Master's Degree Projects

Description

The purpose of this repo is to bring together the main data science projects done during the Master's Degree in Data Science of the Universidad Autónoma de Madrid. You can find the Master's Degree web here.

In such way, the different projects are classified by the name of the subject. Also, as most of them are developed in a Jupyter Notebook, you can have a quick view of the technologies used checking out the following table:

Data Science Projects

Nº	Name of the subject	Project	Technology	Authors
1	Theory of Information	Calculation of mutual information between neural time series	Python	Ignacio Córdova Pou & Daniel Beteta Francisco
2	Advanced Methods in Statistics	Parametric estimation exercises	R	Daniel Beteta Francisco
3	Advanced Methods in Statistics	Non parametric estimation exercises	R	Daniel Beteta Francisco
4	Advanced Methods in Statistics	Regression exercises	R	Daniel Beteta Francisco
5	Advanced Methods in Statistics	Supervised learning exercises	R	Daniel Beteta Francisco
6	Advanced Methods in Machine Learning	Support vector machines task	Python	Mercedes García Villaescusa & Daniel Beteta Francisco
7	Advanced Methods in Machine Learning	Kernel principal components analysis task	Python	Mercedes García Villaescusa & Daniel Beteta Francisco
8	Advanced Methods in Machine Learning	Variable elimination and gradient boosting task	Python	Mercedes García Villaescusa & Daniel Beteta Francisco
9	Stochastic Processes	Stochastic processes in discrete time	Python	Daniel Beteta Francisco
10	Stochastic Processes	Stochastic processes in continuous time	Python	Ignacio Córdova Pou, Luís Sánchez Polo & Daniel Beteta Francisco
11	Stochastic Processes	Stochastic and ordinary differential equations	Python	Ignacio Córdova Pou, Luís Sánchez Polo & Daniel Beteta Francisco
12	Large Scale Data Processing	Introduction to pyspark	PySpark	Pablo López Perez & Daniel Beteta Francisco
13	Large Scale Data Processing	Football analysis with pyspark	PySpark	Pablo López Perez & Daniel Beteta Francisco
14	Large Scale Data Processing	Introduction to cuda computing	CUDA	Pablo López Perez & Daniel Beteta Francisco
15	Large Scale Data Processing	Introduction to quantum computing	Python	Pablo López Perez & Daniel Beteta Francisco
16	Data Management	Graphical data exploration	R	Pablo López Perez & Daniel Beteta Francisco
17	Data Management	Anonymization task	Python	Pablo López Perez & Daniel Beteta Francisco
18	Data Management	Movie data analysis	Python	Pablo López Perez & Daniel Beteta Francisco
19	Numerical Computation	Errors and approximations of functions	Python	Blanca Cano Camarero, Iker Villegas Labairu & Daniel Beteta Francisco
20	Numerical Computation	Application of monte carlo methods to bayesian computing	Python	Javier Irigoyen Muñoz, Santiago Monteso Fernández & Daniel Beteta Francisco
21	Deep Learning for Image Processing	Image Classification with PyTorch Simple CNN	PyTorch	Daniel Beteta Francisco
22	Deep Learning for Image Processing	Image Classification with PyTorch AlexNet	PyTorch	Daniel Beteta Francisco
23	Deep Learning for Image Processing	Image Classification with PyTorch Transfer learning	PyTorch	Daniel Beteta Francisco
24	Deep Learning for Biometric Information Processing	Facial Recognition Task	Python	Daniel Beteta Francisco
25	Reinforcement Learning	GridWorld with SARSA and Q-learning	Python	Ignacio Córdova Pou, Bruno Muñoz Marcos & Daniel Beteta Francisco
26	Reinforcement Learning	Contextual and Non-contextual Multiarmed Bandits	Python	Ignacio Córdova Pou, Bruno Muñoz Marcos & Daniel Beteta Francisco