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

Summary

Web scraping is a great way to gather more data for data science projects. In fact, Wikipedia can be a great source of information and has an API as well. For example, Wikipedia data can be combined with social media and sports data to predict how successful athletes will be (https://www.kaggle.com/noahgift/social-power-nba).

We saw how we can use web scraping to collect data files from the web, and how we can use it to collect text and data from webpages. These methods are useful for collecting data that may not otherwise be accessible, but remember to consider the ethics and legality before undertaking a large web scraping project.

We also saw how we can use APIs to collect data, such as with the Reddit API. Again, remember that websites and APIs each have their own TOS that we should follow.

This chapter concludes the Dealing with Data part of the book. We've gone from the basics of Python file handling and SQL all the way to collecting and analyzing raw...