Libros UCLV { BETA }

Programación en el área de bioinformática utilizando Python como lenguaje de Programación

The purpose of this book is to show the reader how to use the Python programming language to facilitate and automate the wide variety of data manipulation tasks encountered in life science research and development. It is designed to be accessible to readers with a range of interests and backgrounds, both scientific and technical. It emphasizes practical programming, using meaningful examples of useful code. In addition to meeting the needs of individual readers, it can also be used as a textbook for a one-semester upper-level undergraduate or graduate-level course. This book is designed to teach you bioinformatics software development. There is no computational biology here: no statistics, formulas, equations—not even explanations of the algorithms that underlie commonly used informatics software. The book’s examples are all based on the kind of data life science researchers work with and what they do with it. The book focuses on practical data management and manipulation tasks. The term “data” has a wide scope here, including not only the contents of databases but also the contents of text files, web pages, and other information sources. Examples focus on genomics, an area that, relative to others, is more mature and easier to introduce to people new to the scientific content of bioinformatics, as well as dealing with data that is more amenable to representation and manipulation in software. Also, and not incidentally, it is the part of bioinformatics with which the author is most familiar.

This is an introductory textbook on bioinformatics algorithms and the com- putational ideas that have driven them through the last twenty years. There are many important probabilistic and statistical techniques that we do not cover, nor do we cover many important research questions that bioinfor- maticians are currently trying to answer. We deliberately do not cover all areas of computational biology; for example, important topics like protein folding are not even discussed.

The book is aimed at both students and more advanced researchers, with diverse backgrounds. We have tried to provide a succinct description of the main biological concepts and problems for the readers with a stronger background in mathematics, statistics, and computer science. Likewise, the book is tailored to the biologists and biochemists who will often know more about the biological problems than the text explains, but need some help to understand the new data-driven algorithms, in the context of biological data. It should in principle provide enough insights while remaining sufficiently simple for the reader to be able to implement the algorithms described, or adapt them to a particular problem. The book, however, does not cover the informatics needed for the management of large databases and sequencing projects, or the processing of raw fluorescence data. The technical prerequisites for the book are basic calculus, algebra, and discrete probability theory, at the level of an undergraduate course. Any prior knowledge of DNA, RNA, and proteins is of course helpful, but not required.

This project presents demonstrations of selected computer science algorithms important in bioinformatics, implemented in the spreadsheet program Microsoft Excel. Spreadsheets provide an interesting platform for demonstration of algorithms, since various steps of the calculations can be exposed in a manner that is easily comprehensible to users with little programming experience. The algorithms demonstrated include two approaches to approximate string matching (dynamic programming and Shift-AND numeric approximate matching), Hierarchical Clustering (used in phylogenetic studies and microarray analysis of gene expression), a Naive Bayes Classifier for simulated microarray gene expression data, and a simple Neural Network. These demonstrations are designed to serve as instructional aids in bioinformatics courses

A guide to the Java search engine

Lucene in Action delivers details, best practices, caveats, tips, and tricks for using the best open-source Java search engine available.

Trabajo práctico con el software estadístico SPSS

Cubre los temas normas generales del SPSS, estadística descriptiva unidimensional, relación entre dos variables, Probabilidad y variables aleatorias, Contrastes paramétricos y no paramétricos.

Análisis estadístico de datos usando SPSS

This document is intended to help you draw conclusions from your data by statistically analysing it using SPSS (Statistical Package for the Social Sciences).

Análisis estadístico en SPSS

Cubre los temas de estadística descriptiva, distribuciones de probabilidades y contrastes de hipótesis y análisis de varianza, regresión y series temporales

Cubre los temas de gestión de archivos de datos, análisis en modo distribuido, edición, preparación, transformación y trabajo con varios orígenes de datos, tablas pivotes, sintaxis de comando para SPSS y demás utilidades del software

Guía práctica de trabajo con SPSS que cubre los temas de trabajo con las variables, distribución de frecuencias, medidas de tendencia central, dispersión y forma, prueba de T-Student, Chi cuadrado, análisis de varianza, regresión, correlación y determinación simple, diseños DCA y DBCA

El propósito de este libro consiste en familiarizar al lector con el uso del 1programa SPSS (Statistical Package for the Social Sciences) para Windows, de modo que pueda experimentar y realizar análisis adecuados para la investigación educativa con diversas técnicas estadísticas. La organización de los apartados facilita el autoaprendizaje. Además, para fines prácticos, los autores han optado por incluir en el texto tanto los términos españoles como los ingleses, ya que distintos usuarios pueden tener una versión en cualquiera de estos dos idiomas.

This is fundamentally a reference book. Parts of the book are written as standalone tutorials to make it easy for you to get into whatever you’re after. Once you’re up and running with SPSS, you can skip around and read just the sections you need. You really don’t want to read straight through the entire book. That way leads to boredom. The book was designed to be used as follows: 1. Read the opening chapter so you’ll understand what SPSS is. I tried to leave out the boring parts. 2. If SPSS is not already installed, you may need to read about installing it. That’s Chapter 2. 3. Read the stuff in Chapter 4 about defining variables and entering data. It all makes sense once you get the hang of it, but the process seems kind of screwy until you see how it works. 4. Skip around to find the things you want to do. I would mention that you could skip the introduction, but it’s too late for that. Besides, you may find some information here that could be useful. One thing that needs to be clear from the beginning: This book is not about statistics. This book is about the things you can do to command SPSS software to calculate statistics for you.

SPSS, standing for Statistical Package for the Social Sciences, is a powerful, user-friendly software package for the manipulation and statistical analysis of data. The package is particularly useful for students and researchers in psychology, sociology, psychiatry, and other behavioral sciences, contain- ing as it does an extensive range of both univariate and multivariate procedures much used in these disciplines. Our aim in this handbook is to give brief and straightforward descriptions of how to conduct a range of statistical analyses using the latest version of SPSS, SPSS 11. Each chapter deals with a different type of analytical procedure applied to one or more data sets primarily (although not exclusively) from the social and behav- ioral areas. Although we concentrate largely on how to use SPSS to get results and on how to correctly interpret these results, the basic theoretical background of many of the techniques used is also described in separate boxes. When more advanced procedures are used, readers are referred to other sources for details. Many of the boxes contain a few mathematical formulae, but by separating this material from the body of the text, we hope that even readers who have limited mathematical background will still be able to undertake appropriate analyses of their data.

This is fundamentally a reference book. Parts of the book are written as stand?alone tutorials to make it easy for you to get into whatever you’re after. Once you’re up and running with SPSS, you can skip around and read just the sections you need. You really don’t want to read straight through the entire book. That way leads to boredom. We know — we went straight through everything to write the book, and believe us, you don’t want to do that. This book is not about math. It’s about statistics. You don’t derive anything. You don’t do any math by hand or look up numbers in statistical tables. You won’t find one explanation of how calculations are performed under the hood. This book is about the things you can do to command SPSS to calcu- late statistics for you. The inside truth is that you can be as dumb as a post about statistical calculation techniques and still use SPSS to produce some nifty stats!

This exercise book was written for the students of the University of Miskolc within the framework of Business Statistics and Quantitative Statistical Methods. Some parts of the exercises are translated from the Hungarian book of Domán – Szilágyi – Varga: Statisztikai elemzések alapjai II, which are supplemented by SPSS exercises on the basis of SPSS 16.0 and 19.0 Tutorial. This book is a tutorial, which includes theoretical background just to understand the examples included.