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Python Data Mining Quick Start Guide

You're reading from  Python Data Mining Quick Start Guide

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781789800265
Pages 188 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Nathan Greeneltch Nathan Greeneltch
Profile icon Nathan Greeneltch
LinkedIn
Nathan Greeneltch, PhD is a ML engineer at Intel Corp and resident data mining and analytics expert in the AI consulting group. Hes worked with Python analytics in both the start-up realm and the large-scale manufacturing sector over the course of the last decade. Nathan regularly mentors new hires and engineers fresh to the field of analytics, with impromptu chalk talks and division-wide knowledge-sharing sessions at Intel. In his past life, he was a physical chemist studying surface enhancement of the vibration signals of small molecules; a topic on which he wrote a doctoral thesis while at Northwestern University in Evanston, IL. Nathan hails from the southeastern United States, with family in equal parts from Arkansas and Florida
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Table of Contents (9) Chapters

Preface 1. Data Mining and Getting Started with Python Tools 2. Basic Terminology and Our End-to-End Example 3. Collecting, Exploring, and Visualizing Data 4. Cleaning and Readying Data for Analysis 5. Grouping and Clustering Data 6. Prediction with Regression and Classification 7. Advanced Topics - Building a Data Processing Pipeline and Deploying It 8. Other Books You May Enjoy

Collecting, Exploring, and Visualizing Data

As a first step, it is important to understand how to acquire data and how to access it in computer memory. Once data is loaded, sound practices for exploration can save time downstream. This chapter will start by showing you how to interact with different data sources such as databases, disks, and streaming. Despite the practicality of these topics, many new analysts overlook them. Indeed, you cannot actually do any work if you cannot get past this beginning step. The second half of the chapter will introduce you to Seaborn for visualizing data, and then recommend types of plots for relevant and popular problem statements.

The following topics will be covered in this chapter:

  • Types of data sources and loading into pandas
  • Access, search, and sanity checks with pandas
  • Basic plotting in Seaborn
  • Relevant types of plots for visualizing...
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Authors (1)

Profile icon Nathan Greeneltch
LinkedIn
Nathan Greeneltch, PhD is a ML engineer at Intel Corp and resident data mining and analytics expert in the AI consulting group. Hes worked with Python analytics in both the start-up realm and the large-scale manufacturing sector over the course of the last decade. Nathan regularly mentors new hires and engineers fresh to the field of analytics, with impromptu chalk talks and division-wide knowledge-sharing sessions at Intel. In his past life, he was a physical chemist studying surface enhancement of the vibration signals of small molecules; a topic on which he wrote a doctoral thesis while at Northwestern University in Evanston, IL. Nathan hails from the southeastern United States, with family in equal parts from Arkansas and Florida
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