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
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Introduction to supervised learning: regression
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Introduction to supervised learning: classification
Predicting wages Gender wage gap The effect of gun ownership on homicide rates logistic regression the challenger disaster
- Decision trees
- Random forests
- Support vector machines
- Perceptron
- Time series (introduction)
- Intro to neural networks
- Convolutional neural networks
- Transformers
ostrich example
- Intro to recommendation systems
- Matrix
- Tensor, NN for recommendation systems
recomending movies recomending new songs make new product recommendations
- Networks: important nodes and edges, clustering