You're reading from Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801071970

Length 620 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Science

Author (1):

Nathan George

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Table of Contents (30) Chapters

Preface

1. Part I - An Introduction and the Basics

2. Introduction to Data Science FREE CHAPTER

3. Getting Started with Python

4. Part II - Dealing with Data

5. SQL and Built-in File Handling Modules in Python

6. Loading and Wrangling Data with Pandas and NumPy

7. Exploratory Data Analysis and Visualization

8. Data Wrangling Documents and Spreadsheets

9. Web Scraping

10. Part III - Statistics for Data Science

11. Probability, Distributions, and Sampling

12. Statistical Testing for Data Science

13. Part IV - Machine Learning

14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction

15. Machine Learning for Classification

16. Evaluating Machine Learning Classification Models and Sampling for Classification

17. Machine Learning with Regression

18. Optimizing Models and Using AutoML

19. Tree-Based Machine Learning Models

20. Support Vector Machine (SVM) Machine Learning Models

21. Part V - Text Analysis and Reporting

22. Clustering with Machine Learning

23. Working with Text

24. Part VI - Wrapping Up

25. Data Storytelling and Automated Reporting/Dashboarding

26. Ethics and Privacy

27. Staying Up to Date and the Future of Data Science

28. Other Books You May Enjoy

29. Index

The ethics and legality of web scraping

The legality of web scraping has changed over the years. For example, a company called "Bidder's Edge" was scraping eBay in the late 1990s for their auction data. eBay took them to court and Bidder's Edge agreed to pay eBay a settlement in cash and stop scraping their data. However, in more recent times (2019), the company hiQ won a court ruling against LinkedIn, allowing hiQ to scrape LinkedIn's public-facing data. The legal precedent at this point seems to be that if the data is public-facing, it can be scraped. This means if we can access the data without logging in to an account (and without clicking any buttons agreeing to terms of service), then we are probably legally allowed to scrape the data. However, big companies have lots of resources and lawyers, so scraping their data and using it to create a business runs the risk of litigation, like in the case of hiQ.

Craigslist is an example of a site that is...