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

• 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

Week 12: Practical data science

• Decision trees
• Random forests
• Support vector machines
• Perceptron
• Time series (introduction)

Week 13: Deep Learning

• Intro to neural networks
• Convolutional neural networks
• Transformers

ostrich example

Week 14: Recommendation Systems

• Intro to recommendation systems
• Matrix
• Tensor, NN for recommendation systems

recomending movies recomending new songs make new product recommendations

Week 15: Learning break

Week 16-18: Capstone project

• Networks: important nodes and edges, clustering