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Data Analysis with Python

You're reading from Data Analysis with Python A Modern Approach

Product type Paperback

Published in Dec 2018

Publisher Packt

ISBN-13 9781789950069

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

Jupyter

Concepts

Data Analysis

Author (1):

David Taieb

View More author details

Table of Contents (14) Chapters

Preface

1. Programming and Data Science – A New Toolset FREE CHAPTER

2. Python and Jupyter Notebooks to Power your Data Analysis

3. Accelerate your Data Analysis with Python Libraries

4. Publish your Data Analysis to the Web - the PixieApp Tool

5. Python and PixieDust Best Practices and Advanced Concepts

6. Analytics Study: AI and Image Recognition with TensorFlow

7. Analytics Study: NLP and Big Data with Twitter Sentiment Analysis

8. Analytics Study: Prediction - Financial Time Series Analysis and Forecasting

9. Analytics Study: Graph Algorithms - US Domestic Flight Data Analysis

10. The Future of Data Analysis and Where to Develop your Skills

A. PixieApp Quick-Reference

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Index

Getting started with Apache Spark

The term big data can rightly feel vague and imprecise. What is the cut-off for considering any dataset big data? Is it 10 GB, 100 GB, 1 TB or more? One definition that I like is: big data is when the data cannot fit into the memory available in a single machine. For years, data scientists have been forced to sample large datasets, so they could fit into a single machine, but that started to change as parallel computing frameworks that are able to distribute the data into a cluster of machines made it possible to work with the dataset in its entirety, provided of course that the cluster had enough machines. At the same time, advances in cloud technologies made it possible to provision on demand a cluster of machines that are adapted to the size of the dataset.

Today, there are multiple frameworks (most of the time available as open source) that can provide robust, flexible parallel computing capabilities. Some of the most popular include Apache Hadoop (http...

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Authors (1)

David Taieb

David Taieb

David Taieb is the Distinguished Engineer for the Watson and Cloud Platform Developer Advocacy team at IBM, leading a team of avid technologists on a mission to educate developers on the art of the possible with data science, AI and cloud technologies. He's passionate about building open source tools, such as the PixieDust Python Library for Jupyter Notebooks, which help improve developer productivity and democratize data science. David enjoys sharing his experience by speaking at conferences and meetups, where he likes to meet as many people as possible.

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