Armando B. Mendes

A Programmer’s Guide to Data Mining

Posted by Armando Brito Mendes | Filed under estatística, materiais para profissionais

Um livro on-line com alguns dos métodos de data mining

A guide to practical data mining, collective intelligence, and building recommendation systems by Ron Zacharski.

About This Book

Before you is a tool for learning basic data mining techniques. Most data mining textbooks focus on providing a theoretical foundation for data mining, and as result, may seem notoriously difficult to understand. Don’t get me wrong, the information in those books is extremely important. However, if you are a programmer interested in learning a bit about data mining you might be interested in a beginner’s hands-on guide as a first step. That’s what this book provides.
This guide follows a learn-by-doing approach. Instead of passively reading the book, I encourage you to work through the exercises and experiment with the Python code I provide. I hope you will be actively involved in trying out and programming data mining techniques. The textbook is laid out as a series of small steps that build on each other until, by the time you complete the book, you have laid the foundation for understanding data mining techniques. This book is available for download for free under a Creative Commons license (see link in footer). You are free to share the book, and remix it. Someday I may offer a paper copy, but the online version will always be free.

This book’s contents are freely available as PDF files. When you click on a chapter title below, you will be taken to a webpage for that chapter. The page contains links for a PDF of that chapter and for any sample Python code and data that chapter requires. Please let me know if you see an error in the book, if some part of the book is confusing, or if you have some other comment. I will use these to revise the chapters.

Chapter 1: Introduction

Finding out what data mining is and what problems it solves. What will you be able to do when you finish this book.

Chapter 2: Get Started with Recommendation Systems

Introduction to social filtering. Basic distance measures including Manhattan distance, Euclidean distance, and Minkowski distance. Pearson Correlation Coefficient. Implementing a basic algorithm in Python.

Chapter 3: Implicit ratings and item-based filtering

A discussion of the types of user ratings we can use. Users can explicitly give ratings (thumbs up, thumbs down, 5 stars, or whatever) or they can rate products implicitly–if they buy an mp3 from Amazon, we can view that purchase as a ‘like’ rating.

Chapter 4: Classification

In previous chapters we used people’s ratings of products to make recommendations. Now we turn to using attributes of the products themselves to make recommendations. This approach is used by Pandora among others.

Chapter 5: Further Explorations in Classification

A discussion on how to evaluate classifiers including 10-fold cross-validation, leave-one-out, and the Kappa statistic. The k Nearest Neighbor algorithm is also introduced.

Chapter 6: Naïve Bayes

An exploration of Naïve Bayes classification methods. Dealing with numerical data using probability density functions.

Chapter 7: Naïve Bayes and unstructured text

This chapter explores how we can use Naïve Bayes to classify unstructured text. Can we classify twitter posts about a movie as to whether the post was a positive review or a negative one?

Chapter 8: Clustering

Clustering – both hierarchical and kmeans clustering.

Tags: data mining, previsão

Read more | Comments off | August 11th, 2014

Site sobre visualização da GE.com

Posted by Armando Brito Mendes | Filed under estatística, visualização

Site com muitos exemplos de visualização mantido pela GE

GE Works. Building, Moving, Powering and Curing the world. In the process, our technologies are generating data on a petabyte scale. This data contains valuable information that will drive insights, innovations, and discoveries, but it can be difficult to access and digest. Using data visualization, we’re pairing science and design to simplify the complexity and drive a deeper understanding of the context in which we operate.

Check out our latest video.

We encourage you to explore the projects below.

For further information about GE’s data visualization program, please contact us at datavizinfo@ge.com

To share your own visualizations, please visit www.visualizing.org

Tags: análise de dados, belo, data mining, Estat Descritiva, mapas

Read more | Comments off | August 11th, 2014

Posted by Armando Brito Mendes | Filed under estatística, visualização

Tutorial para explicar como acrescentar um segundo eixo aos gráficos do Excel

Data Visualization – Banking Case Lab : Microsoft Excel – use Secondary Axis to Create Two Y Axes

25th May, 2014 · Roopam Upadhyay

Analytics Lab

Banking Case

Using Secondary Axis to Create Two Y Axes in Excel

Tags: Estat Descritiva, Excel

Read more | Comments off | July 2nd, 2014

Humor com gráficos kindofnormal

Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, materiais para profissionais, visualização

Humor com esquemas e gráficos

Alguns exemplos:

- June 12, 2014
- 0
- June 11, 2014
- 3.8
- June 10, 2014
- 5.9
- June 9, 2014
- 5
- June 6, 2014
- 5.5

Tags: belo, Estat Descritiva

Read more | Comments off | June 18th, 2014

khanacademy: Lei dos grandes números

Posted by Armando Brito Mendes | Filed under estatística, materiais ensino, videos

Boas aulas de video em pt sobre a lei dos grandes números

Vídeo original: Law of Large Numbers (https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/law-of-large-numbers) 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/khanacademy).

Tags: inferência