Wheelers Books

Beginning Data Science with Python and Jupyter: Use powerful industry-standard tools within Jupyter and the Python ecosystem to unlock new, actionable insights from your data

Beginning Data Science with Python and Jupyter: Use powerful industry-standard tools within Jupyter and the Python ecosystem to unlock new, actionable insights from your data
  

Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to gi... read full description below.

Usually ships 6-12 working days – Title is in stock with supplier internationally

This title is firm sale. Please select carefully as returns are not accepted.

Quick Reference

ISBN 9781789532029
Barcode 9781789532029
Published 29 May 2018 by Packt Publishing
Format Paperback
Author(s) By Galea, Alex
Availability Internationally sourced; ships 6-12 working days

... view full title details below.

Buy Now

  • $54.99 Wheelers price
Add to Basket Add to Wishlist

Full details for this title

ISBN-13 9781789532029
ISBN-10 1789532027
Stock Available
Status Internationally sourced; ships 6-12 working days
Publisher Packt Publishing
Imprint Packt Publishing
Publication Date 29 May 2018
Publication Country United Kingdom United Kingdom
Format Paperback
Author(s) By Galea, Alex
Category Computer Programming Languages
Data Capture & Analysis
Human-Computer Interaction
Family, Home & Self Help
Number of Pages 194
Dimensions Width: 75mm
Height: 92mm
Spine: 10mm
Weight 345g
Interest Age General Audience
Reading Age General Audience
Library of Congress Computers - Programming Languages - Python, Python Computer program language, Electronic data processing - Management, Data mining, Programming & scripting languages general
NBS Text Computing: Textbooks & Study Guides
ONIX Text General/trade
Dewey Code 005.133
Catalogue Code Not specified

Description of this Book

Use powerful industry-standard tools to unlock new, actionable insight from your existing data Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book DescriptionGetting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll start with understanding the basics of Jupyter and its standard features. You'll be analyzing an example of a data analytics report. After analyzing a data analytics report, next step is to implement multiple classification algorithms. We'll then show you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. Finish up by learning to visualize these data interactively. What you will learn Identify potential areas of investigation and perform exploratory data analysis Plan a machine learning classification strategy and train classification models Use validation curves and dimensionality reduction to tune and enhance your models Scrape tabular data from web pages and transform it into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is forThis course is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.

^ top

Awards, Reviews & Star Ratings

There are no reviews for this title.

^ top

Author's Bio

Alex Galea has been professionally practicing data analytics since graduating with a Master's degree in Physics from the University of Guelph, Canada. He developed a keen interest in Python while researching quantum gases as part of his graduate studies. Alex is currently doing web data analytics, where Python continues to play a key role in his work. He is a frequent blogger about data-centric projects that involve Python and Jupyter Notebooks.

^ top