Key Features

Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data

Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images

A hands-on guide to understanding the nature of data and how to turn it into insight

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

About the Author

Hector Cuesta

is founder and Chief Data Scientist at Dataxios, a machine intelligence research company. Holds a BA in Informatics and a M.Sc. in Computer Science. He provides consulting services for data-driven product design with experience in a variety of industries including financial services, retail, fintech, e-learning and Human Resources. He is an enthusiast of Robotics in his spare time.

You can follow him on Twitter at https://twitter.com/hmCuesta.

Dr. Sampath Kumar

works as an assistant professor and head of Department of Applied Statistics at Telangana University. He has completed M.Sc., M.Phl., and Ph. D. in statistics. He has five years of teaching experience for PG course. He has more than four years of experience in the corporate sector. His expertise is in statistical data analysis using SPSS, SAS, R, Minitab, MATLAB, and so on. He is an advanced programmer in SAS and matlab software. He has teaching experience in different, applied and pure statistics subjects such as forecasting models, applied regression analysis, multivariate data analysis, operations research, and so on for M.Sc. students. He is currently supervising Ph.D. scholars.

Table of Contents

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

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