Practical Data Analysis by Hector CuestaI 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 doesnt go deep into the math behind it all, he isnt afraid to use the technical names for different formulae, which should make it easy for you to do your own follow-up research. 
Jeff Leeks 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 Leeks 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., Leeks pure R vs. Cuestas Python plus D3.js), and I felt that it was best to consider them together.
Cuestas 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. Theres 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. Youll 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 youll 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 received an electronic copy of this book from the publisher in exchange for writing this review.
 As an aside, this seems to be par for the course for the “technical” data analysis books, blog posts, and MOOCs that I’ve encountered. That is to say, “the math” is touched on, but if you don’t already have a background in linear algebra (or whatever) then you’re going to wind up taking it on faith that support vector machines do what you need them to do.
 I wrote about my experience in Jeff Leek’s class in April of 2013. (See: “reflecting on Data Analysis”.)
 Both the Python standard library and a collection of libraries like mlpy and matplotlib.
Practical Data Analysis
Practical Data Analysis - Transform, model, and visualize your data through hands-on projects, developed in open source tools. A hands-on guide to understanding the nature of your data and turn it into insight. Presents a detailed exploration of the current work in data analysis through self-contained projects. Publisher : Packt Publishing. Hector Cuesta hmCuesta holds a B. A in Informatics and M.
This free data analysis eBook is designed to give you the knowledge you need to start Author(s) Hector Cuesta; Publisher: Packt Publishing; 2nd Revised edition Hardcover/Paperback pages; eBook PDF; Language: English; ISBN
best happy quotes about life
Stay ahead with the world's most comprehensive technology and business learning platform.
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.
Goodreads helps you keep track of books you want to read. Want to Read saving…. Want to Read Currently Reading Read. Other editions. Enlarge cover.
This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark.