SNS Trabalhadores por Grupo Profissional
Posted by Armando Brito Mendes | Filed under data sets
Bons dados sobre o SNS
Apurar o nº de trabalhadores (empregos), por instituição e por grupo profissional, com contrato de trabalho ativo no mês de análise.
Número de trabalhadores (empregos) com contrato de trabalho ativo no mês em análise, por entidade e por mês, discriminado pelos grupos profissionais: Médicos (sem contabilizar Internos), Médicos Internos, Enfermeiros, Técnicos Superiores de Saúde, Técnicos Superiores de Diagnóstico e Terapêutica, Assistentes Técnicos, Assistentes Operacionais, Técnicos Superiores, Informáticos e Outros.
Nota: Os dados apresentados dizem respeito aos trabalhadores vinculados com contrato de trabalho às entidades do setor público administrativo (SPA) e entidades públicas empresarias (EPE) que se encontram sob a tutela do Ministério da Saúde, aos quais acrescem ainda os profissionais que exercem funções nos estabelecimentos hospitalares em regime de parceria público-privada integrados no Serviço Nacional de Saúde. (Ver anexo – Número de Profissionais nos Estabelecimentos Hospitalares em Regime de Parceira Público-Privada).
The 5 Computer Vision Techniques
Posted by Armando Brito Mendes | Filed under lições, materiais ensino
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Boa introdução ao tema da visão por computador
The 5 Computer Vision Techniques That Will Change How You See The World
Computer Vision is one of the hottest research fields within Deep Learning at the moment. It sits at the intersection of many academic subjects, such as Computer Science (Graphics, Algorithms, Theory, Systems, Architecture), Mathematics (Information Retrieval, Machine Learning), Engineering (Robotics, Speech, NLP, Image Processing), Physics (Optics), Biology (Neuroscience), and Psychology (Cognitive Science). As Computer Vision represents a relative understanding of visual environments and their contexts, many scientists believe the field paves the way towards Artificial General Intelligence due to its cross-domain mastery.
So what is Computer Vision?
Tags: data mining, machine learning, robot
Basketball Stat Cherry Picking
Posted by Armando Brito Mendes | Filed under estatística, visualização
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Deep into the NBA playoffs, we are graced with stats-o-plenty before, during, and after every game. Some of the numbers are informative. Most of them are randomly used to illustrate a commentator’s point.
One of the most common stats is the conditional that says something like, “When player X scores at least Y points, the team wins 90 percent of their games.” It implies a cause-and-effect relationship.
The Cleveland Cavaliers won the most games when LeBron James scored 30 or more points. So James should just score that many points every time. Easy. I should be a coach.
It’s a bit of stat cherry picking, trying to find something in common among games won. So to make things easier, and for you to wow your friends during the games, I compiled winning percentages for several stats during the 2017-18 regular season. Select among the star players still in the playoffs.
Tags: análise de dados
50 Great Examples of Data Visualization
Posted by Armando Brito Mendes | Filed under visualização
Bons exemplos de representações gráficas
Wrapping your brain around data online can be challenging, especially when dealing with huge volumes of information.
And trying to find related content can also be difficult, depending on what data you’re looking for.
But data visualizations can make all of that much easier, allowing you to see the concepts that you’re learning about in a more interesting, and often more useful manner.
Below are 50 of the best data visualizations and tools for creating your own visualizations out there, covering everything from Digg activity to network connectivity to what’s currently happening on Twitter.
Tags: belo, captura de conhecimento
SQL Server Data Mining News
Posted by Armando Brito Mendes | Filed under Bases de Dados
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Um site com visão da microsoft para o data mining
Welcome to SQLServerDataMining.com
This site has been designed by the SQL Server Data Mining team to provide the SQL Server community with access to and information about our in-database data mining and analytics features. SQL Server 2000 was the first major database release to put analytics in the database. Catch up with the latest SQL Server Data Mining news in our newsletter.
SQL Server 2012 SP1 Data Mining Add-ins for Office (with 32-bit or 64-bit Support)
The Data Mining Add-ins allow you to harness the power of SQL Server 2012 predictive analytics in Excel and Visio and they have been updated to include 32-bit or 64-bit support for Office 2010 or Office 2013. Use Table Analysis Tools to get insight with a couple of clicks. Use the Data Mining tab for full-lifecycle data mining, and build models which can be exported to a production server. Visualize your models in Visio.
SQL Server 2012 Data Mining
Microsoft expert Rafal Lukawiecki provides free and paid videos on data mining for SQL Server 2012 at Project Botticelli. The website has other Microsoft BI topics too from leading Microsoft experts.
SQL Server DM with Excel 2010 and PowerPivot
Microsoft MVP Mark Tabladillo shows you how to unleash SQL Server 2008 Data Mining with Excel 2010 and SQL Server PowerPivot for Excel, Microsoft’s new self-service BI offering.
Tags: data mining, DW \ BI, SQL, text mining
When Variable Reduction Doesn’t Work
Posted by Armando Brito Mendes | Filed under estatística, materiais para profissionais
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Um bom exemplo de como os procedimentos habituais nem sempre funcionam
Summary: Exceptions sometimes make the best rules. Here’s an example of well accepted variable reduction techniques resulting in an inferior model and a case for dramatically expanding the number of variables we start with.
of the things that keeps us data scientists on our toes is that the well-established rules-of-thumb don’t always work. Certainly one of the most well-worn of these rules is the parsimonious model; always seek to create the best model with the fewest variables. And woe to you who violate this rule. Your model will over fit, include false random correlations, or at very least will just be judged to be slow and clunky.
Certainly this is a rule I embrace when building models so I was surprised and then delighted to find a well conducted study by Lexis/Nexis that lays out a case where this clearly isn’t true.
Tags: data mining, problemas
How signal processing can be used to identify patterns in complex time series
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional
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Uso de técnicas de processamento de sinal em séries cronológicas
The trend and seasonality can be accounted for in a linear model by including sinusoidal components with a given frequency. However, finding the appropriate frequency for each sinusoidal component requires a little more digging. This post shows how to use fast Fourier transforms to find these frequencies.
How To Forecast Time Series Data With Multiple Seasonal Periods
Posted by Armando Brito Mendes | Filed under estatística, matemática, materiais para profissionais
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Análise de séries complexas com múltiplos períodos sazonais
Time series data is produced in domains such as IT operations, manufacturing, and telecommunications. Examples of time series data include the number of client logins to a website on a daily basis, cell phone traffic collected per minute, and temperature variation in a region by the hour. Forecasting a time series signal ahead of time helps us make decisions such as planning capacity and estimating demand. Previous time series analysis blog posts focused on processing time series data that resides on Greenplum database using SQL functions. In this post, I will examine the modeling steps involved in forecasting a time series sequence with multiple seasonal periods. The various steps involved are outlined below:
- Multiple seasonality is modelled with the help of fourier series with different periods
- External regressors in the form of fourier terms are added to an ARIMA model to account for the seasonal behavior
- Akaike Information Criteria (AIC) is used to find the best fit model
Tags: previsão
Exponential Smoothing of Time Series Data in R
Posted by Armando Brito Mendes | Filed under Sem categoria
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Alisamento exponencial com o pacote expsmooth do R
This article is not about smoothing ore into gems though your may find a few gems herein.
Systematic Pattern and Random Noise
In “Components of Time Series Data”, I discussed the components of time series data. In time series analysis, we assume that the data consist of a systematic pattern (usually a set of identifiable components) and random noise (error), which often makes the pattern difficult to identify. Most time series analysis techniques involve some form of filtering out noise to make the pattern more noticeable.
How To Use Multivariate Time Series Techniques For Capacity Planning on VMs
Posted by Armando Brito Mendes | Filed under estatística, Investigação Operacional, materiais ensino
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Métodos multivariados para séries cronológicas com VMs
Capacity planning is an arduous, ongoing task for many operations teams, especially for those who rely on Virtual Machines (VMs) to power their business. At Pivotal, we have developed a data science model capable of forecasting hundreds of thousands of models to automate this task using a multivariate time series approach. Open to reuse for other areas such as industrial equipment or vehicles engines, this technique can be applied broadly to anything where regular monitoring data can be collected.
Tags: data mining, machine learning, previsão
