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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 2. Preprocessing Data FREE CHAPTER 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

Summary

In this chapter, we explored the basic operations and functions of MongoDB directly from the Mongo shell and from a graphic interface (Robomongo). We also performed a data preparation of a CSV dataset with OpenRefine, and turned it into a well-formatted JSON dataset. Finally, we presented an introduction to data processing with the aggregation framework, which is a faster alternative to MapReduce for common aggregations. We introduced the basic operators used in the pipelines and the expressions supported by the aggregation framework.

In the next chapter, we will explore the MapReduce functionality of MongoDB, and we will create a word cloud in D3 with the most frequent words in positive tweets.

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