Learn about data transforming, visualization, and even more through projects that are developed with open source tools using Packt’s new book and eBook.
Packt is pleased to announce the release of its new book Practical Data Analysis , a step-by-step guide to manage and analyze data for small businesses. The book is now available in all the popular eBook formats. The book has 360 pages and is competitively priced at $49.99, while the eBook and Kindle versions are available for $25.49.
About the Author:
Hector Cuesta has a B.A. in Informatics and an M.Sc. in Computer Science. He provides consulting services for software engineering and data analysis with experience in a variety of industries including the financial services, social networking, e-learning, and human resources. He is a lecturer at 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 is the technical reviewer of Packt's Raspberry Pi Networking Cookbook by Rick Golden, and Hadoop Operations and Cluster Management Cookbook by Shumin Guo. He is also a columnist at Software Guru Magazine and has published several scientific papers about international journals and conferences.
Data analysis refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns. Data analysis is mainly conducted in B2C (Business to Consumer) applications. There are variety of data analysis techniques such as data mining, data transmission, data warehousing, and business intelligence.
Practical Data Analysis is an instructional guide that provides insights about data used for business purposes. It provides hands-on learning experience and gives a brief introduction to social networking analysis, econometrics, and machine learning techniques.
The book covers the basic concepts of OpenRefine and moves on to exploratory data analysis using D3j’s visualization framework. It explores all aspects of transforming and analyzing data through different projects. This book is aimed at readers who have a basic knowledge of programming, statistics, and linear algebra, and is suitable for professional developers or analysts from small businesses who want to implement data analysis and visualization in a more practical way.
Practical Data Analysis covers the following topics:
Chapter 1: Getting Started
Chapter 2: Working with Data
Chapter 3: Data Visualization
Chapter 4: Text Classification
Chapter 5: Similarity-based Image Retrieval
Chapter 6: Simulation of Stock Prices
Chapter 7: Predicting Gold Prices
Chapter 8: Working with Support Vector Machines
Chapter 9: Modeling Infectious Disease with Cellular Automata
Chapter 10: Working with Social Graphs
Chapter 11: Sentiment Analysis of Twitter Data
Chapter 12: Data Processing and Aggregation with MongoDB
Chapter 13: Working with MapReduce
Chapter 14: Online Data Analysis with IPython and Wakari
Appendix: Setting Up the Infrastructure
Packt is one of the most prolific and fastest-growing tech book publishers in the world. Originally focused on open source software, Packt books now focus on practicality, recognizing that readers are ultimately concerned with getting the job done. Packt’s digitally focused business model allows them to publish up-to-date books in very specific areas.
|Practical Data Analysis|
|Transform, model and visualize your data through hands-on projects, developed in open source tools.