Practical Data Analysis: For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that.

Hector Cuesta

€18.99 per month

3.6 (7 Ratings)

Paperback Oct 2013 360 pages 1st Edition

Hector Cuesta

€18.99 per month

3.6 (7 Ratings)

Paperback Oct 2013 360 pages 1st Edition

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Key benefits

Explore how to analyze your data in various innovative ways and turn them into insight
Learn to use the D3.js visualization tool for exploratory data analysis
Understand how to work with graphs and social data analysis
Discover how to perform advanced query techniques and run MapReduce on MongoDB

Description

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.

Who is this book 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.

What you will learn

Work with data to get meaningful results from your data analysis projects Visualize your data to find trends and correlations Build your own image similarity search engine Learn how to forecast numerical values from time series data Create an interactive visualization for your social media graphExplore the MapReduce framework in MongoDB Create interactive simulations with D3js

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Pedram Jun 23, 2014

This is not a finished book. It is not well-written, the examples are hard to follow, they seem to shift quickly from one subject to another without explaining any of the logic or theory behind it. The code on the author's website doesn't match the code in the book, and some of the code is simply missing. I was very interested in this topic, but found it really hard to get through past Chapter 3.This book clearly needs more time and effort to make it something enjoyable and understandable for the reader.

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Carlos Rodriguez Contreras Feb 19, 2014

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.

R. Friesel Jr. Dec 09, 2013

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.

José Carlos Dec 07, 2013

This book is not about theories of data analysis, is about how move your hacking skills into the data analysis world.If you are a programmer/hacker who want to understanding a problem from a data-oriented perspective, this book isfor you.This book is a fast introduction to data analysis methods including some of the most used techniques forclassification, regression and clustering. The book provides a wide range of tools like Python, mlpy, Pandas, D3jsand MongoDB. The recipes are clear and easy to follow you can get into data analysis in fast way if you alreadyhave some programming skills.I can highly recommend chapters 10 and 11 which focus on Social Networks Analytics and Social NetworksGraph’s Visualization.

Joshua E. Simons Dec 05, 2013

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.

Practical Data Analysis: For small businesses, analyzing the information contained in their data using open source technology could be game-changing. All you need is some basic programming and mathematical skills to do just that.

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