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.
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 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.