English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 2h 27m | 334 MB
Python skills are in high demand, and programmers who demonstrate real-world skill in building applications are highly sought after. In this course, Megan Silvey shows you how to take real-life programming problems and turn them into actionable results through building three different practical applications. These applications focus on working with popular Python topics: web scraping, web APIs, and text analysis. Check out this course to improve your skills and build applications that you can show off to potential employers.
This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace.
Table of Contents
Introduction
1 Real-world Python applications
2 What you should know
3 GitHub Codespaces
Scraping the Wisdom Pet Medicine Website
4 What is web scraping
5 Introducing the Wisdom Pet Medicine website
6 How to explore a webpage
7 Understanding HTML code
8 Understanding the requests package
9 How to use the get() method
10 How to retrieve webpage information
11 How to retrieve HTML code
12 How to use BeautifulSoup
13 How to use find() and find_all() methods
14 How to loop find_all() methods
15 How to retrieve webpage links
16 How to write HTML code to a text file
Exploring Alpha Vantage API
17 What are APIs
18 Introducing Alpha Vantage API
19 How to access Alpha Vantage data
20 How to use an API key
21 How to pull stock data with the alpha_vantage package Part 1
22 How to pull stock data with the alpha_vantage package Part 2
23 How to pull stock data with the requests package
24 How to put stock data into a DataFrame
25 How to use Alpha Vantage functions Part 1
26 How to use Alpha Vantage functions Part 2
27 How to use Alpha Vantage functions Part 3
28 Challenge Analyze Microsoft stock
29 Solution Analyze Microsoft stock
Text Analysis on Great Expectations Novel
30 What is text analysis
31 Introducing Project Gutenberg and Great Expectations
32 How to pull text data
33 How to clean text data
34 How to create a word cloud
35 How to improve a word cloud
36 How to analyze word frequency
37 How to perform Vader sentiment analysis
38 How to build a corpus and dictionary for topic modeling
39 How to perform topic modeling
40 Challenge Analyze Sense and Sensibility
41 Solution Analyze Sense and Sensibility
Conclusion
42 Next steps
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