Visual analytics can be seen as an integral approach combining visualization, data analysis, and human factors. In order to gain knowledge from data, the visual analytics procedure unites visual analysis methods and automatic processes through human interaction. In many application scenarios, visual or automatic analysis methods were applied after the integration of heterogeneous data sources. Therefore, before performing visual analysis we should clean, normalize, and integrate the heterogeneous data sources. After the data cleaning, the analyst may choose visual analysis methods, wherein visualization helps the analyst to relate with the automatic methods by modifying parameters or selecting other analysis algorithms. At the end, the model visualization can be used to study the findings of the generated models.
Practical Data Analysis - Second Edition
By :
Practical Data Analysis - Second Edition
By:
Overview of this book
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.
This book explains the basic data algorithms without the theoretical jargon, and you’ll get hands-on turning data into insights using machine learning techniques. 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.
Table of Contents (21 chapters)
Practical Data Analysis - Second Edition
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Getting Started
Preprocessing Data
Getting to Grips with Visualization
Text Classification
Similarity-Based Image Retrieval
Simulation of Stock Prices
Predicting Gold Prices
Working with Support Vector Machines
Modeling Infectious Diseases with Cellular Automata
Working with Social Graphs
Working with Twitter Data
Data Processing and Aggregation with MongoDB
Working with MapReduce
Online Data Analysis with Jupyter and Wakari
Understanding Data Processing using Apache Spark
Customer Reviews