Publications by Pablo Casas
Time Series Analysis Using Max/Min… and some Neuroscience.
Introduction Time series have maximum and minimum points as general patterns. Sometimes the noise present on it causes problems to spot general behavior. In this post, we will smooth time series -reducing noise- to maximize the story that data has to tell us. And then, an easy formula will be applied to find and plot max/min points thus character...
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Data Science Live Book (open source)
Hi! Well finally there is the first release of this project: A open source book which will hopefully contain some useful resources for those who want to learn some data analysis/machine learning. This release covers a little of data preparation, data profiling, selecting best variables (DataViz), assessing model performance, and coming soon a ca...
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Data Science Live Book – Scoring, Model Performance & profiling – Update!
This update contains a new chapter –scoring– which is related to model performance and model deployment, used when predicting a binary outcome. Link to the scoring chapter. Important: To use following updates please update funModeling package 🙂 install.packages("funModeling") Also related to predictive modelling for binary outcome, there ...
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Model Performance in Data Science Live Book
Hi there! I decided to almost re-write the model validation section since it didn’t reflect real case scenarios. Hopefully in the two new chapters you will gain a deeper knowledge on methodological aspects on model validation through classical cross-validation, bootstrapping, and going further in the nature of the error. And also take advantag...
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Playing with dimensions: from Clustering, PCA, t-SNE… to Carl Sagan!
Playing with dimensions Hi there! This post is an experiment combining the result of t-SNE with two well known clustering techniques: k-means and hierarchical. This will be the practical section, in R. But also, this post will explore the intersection point of concepts like dimension reduction, clustering analysis, data preparation, PCA, HDBSCAN...
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Data Science Live Book (open source) ~ new big release! 200-pages
Well after some time, and +300 commits, this is the biggest release of the Data Science Live Book! (open source), after the first publication more than 1 year ago 🙂 tl;dr: Hi there! I invite you to read the book online and/or download here. Thanks and have a nice day 🙂 !(tl;dr): An overview… It’s a book to learn data science, machine ...
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Data discretization made easy with funModeling
tl;dr: Convert numerical variables into categorical, as it is shown in the next image. ⏳ Reading time ~ 6 min. Let’s start! The package funModeling (from version > 1.6.6) introduces two functions— discretize_get_bins & discretize_df —that work together in order to help us in the discretization task. If you were using the 1.6.6, please se...
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Exploratory Data Analysis & Data Preparation with ‘funModeling’
funModeling quick-start This package contains a set of functions related to exploratory data analysis, data preparation, and model performance. It is used by people coming from business, research, and teaching (professors and students). funModeling is intimately related to the Data Science Live Book -Open Source- (2017) in the sense that most of...
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Exploratory Data Analysis in R (introduction)
Hi there! tl;dr: Exploratory data analysis (EDA) the very first step in a data project. We will create a code-template to achieve this with one function. Introduction EDA consists of univariate (1-variable) and bivariate (2-variables) analysis. In this post we will review some functions that lead us to the analysis of the first case. Step 1 – ...
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How to self-publish a book: A handy list of resources
Hello World! tl;dr: A list of useful resources aimed to self-publish a book on Amazon using Bookdown. Writing style Did I use any editor? Marketing How to create the book: Bookdown! Self-publishing on Amazon (Kindle and paperback) Costs and earnings Publishing outside Amazon: Gumroad Proofreading Cover ISBN Linking: B&W, color and Kindle on Amaz...
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