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Practical Data Analysis von [Cuesta, Hector]
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Practical Data Analysis Kindle Edition


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Länge: 361 Seiten Verbesserter Schriftsatz: Aktiviert PageFlip: Aktiviert
Sprache: Englisch
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Produktbeschreibungen

Kurzbeschreibung

In Detail

Plenty of small businesses face big amounts of data but lack the internal skills to support quantitative analysis. Understanding how to harness the power of data analysis using the latest open source technology can lead them to providing better customer service, the visualization of customer needs, or even the ability to obtain fresh insights about the performance of previous products. Practical Data Analysis is a book ideal for home and small business users who want to slice and dice the data they have on hand with minimum hassle.

Practical Data Analysis is a hands-on guide to understanding the nature of your data and turn it into insight. It will introduce you to the use of machine learning techniques, social networks analytics, and econometrics to help your clients get insights about the pool of data they have at hand. Performing data preparation and processing over several kinds of data such as text, images, graphs, documents, and time series will also be covered.

Practical Data Analysis presents a detailed exploration of the current work in data analysis through self-contained projects. First you will explore the basics of data preparation and transformation through OpenRefine. Then you will get started with exploratory data analysis using the D3js visualization framework. You will also be introduced to some of the machine learning techniques such as, classification, regression, and clusterization through practical projects such as spam classification, predicting gold prices, and finding clusters in your Facebook friends’ network. You will learn how to solve problems in text classification, simulation, time series forecast, social media, and MapReduce through detailed projects. Finally you will work with large amounts of Twitter data using MapReduce to perform a sentiment analysis implemented in Python and MongoDB.

Practical Data Analysis contains a combination of carefully selected algorithms and data scrubbing that enables you to turn your data into insight.

Approach

Practical Data Analysis is a practical, step-by-step guide to empower small businesses to manage and analyze your data and extract valuable information from the data.

Who this book is for

This book is for developers, small business users, and analysts who want to implement data analysis and visualization for their company in a practical way. You need no prior experience with data analysis or data processing; however, basic knowledge of programming, statistics, and linear algebra is assumed.

Über den Autor und weitere Mitwirkende

Hector Cuesta

Hector Cuesta holds a B.A in Informatics and M.Sc. in Computer Science. He provides consulting services for software engineering and data analysis with experience in a variety of industries including financial services, social networking, e-learning, and human resources.

He is a lecturer in the Department of Computer Science at the Autonomous University of Mexico State (UAEM). His main research interests lie in computational epidemiology, machine learning, computer vision, high-performance computing, big data, simulation, and data visualization.

He helped in the technical review of the books, Raspberry Pi Networking Cookbook by Rick Golden and Hadoop Operations and Cluster Management Cookbook by Shumin Guo for Packt Publishing. He is also a columnist at Software Guru magazine and he has published several scientific papers in international journals and conferences. He is an enthusiast of Lego Robotics and Raspberry Pi in his spare time.

You can follow him on Twitter at https://twitter.com/hmCuesta.


Produktinformation

  • Format: Kindle Edition
  • Dateigröße: 40287 KB
  • Seitenzahl der Print-Ausgabe: 361 Seiten
  • ISBN-Quelle für Seitenzahl: 1512108324
  • Verlag: Packt Publishing (22. Oktober 2013)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B00G395ON0
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Word Wise: Nicht aktiviert
  • Verbesserter Schriftsatz: Aktiviert
  • Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
  • Amazon Bestseller-Rang: #540.720 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)
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Amazon.com: 3.4 von 5 Sternen 7 Rezensionen
2 von 2 Kunden fanden die folgende Rezension hilfreich
5.0 von 5 Sternen A must for a Data Scientist 19. Februar 2014
Von Carlos Rodriguez Contreras - Veröffentlicht auf Amazon.com
Format: Kindle Edition Verifizierter Kauf
This a very useful text for all people trying to get into Big Data Analysis. Concepts are clearly explained and readers do not need to be experts in any topic covered, this is why I chose the Cuesta's book over a lot of books on Big Data that apparently try to show mainly the expertise of authors. If you, like me, are interested in Big Data, this is a must on your shelf.
7 von 11 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen Poorly-written and lacking in rigorous treatment of presented techniques 5. Dezember 2013
Von Joshua E. Simons - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
I am returning this book for two reasons. First, it is poorly-written and would have benefited greatly from editing to fix basic language issues. For me, this problem was bad enough that it prevented me from reading the book, despite my interest in the topic. The second problem is that this is much more of a cookbook than I was looking for, based on the content of the SVM chapter which was of most interest to me. There is only cursory coverage of the theory underlying the techniques mentioned, which for me is a problem because without that understanding one risks using poorly understood techniques in inappropriate ways to draw questionable conclusions.

For someone who already has a good grounding in data analysis, I imagine this book could be a great introduction to a variety of software tools that can be used to perform practical data analysis. But for someone like me, who wants to develop a solid understanding of the statistical principles underlying these techniques so I can apply them correctly and thoughtfully, this is not the right book.
7 von 7 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen good intro to data analysis, great if you have something to compare it to 9. Dezember 2013
Von R. Friesel Jr. - Veröffentlicht auf Amazon.com
Format: Taschenbuch
I just finished up reading "Practical Data Analysis" by Hector Cuesta (Packt Publishing, 2013) and overall, it was a pretty good overview and recommends some good tools. I would say that the book is a good place for someone to get started if they have no real experience performing these kinds of analyses, and though Cuesta doesn't go deep into the math behind it all, he isn't afraid to use the technical names for different formulae, which should make it easy for you to do your own follow-up research.

Jeff Leek's Data Analysis on Coursera provides the lens through which I read this book. That being said, I found myself doing a lot of comparing and contrasting between the two. For example, they both use practical, reasonably small "real world" sample problems to highlight specific analytical techniques and/or features of their chosen toolkits. However, whereas Leek's course focused exclusively on using R, Cuesta assembles his own all-star team of tools using Python and D3.js. Perhaps it goes without saying, but there are pros and cons to each approach (e.g., Leek's "pure R" vs. Cuesta's "Python plus D3.js"), and I felt that it was best to consider them together.

Cuesta's approach with this book is to present a sample scenario in each chapter that introduces a class of problem, a solution to that problem, and his recommended toolkit. For example, chapter six creates a stock price simulation, introducing simple simulation problems (especially for apparently stochastic data), time series data and Monte Carlo methods, and then how to simulate the data using Python and visualizing it in D3.js. Although the book is not strictly a "cookbook", the chapters very much feel like macro-level "recipes". There's quite a bit of code and some decent discussion around the concepts that govern the analytical model, and (true to the "practical" in the title) the emphasis is on the "how" and not the "why".

While I did not read the entire book cover-to-cover, I would definitely recommend it to anyone that wants an introduction to some basic data analysis techniques and tools. You'll get more out of this book if you have some base to compare it to -- e.g., some experience in R (academic or otherwise); and you'll get the most out of this book if you also have a solid foundation in the mathematics and/or statistics that underlie these analytical approaches.

DISCLOSURE: I was given an electronic copy of this book from the publisher in exchange for writing a review.
5 von 5 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Make data talk to you 27. November 2013
Von Mark Kerzner - Veröffentlicht auf Amazon.com
Format: Taschenbuch
This is a very practical book, which teaches you how to "make data talk to you," that is, how to extract information, quantitative and qualitative, out of your data, and make it useful beyond just numbers.

Following the by now ubiquitous quote by Hal Varian of Google that "the sexy job in the next ten years will be statisticians" [...] the book teaches not the theory and not the programming languages, but methods and operations on the data.

Programming languages do come in (Python with its mathematical and word analysis packages), but only as tools for the practical applications. So, if you are not looking for the theoretical mathematical proofs or for computers science implementation details but are rather interested in the answers that the data can provide, you have come to the right place. Here are some of the the areas that the books covers:

Data formats and visualization
Text classification
Finding similar images
Simulation of stock price and predicting the prices of gold
Machine learning
Modeling infectious diseases
Working with social graphs
Sentiment analysis of Twitter data

The reader will do well to go deeper and to read the description of the algorithms mentioned in the books. As mentioned, the books is practical in that it explains the benefits of the analysis but not the analysis itself. However, it gives you a good list of areas you need to go deeper into, and sets you on the right track with that. Later, you will be able to use it as handbook and a cheat sheet.
4 von 4 Kunden fanden die folgende Rezension hilfreich
4.0 von 5 Sternen Practical Introduction to Data Analysis for Beginners 24. November 2013
Von View2 - Veröffentlicht auf Amazon.com
Format: Taschenbuch
This books gives a very practical introduction to data analysis. It covers a wide range of topics, including data visualization, text analysis (spam recognition, sentiment analysis), image analysis, social graph analysis, Bayes classification, SVM, etc. The examples are very practical, and teaches the user how to use popular languages and libraries like d3.js, python3, nltk, mlpy etc. to do basic data analysis.

The book is a great read for beginners. To read and fully appreciate it, no data analysis is required. The books provides an introductory to the very basic techniques. Some basic understanding of python and javascript would be necessary, though.

What I like of this book is its hand-on style: while reading, you can easily get started with your first data analyses. The examples are very simple, the code easy to read, and a very detailed appendix helps to install the tools used. This book is a great help to learn data analysis by doing.

What may be improved is precision. I found some grammar mistakes. Not so big a problem, but not perfect, either. For instance reading sentences like "we will use Pillow due to its compatibility with Python 3.2 and can be downloaded ..." [p. 97] does hurt a little. More problematic is the section "Classifier accuracy" [p. 90]. It simply uses the ratio of correctly predicted emails to be a measure of accuracy, although actually every discussion of classification accuracy must contain the rations of false positives and false negatives as well.

Overall, this book is a very practical introduction to data analysis for beginners.
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