In this post, we are going to discuss how to build your own blog with Julia and Franklin.jl, a popular static site generator among Julia users who create their own blogs or even build websites for tutorials. I hope that if you are reading this entry and you don’t have your own space, it can motivate you to build your own website. Some Reasons to Create Your Own Blog Blogs may sound old-fashioned, something created by people who are still living in the 90s, typing with passion about the political system while listening to Soundgarden in the background and drinking some kind of cheap beer… or programmers.

In this post, I will provide some notes on getting started with Neovim for a Data Science Workflow. This setup is not strictly related to Julia and can also be used with Python and R. The idea is to work with a double panel structure, where one side contains your code and the other side has the REPL, which receives the snippets of code you send from the code side.

Introduction In this post, I want to show an analysis of a time series that I’ve been working on. Usually, when dealing with time series, it is not so common to use machine learning algorithms (without at least trying more traditional models like the ARIMA family), but I still wanted to test how well a GBM model fits for these kinds of problems that are so popular. NOTE: I don’t recommend starting with models of this type for time series problems.

Introduction In this tutorial, I will dedicate myself to explaining how web scraping can be done from a platform where a dynamic interaction of the web application is required, this is quite useful when obtaining data from different links within the platform and where it is necessary a management scheme of the front-end components to carry it out. Here there are mainly two essential libraries, the first is selenium which corresponds to a framework that operates for multiple languages and serves to automate and control the browser, while Pandas for data manipulation will allow us to read data tables directly.

The purpose of this post is to show Julia as a language for data analysis and Machine Learning. Sadly Kaggle does not support Julia Kernels (hopefully, they will add it in the future). Therefore I wanted to take advantage of this space to show a reimplementation of Python/R Notebooks to Julia. In this context, I took data on happiness in countries in 2021 and some factors considered in this exciting survey.

Introduction The purpose of starting this blog is to create a space where I can write about topics that interest me related to technology, the world of quantitative methods for decision making, programming, among others. But why create a blog, if there are so many spaces out there that are more convenient than building one? The truth is, I wanted to have a more independent space and not too comfortable on third parties (nor benefit them), although there are spaces like Twitter (which seems to be in greater decline every day) or Medium, I simply thought that the best thing I could do is to have a personal space, and given that the dream of owning a house seems to be increasingly distant for my generation, well at least I can settle for having a blog, right?