Practical Data Analysis, 2nd Edition

Key Features

• Learn how to turn data into real insight
• Explore various concrete examples by mixing data and algorithms to discover the things we don’t know that we don’t know
• Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images

Book Description

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.

What you will learn

• Acquire, format, and visualize your data
• Build an image-similarity search engine
• Generate meaningful visualizations anyone can understand
• Get started with analyzing social network graphs
• Find out how to implement sentiment text analysis
• Install data analysis tools such as Pandas, MongoDB, and Apache Spark
• Get to grips with Apache Spark
• Implement machine learning algorithms such as classification or forecasting

Table of Contents

Chapter 1: Getting Started
Chapter 2: Preprocessing Data
Chapter 3: Getting to Grips with 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 Diseases with Cellular Automata
Chapter 10: Working with Social Graphs
Chapter 11: Working with Twitter Data
Chapter 12: Data Processing and Aggregation with MongoDB
Chapter 13: Working with MapReduce
Chapter 14: Online Data Analysis with Jupyter and Wakari
Chapter 15: Understanding Data Processing using Apache Spark