- Tapa blanda: 360 páginas
- Editor: Packt Publishing (22 de octubre de 2013)
- Idioma: Inglés
- ISBN-10: 1783280999
- ISBN-13: 978-1783280995
- Valoración media de los clientes: Sé el primero en opinar sobre este producto
- Clasificación en los más vendidos de Amazon: nº272.665 en Libros en idiomas extranjeros (Ver el Top 100 en Libros en idiomas extranjeros)
Practical Data Analysis (Inglés) Tapa blanda – 22 oct 2013
Descripción del producto
Reseña del editor
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.
Biografía del autor
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 scientifi c 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.
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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.
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.
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
Finding similar images
Simulation of stock price and predicting the prices of gold
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.