Practical data analysis

Each chapter of the book quickly introduces a key 'theme' of Data Analysis, before immersing you in the practical aspects of each theme. You'll learn quickly how to perform all aspects of Data Analysis. Practical Data Analysis is a book ideal for home and small business users who want...

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Bibliographic Details
Main Author: Cuesta, Hector.
Format: eBook
Language: English
Published: Birmingham, UK : Packt Publishing, 2013.
Series: Community experience distilled
Subjects:
Electronic data processing.
Databases.
Data structures (Computer science)
System design.
System analysis.
elektronické knihy
electronic books
ISBN: 9781680153613
1680153617
9781783281008
1783281006
9781783280995
1783280999
Physical Description: 1 online resource (360 pages) : illustrations

Cover

Table of contents

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020 |a 9781783281008  |q (electronic bk.) 
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020 |z 9781783280995  |q (print) 
020 |z 1783280999  |q (print) 
035 |a (OCoLC)862050192  |z (OCoLC)961645436  |z (OCoLC)962639130  |z (OCoLC)968016498  |z (OCoLC)969056863  |z (OCoLC)1259149271 
100 1 |a Cuesta, Hector. 
245 1 0 |a Practical data analysis /  |c Hector Cuesta. 
260 |a Birmingham, UK :  |b Packt Publishing,  |c 2013. 
300 |a 1 online resource (360 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 0 |a Community experience distilled 
500 |a Includes index. 
505 0 |a Cover; Copyright; Credits; Foreword; About the Author; Acknowledgments; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1:Getting Started; Computer science; Artificial intelligence (AI); Machine Learning (ML); Statistics; Mathematics; Knowledge domain; Data, information, and knowledge; The nature of data; The data analysis process; The problem; Data preparation; Data exploration; Predictive modeling; Visualization of results; Quantitative versus qualitative data analysis; Importance of data visualization; What about big data?; Sensors and cameras. 
505 8 |a Social networks analysisTools and toys for this book; Why Python?; Why mlpy?; Why D3.js?; Why MongoDB?; Summary; Chapter 2:Working with Data; Data sources; Open data; Text files; Excel files; SQL databases; NoSQL databases; Multimedia; Web scraping; Data scrubbing; Statistical methods; Text parsing; Data transformation; Data formats; CSV; Parsing a CSV file with the csv module; Parsing a CSV file using NumPy; JSON; Parsing a JSON file using json module; XML; Parsing an XML file in Python using xml module; YAML; Getting started with OpenRefine; Text facet; Clustering; Text filters. 
505 8 |a Numeric facetsTransforming data; Exporting data; Operation history; Summary; Chapter 3:Data Visualization; Data-Driven Documents (D3); HTML; DOM; CSS; JavaScript; SVG; Getting started with D3.js; Bar chart; Pie chart; Scatter plot; Single line chart; Multi-line chart; Interaction and animation; Summary; Chapter 4:Text Classification; Learning and classification; Bayesian classification; Naïve Bayes algorithm; E-mail subject line tester; The algorithm; Classifier accuracy; Summary; Chapter 5:Similarity-based Image Retrieval; Image similarity search; Dynamic time warping (DTW). 
505 8 |a Processing the image datasetImplementing DTW; Analyzing the results; Summary; Chapter 6:Simulation of Stock Prices; Financial time series; Random walk simulation; Monte Carlo methods; Generating random numbers; Implementation in D3.js; Summary; Chapter 7:Predicting Gold Prices; Working with the time series data; Components of a time series; Smoothing the time series; The data -- historical gold prices; Nonlinear regression; Kernel ridge regression; Smoothing the gold prices time series; Predicting in the smoothed time series; Contrasting the predicted value; Summary. 
505 8 |a Chapter 8:Working with Support Vector MachinesUnderstanding the multivariate dataset; Dimensionality reduction; Linear Discriminant Analysis; Principal Component Analysis; Getting started with support vector machine; Kernel functions; Double spiral problem; SVM implemented on mlpy; Summary; Chapter 9:Modeling Infectious Disease with Cellular Automata; Introduction to epidemiology; The epidemiology triangle; The epidemic models; The SIR model; Solving ordinary differential equation for the SIR model with SciPy; The SIRS model; Modelling with cellular automata cell, state, grid, and neighborhood. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a Each chapter of the book quickly introduces a key 'theme' of Data Analysis, before immersing you in the practical aspects of each theme. You'll learn quickly how to perform all aspects of Data Analysis. Practical Data Analysis is a book ideal for home and small business users who want to slice & dice the data they have on hand with minimum hassle. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Electronic data processing. 
650 0 |a Databases. 
650 0 |a Data structures (Computer science) 
650 0 |a System design. 
650 0 |a System analysis. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
776 0 8 |i Print version:  |a Cuesta, Hector.  |t Practical Data Analysis.  |d Birmingham : Packt Publishing, ©2013  |z 9781783280995 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpPDA00001/practical-data-analysis?kpromoter=marc  |y Full text 

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