Demosense S.L.

Entepreneour, co-founder of the enterprise Demosense S.L.

Blank Space Dev

Co-founder of the association Blank Space Dev (2015-2016), a community focused on the divulgation of the most recent and interesting technologies for both professionals and students.

At the begining, we have mainly focused on web technologies like Jekyll, GitHub Pages, Flask, VR in Unity and Google Machine Learning APIs. Finally, the last talk was about Docker, a very extended and useful technology for pure software developers and Dev Ops.

exreport R package

Jacinto Arias and I developed an R package called exReport, which is included in the CRAN repository.

This package has been conceived to obtain robust and transparent reproducibility when developing scientific publishable results, allowing to describe experiments, generate tables, plots and carry out a descriptive statistic analysis.

Please visit the github repository to know more about the project and help us maintain it clean of bugs and errors via issues. Collaborate with us requesting new functionalities or adding them yourself via pull-requests. View it in GitHub.

Participation in Endesa Datathon

Nowadays, I am a contestant in the Endesa Datathon competition which started the 1st of February and ends the 30th of April 2016. As a summary, the competition consists on finding a solution benefit both parts, the company and the customers, using a data set of power consumptions from several customers (non real or at least manipulated data, in order to protect their privacy).

The starting date was the 6th of February, when the company celebrated the first meetup and announced the forty selected contestants to continue in this competition. As one of them, I am currently working on the project, whose scope is Data Analysis and Machine Learning.

Kaggle competition

Paricipation in Rossmann Store Sales competition in Kaggle as a member of the team SIMD.

Even if we did not win, nor even close to it, we gained a lot of valuable experience. Some of the tasks we did are study the problem, create new and more representative variables for the problem, plot different visualizations of the data and learn different ways to optimize parametrizations of the models we used for a particular problem (such as Random Forest, Logistic Regression and Convolutional Neural Networks).