https://github.com/jdvelasq/courses
Material de apoyo para cursos, Facultad de Minas, Universidad Nacional de Colombia
https://github.com/jdvelasq/courses
analytics big-data big-data-analytics data-science training-materials
Last synced: 3 months ago
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Material de apoyo para cursos, Facultad de Minas, Universidad Nacional de Colombia
- Host: GitHub
- URL: https://github.com/jdvelasq/courses
- Owner: jdvelasq
- License: mit
- Created: 2021-03-08T16:55:56.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2025-06-11T12:12:12.000Z (6 months ago)
- Last Synced: 2025-07-31T07:48:17.988Z (4 months ago)
- Topics: analytics, big-data, big-data-analytics, data-science, training-materials
- Language: Python
- Homepage: https://jdvelasq.github.io/courses/
- Size: 470 MB
- Stars: 18
- Watchers: 3
- Forks: 8
- Open Issues: 1
-
Metadata Files:
- Readme: readme.md
- License: LICENSE
Awesome Lists containing this project
README
Cambios al curso de Fundamentos de Analitica
===============================================================================
Week 01: Introducción a Analytics
Week 02: Programación en Python
Week 03: Pandas
Week 04: Ingesta y limpieza de datos
Week 05: Visualización de datos
Week 06: Fundamentos de estadística
* Descriptive Statistics
* Inferential Statistics
FIFA Worl Cup Analysis
Fitness product customer football analysis
Assesment: Movielens project
Week 07: Fundamentos de ML (gradiente)
Week 08: Validación cruzada y bootstrap
Week 09: Clustering
* EDA, PCA & t-SNE
* Clustering: k-means, dbscan, gaussian mixture
Genetic Codes
Finding themes in the project description
PCA identifying cases
Grouping news stories
Week 10: Machine learning
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* Introduction to supervised learning: regression
* Introduction to supervised learning: classification
Predicting wages
Gender wage gap
The effect of gun ownership on homicide rates
logistic regression the challenger disaster
Week 11: Learning break
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Week 12: Practical data science
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* Decision trees
* Random forests
* Support vector machines
* Perceptron
* Time series (introduction)
Week 13: Deep Learning
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* Intro to neural networks
* Convolutional neural networks
* Transformers
ostrich example
Week 14: Recommendation Systems
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* Intro to recommendation systems
* Matrix
* Tensor, NN for recommendation systems
recomending movies
recomending new songs
make new product recommendations
Week 15: Learning break
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Week 16-18: Capstone project
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* Networks: important nodes and edges, clustering