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Practical Data Analysis

You're reading from   Practical Data Analysis Pandas, MongoDB, Apache Spark, and more

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Product type Paperback
Published in Sep 2016
Publisher
ISBN-13 9781785289712
Length 338 pages
Edition 2nd Edition
Languages
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Authors (2):
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Hector Cuesta Hector Cuesta
Author Profile Icon Hector Cuesta
Hector Cuesta
Dr. Sampath Kumar Dr. Sampath Kumar
Author Profile Icon Dr. Sampath Kumar
Dr. Sampath Kumar
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Toc

Table of Contents (16) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Preprocessing Data 3. Getting to Grips with Visualization 4. Text Classification 5. Similarity-Based Image Retrieval 6. Simulation of Stock Prices 7. Predicting Gold Prices 8. Working with Support Vector Machines 9. Modeling Infectious Diseases with Cellular Automata 10. Working with Social Graphs 11. Working with Twitter Data 12. Data Processing and Aggregation with MongoDB 13. Working with MapReduce 14. Online Data Analysis with Jupyter and Wakari 15. Understanding Data Processing using Apache Spark

Chapter 1. Getting Started

Data analysis is the process in which raw data is ordered and organized to be used in methods that help to evaluate and explain the past and predict the future. Data analysis is not about the numbers, it is about making/asking questions, developing explanations, and testing hypotheses based on logical and analytical methods. Data analysis is a multidisciplinary field that combines computer science, artificial intelligence, machine learning, statistics, mathematics, and business domain, as shown in the following figure:

Getting Started

All of these skills are important for gaining a good understanding of the problem and its optimal solutions, so let's define those fields.

You have been reading a chapter from
Practical Data Analysis - Second Edition
Published in: Sep 2016
Publisher:
ISBN-13: 9781785289712
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