Synced: 2024-04-22T15:08:51.158Z (5 months ago)
- Size: 15.6 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
- Contributing: contributing.md
- Code of conduct: code-of-conduct.md
Awesome Lists containing this project
- ultimate-awesome - awesome-tera-dsml - A curated list of Tera Data Science & Machine Learning for Business Bootcamp. (Other Lists / PowerShell Lists)
README
# awesome-tera-dsml [](https://github.com/sindresorhus/awesome)
> A curated list of references shared along the [Tera Data Science & Machine Learning for Business Bootcamp](http://somostera.com/cursos/data-science-for-business/)
## Contents
- [Aula 00: sample](#aula-00-sample)
- [Aula 03: Python](#aula-03-python)
- [Aula 06: Introduction to SQL with Python](#aula-06-introduction-to-sql-with-python)
- [Aula 07: Introduction to web scraping with Python](#aula-06-introduction-to-web-scraping-with-python)
- [Aula 27: Clustering](#aula-27-clustering)
- [Aula 28: Topic Analysis](#aula-28-topic-analysis)
## Aula 00: sample
Copy and paste this sample section. Follow the [Contribution Guidelines](contributing.md).
- [List item](http://example.com)
- [List item](http://example.com)
## Aula 03: Python
- [Anaconda](https://docs.anaconda.com/)
- [Jupyter Notebook Tutorial: Definitive Guide](https://www.datacamp.com/community/tutorials/tutorial-jupyter-notebook)
- [Jupyter Notebook Best Practices for Data Science](https://www.svds.com/tbt-jupyter-notebook-best-practices-data-science)
- [Which is the fastest growing programming language? Hint, it's not JavaScript](http://www.techrepublic.com/google-amp/article/which-is-the-fastest-growing-programming-language-hint-its-not-javascript)
- [Variables](https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-1-python-basics?ex=7)
- [Types](https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-1-python-basics?ex=11)
- [Type conversion](https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-1-python-basics?ex=12)
- [Lists & Types](https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-2-python-lists?ex=3)
- [List Grouping](https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-2-python-lists?ex=5)
- [Slicing and dicing](https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-2-python-lists?ex=9)
- [Built-in functions](https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-3-functions-and-packages?ex=2)
- [Multiple Arguments](https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-3-functions-and-packages?ex=4)
- [List Methods](https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-3-functions-and-packages?ex=7)
- [Import Package](https://campus.datacamp.com/courses/intro-to-python-for-data-science/chapter-3-functions-and-packages?ex=10)
- [Python Tutor](http://pythontutor.com/)
## Aula 06: Introduction to SQL with Python
- [Relational Databases Overview](https://www.tutorialspoint.com/sql/sql-rdbms-concepts.htm)
- [SQLite Tutorial](http://www.sqlitetutorial.net/)
- [Learn HTML in 15 Minutes](https://www.youtube.com/watch?v=Ggh_y-33Eso)
- [What is HTTP?](https://www.youtube.com/watch?v=SzSXHv8RKdM)
- [Differences Between Get and Post - Web Development](https://www.youtube.com/watch?v=UObINRj2EGY)
- [Learn to Program 8 : Reading / Writing Files](https://www.youtube.com/watch?v=EukxMIsNeqU)
## Aula 07: Introduction to Web Scraping with Python
## Aula 08: Numpy & Pandas
- [Numpy](http://www.numpy.org/
)
- [Numpy Quickstart](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html
)
- [Numpy Operations](http://www.scipy-lectures.org/intro/numpy/operations.html
)
- [Pandas](http://pandas.pydata.org/
)
- [10 Minutes to Pandas](https://vimeo.com/59324550
)
- [Pandas Documentation](http://pandas.pydata.org/pandas-docs/stable/
)
- [Normal Distribution](https://en.wikipedia.org/wiki/Normal_distribution
)
## Aula 27: Clustering
- [Getting your clustering right (Part 1)](https://www.analyticsvidhya.com/blog/2013/11/getting-clustering-right/)
- [Getting your clustering right (Part 2)](https://www.analyticsvidhya.com/blog/2013/11/getting-clustering-right-part-ii/)
- [Curso Udacity - Unsupervised Learning](https://classroom.udacity.com/courses/ud741)
- [Exemplo interessante de clustering de consumidores](http://www.ritchieng.com/machine-learning-project-customer-segments/)
## Aula 28: Topic Analysis
- [Conjunto de tutoriais sobre Topic Analysis](https://de.dariah.eu/tatom/index.html)
- [Comparação LDA, NMF e outros métodos](http://aclweb.org/anthology/D/D12/D12-1087.pdf)
- [Artigo original NMF](http://www.columbia.edu/~jwp2128/Teaching/E4903/papers/nmf_nature.pdf)
- [Artigo original LDA](http://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf)
- [LDA2VEC: Utilizar LDA com Word2Vec](https://www.datacamp.com/community/tutorials/lda2vec-topic-model#http://multithreaded.stitchfix.com/blog/2016/05/27/lda2vec/#topic=38&lambda=1&term=)
- [Implementing your own recommender systems in Python](https://cambridgespark.com/content/tutorials/implementing-your-own-recommender-systems-in-Python/index.html)
- [Sistema de recomendação vencedor do prêmio Netflix](https://www.netflixprize.com/assets/GrandPrize2009_BPC_BigChaos.pdf)
## Contribute
Contributions welcome! Read the [contribution guidelines](contributing.md) first.
## License
[](http://creativecommons.org/publicdomain/zero/1.0)
To the extent possible under law, Marco Antonio Gonzalez Junior has waived all copyright and
related or neighboring rights to this work.