khanacademy: Distribuição Normal
Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, videos
Boas aulas de video em pt sobre a distribuição normal padrão
Vídeo original: ck12.org Exercise: Standard Normal Distribution and the Empirical Rule(http://www.khanacademy.org/video/ck12-org-exercise–standard-normal-distribution-and-the-empirical-rule) A Khan Academy Portugal disponibiliza explicações online de Matemática gratuitas desde o 1º até ao 12º ano de escolaridade. Este vídeo foi produzido pela Khan Academy e traduzido para português pela Fundação Portugal Telecom (ver todos os vídeos disponíveis em http://fundacao.telecom.pt/kha
Tags: inferência
khanacademy – aulas de probabildade
Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, videos
Boas aulas de video em pt sobre probabilidade
Vídeo original: Compound Probability of Independent Events (https://www.khanacademy.org/math/trigonometry/prob_comb/independent_events_precalc/v/compound-probability-of-independent-events) A Khan Academy Portugal disponibiliza explicações online de Matemática gratuitas desde o 1º até ao 12º ano de escolaridade. Este vídeo foi produzido pela Khan Academy e traduzido para português pela Fundação Portugal Telecom (ver todos os vídeos disponíveis em http://fundacao.telecom.pt/khanac
Tags: inferência
Resources for Getting Started with R
Posted by Armando Brito Mendes | Filed under estatística, software
Recursos para começar a usar o R
Resources for Getting Started with R
R, the open source statistical software environment, is powerful but can be a challenge to approach for beginners. For me, the best way to learn R, especially on the visualization side of things, is to dive right in. Grab some data and make some charts, or better yet, find a graph you like and try to replicate it.
R core functionality and the many available packages let you do a lot without having to know what’s going on underneath. I use this approach in Visualize This and the tutorials around here. I like the satisfaction of immediate results. Then I learn the nitty gritty later.
That said, it doesn’t hurt to familiarize yourself with the environment. Also, visualization is a small part of what you can do with R, so it can help to know what else you can do analysis-wise.
Tags: data mining, R-software, software estatístico
An intro to R for new programmers
Posted by Armando Brito Mendes | Filed under estatística, software
Introdução às estruturas de dados no R
Following the lead of JavaScript for Cats by Maxwell Ogden, Scott Chamberlain and Carson Sievert wrote R for Cats. It’s a playful introduction to R intended for those who have little to no programming experience.
The bulk of it so far is a primer on data structures, and there’s a little bit on functions and some dos and don’ts. It’s stuff you should know before you get into more advanced tutorials.
Mainly though: ooo look, kitty.
Once you’re done with that (It only takes about 30 minutes.), there are lots of other resources for getting started with R.
Tags: data mining, R-software, software estatístico
Create a barebones R package from scratch
Posted by Armando Brito Mendes | Filed under estatística, software
Criar pacotes para o R é muito fácil
While we’re on an R kick, Hilary Parker described how to create an R package from scratch, not just to share code with others but to save yourself some time on future projects. It’s not as hard as it seems.
This tutorial is not about making a beautiful, perfect R package. This tutorial is about creating a bare-minimum R package so that you don’t have to keep thinking to yourself, “I really should just make an R package with these functions so I don’t have to keep copy/pasting them like a goddamn luddite.” Seriously, it doesn’t have to be about sharing your code (although that is an added benefit!). It is about saving yourself time. (n.b. this is my attitude about all reproducibility.)
I need to do this. I’ve been meaning to wrap everything up for a while now, but it seemed like such a chore. Sometimes I’d even go back to my own tutorials for some copy and paste action. Now I know better. And that’s half the battle.
Tags: data mining, R-software, software estatístico
Using R in Nonparametric Statistical Analysis
Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, software
Blog com vários tutoriais para usar estatísticas não paramétricas simples em R
- Using R in Nonparametric Statistical Analysis: The Kruskall-Wallace Test for One-Way Analysis of Variance
- Using R in Nonparametic Statistical Analysis: The Binomial Sign Test
Tags: desnvolvimento de software, R-software, software estatístico
Warm and cold weather anomalies
Posted by Armando Brito Mendes | Filed under visualização
Mais um exemplo de boas visualizações, agora com dados de clima
This year’s polar vortex churned up some global warming skeptics, but as we know, it’s more useful to look at trends over significant spans of time than isolated events. And, when you do look at a trend, it’s useful to have a proper baseline to compare against.
To this end, Enigma.io compared warm weather anomalies against cold weather anomalies, from 1964 to 2013. That is, they counted the number of days per year that were warmer than expected and the days it was colder than expected.
An animated map leads the post, but the meat is in the time series. There’s a clear trend towards more warm.
Since 1964, the proportion of warm and strong warm anomalies has risen from about 42% of the total to almost 67% of the total – an average increase of 0.5% per year. This trend, fitted with a generalized linear model, accounts for 40% of the year-to-year variation in warm versus cold anomalies, and is highly significant with a p-value approaching 0.0. Though we remain cautious about making predictions based on this model, it suggests that this yearly proportion of warm anomalies will regularly fall above 70% in the 2030’s.
Explore in full or download the data and analyze yourself. Nice work. [Thanks, Dan]
High-detail maps with Disser
Posted by Armando Brito Mendes | Filed under mapas SIG's, software, visualização
Software open source para trabalhar com mapas
Open data consultancy Conveyal released Disser, a command-line tool to disaggregate geographic data to show more details. For example, we’ve seen data represented with uniformly distributed dots to represent populations, which is fine for a zoomed out view. However, when you get in close, it can be useful to see distributions more accurately represented.
If the goal of disaggregation is to make a reasonable guess at the data in its pre-aggregated form, we’ve done an okay job. There’s an obvious flaw with this map, though. People aren’t evenly distributed over a block — they’re concentrated into residential buildings.
So Disser combines datasets of different granularity, so that you can see spreads and concentrations that are closer to real life.
Tags: belo, image mining, mapas
Why use R? Five reasons
Posted by Armando Brito Mendes | Filed under materiais para profissionais, software
Bom blogue, as principais razões para usar R
Why use R? Five reasons.
In this post I will go through 5 reasons: zero cost, crazy popularity, awesome power, dazzling flexibility, and mind-blowing support. I believe R is the best statistical programming language to learn. As a blogger who has contributed over 150 posts in Stata and over 100 in R I have extensive experience with both a proprietary statistical programming language as well as the open source alternative. In my graduate career I have also had the opportunity to experiment with the proprietary software SPSS, SAS, Mathematica, as well as MPlus.
Tags: big data, definição, R-software, software estatístico
9 “must read” articles on Big Data
Posted by Armando Brito Mendes | Filed under estatística, materiais para profissionais, visualização
Textos para big data
My selection
- Big Data – From Descriptive to Prescriptive
- Can big data be racist?
- NodeXL Graph Gallery: Graph Details
- Best Metrics For Digital Marketing: Rock Your Own And Rent Strategies
- Big Data: from mining to meaning
- Beautiful versus useful visualizations (in French, but interesting)
- Learning and Teaching Machine Learning: A Personal Journey
- Big data techniques and technologies
- The Sexiest Job of the 21st Century is Tedious, and that Needs to C… (*)
- From the trenches: 360-degree data science
(*) I disagree with this Harvard Business Review author. Senior data scientists work on high level data from various sources, use automated processes for EDA (exploratory analysis) and spend little to no time in tedious, routine, mundane tasks (less than 5% of my time, in my case). I also use robust techniques that work well on relatively dirty data, and … I create and design the data myself in many cases.
Tags: belo, big data, data mining, Estat Descritiva, grafos
