2019-BigData_master

The whole set of essays produced during each master's subject

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I have tried to organize and show the work that I have done during a Big Data course at the Universidad Complutense in Madrid.

Warning: All the essays available here are written in Spanish!

Big Data & Business Analytics

This master course consisted in 10 h/week of in-person classes, and more than 20 modules that took between 1 and 6 weeks. Not all these modules have asked for a final essay about its subject, but most of them did, so here I have decided to present all of them. Modules that won’t be shown:

1. Introduction - Business Intelligence
2. Python
3. DataScience workflow
4. Text Mining
5. Open Data
6. Distributed Computation - Spark
7. DevOps

SQL Databases:

• Entity - relationship diagram, from scratch.
• Run an SQL Server on your machine.
• Create your own SQL database.
• From SELECT * to User Defined Functions.
• Normalization and normal forms.

During this module I was still using Latex to make my documents, and compiling them to pdf.

DOWNLOAD FINAL ESSAY

NoSQL Databases:

• Document-based databases as an alternative.
• Databases theory. CAP diagram.
• Mongo. How does it work?.
• Run Mongo server. Cloud & local.
• Mongo syntax.

During this module I was still using Latex to make my documents, and compiling them to pdf.

DOWNLOAD FINAL ESSAY

Google Cloud Computing:

• Cloud-based computing. SaaS, PaaS and IaaS.
• Google Cloud main services.
• Google Cloud Big Data services.
• Big Data codelabs.
• Real life examples.

During this module I was still using Latex to make my documents, and compiling them to pdf.

DOWNLOAD FINAL ESSAY

R:

• Introduction. Statistical language.
• Data structures. How to manipulate them.
• Libraries. How to load and understand them.
• Functions. Use and define. Plotting tools.
• Further steps.

I decided to start using Rmarkdown to generate my documents, and compile them to html (if allowed).

VISIT FINAL ESSAY

Statistics:

• Descriptive Statistics. Types of variables.
• Centrality, dispersion and shape measures.
• Bidimensional distributions. Correlation.
• Usual probability distributions. Confidence intervals.
• Hypothesis testing.

I used Rmarkdown and compile the essay to html.

VISIT FINAL ESSAY

Lineal and Logistic regressions:

• Data understanding.
• Test for independence.
• Lineal regression.
• Logistic regression.
• Variable selection.

I used Rmarkdown and compile the essay to html.

VISIT FINAL ESSAY

Principal Component Analysis:

• Multivariational data techniques.
• Theory.
• Application with R.
• Visualization with R.
• Interpretation.

I used Rmarkdown and compile the essay to html.

VISIT FINAL ESSAY

Timeseries:

• Descriptive Initial Analysis. Timeseries decomposition.
• Correlation vs causality. Cointegration test.
• Univariate Dynamic Models: ARIMA.
• Dynamic Models with static regressors: ARIMAX.
• Multivariate Dynamic Linear Models: VAR.

I used Rmarkdown and compile the essay to html.

VISIT FINAL ESSAY

Machine Learning:

• Artificial Neural Networks.
• Tree-based models. Decision trees - bagging - random forest - gradient boosting.
• Support Vector Machines.
• Ensemble techniques.
• Deep Learning.

I used Rmarkdown and compile the essay to pdf.

DOWNLOAD FINAL ESSAY

Competition:

This module make us participate in this competiton in teams of 5 or 6 students, and apply the agile workflow to get the best model.

I used Rmarkdown and compile the essay to html.

VISIT FINAL ESSAY

Graph Theory:

• Application fields.
• Degree distribution and categorization.
• Distance analysis.
• Centrality measures. Closeness and Betweenness.
• Closest nodes and paths. Dijkstra algorithm.

I used Rmarkdown and compile the essay to pdf.

DOWNLOAD FINAL ESSAY

Final Project:

Last module was a collaboration with San Carlos Hospital to make 3 ML models that detects reentries, duration of the hospitalisation and type of hospital discharge. We made it in groups of 6 students.

I used Rmarkdown and compile the essay to html. I have ommited some content.

VISIT FINAL ESSAY