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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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

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Building Machine Learning Systems with Python

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Total € 129.97

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.

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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.

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Mark Kerzner Nov 27, 2013

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 visualizationText classificationFinding similar imagesSimulation of stock price and predicting the prices of goldMachine learningModeling infectious diseasesWorking with social graphsSentiment analysis of Twitter dataThe 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.

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View2 Nov 24, 2013

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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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.

Amazon Verified review

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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