Computer science creates the tools for data analysis. The vast amount of data generated has made computational analysis critical and has increased the demand for skills like programming, database administration, network administration, and high-performance computing. Some programming experience in Python (or any high-level programming language) is needed to follow the chapters in this book.
Practical Data Analysis - Second Edition
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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