Galea 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
1. Auflage 2024
ISBN: 978-1-78953-465-8
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 194 Seiten

Getting 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.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 DescriptionGet 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 finish up by showing 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.What you will learn - 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
- Plan a machine learning classification strategy and train classification, models
- Use validation curves and dimensionality reduction to tune and enhance your models
- Discover how you can use web scraping to gather and parse your own bespoke datasets
- Scrape tabular data from web pages and transform them into Pandas DataFrames
- Create interactive, web-friendly visualizations to clearly communicate your findings
Who this book is forThis book 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.

Autoren/Hrsg.

Galea, Alex

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsvisualisierung
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen

Inhaltsverzeichnis

Table of Contents - Jupyter Fundamentals
- Data Cleaning and Advanced Machine Learning
- Web Scraping and Interactive Visualizations

Über Autor(innen)

Galea Alex :

Alex Galea has been professionally practicing data analytics since graduating with a masters 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.