Course Summary
Learn how to use Python to analyze data from networks and the internet. This course covers topics such as web scraping, data parsing, and network protocols.Key Learning Points
- Learn how to use Python to manipulate data from networks and the internet
- Understand concepts such as web scraping and data parsing
- Gain knowledge of network protocols and their applications
Job Positions & Salaries of people who have taken this course might have
- USA: $62,453 - $97,661
- India: ₹298,472 - ₹1,350,000
- Spain: €19,365 - €36,000
- USA: $62,453 - $97,661
- India: ₹298,472 - ₹1,350,000
- Spain: €19,365 - €36,000
- USA: $52,000 - $107,000
- India: ₹150,000 - ₹1,200,000
- Spain: €18,000 - €35,000
- USA: $62,453 - $97,661
- India: ₹298,472 - ₹1,350,000
- Spain: €19,365 - €36,000
- USA: $52,000 - $107,000
- India: ₹150,000 - ₹1,200,000
- Spain: €18,000 - €35,000
- USA: $61,000 - $127,000
- India: ₹200,000 - ₹2,000,000
- Spain: €22,000 - €45,000
Related Topics for further study
Learning Outcomes
- Ability to use Python to manipulate data from networks and the internet
- Knowledge of web scraping and data parsing techniques
- Understanding of network protocols and their applications
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with computer networks
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Quizzes and assignments
Similar Courses
- Applied Data Science with Python
- Data Science Methodology
- Python Data Structures
Related Education Paths
Related Books
Description
This course will show how one can treat the Internet as a source of data. We will scrape, parse, and read web data as well as access data using web APIs. We will work with HTML, XML, and JSON data formats in Python. This course will cover Chapters 11-13 of the textbook “Python for Everybody”. To succeed in this course, you should be familiar with the material covered in Chapters 1-10 of the textbook and the first two courses in this specialization. These topics include variables and expressions, conditional execution (loops, branching, and try/except), functions, Python data structures (strings, lists, dictionaries, and tuples), and manipulating files. This course covers Python 3.
Knowledge
- Use regular expressions to extract data from strings
- Understand the protocols web browsers use to retrieve documents and web apps
- Retrieve data from websites and APIs using Python
- Work with XML (eXtensible Markup Language) data
Outline
- Getting Started
- Welcome to The Course
- Welcome to Python - Guido van Rossum
- Windows 10: Installing Python and Writing A Program
- Windows: Taking Screen Shots
- Macintosh: Using Python and Writing A Program
- Macintosh: Taking Screen Shots
- Python Textbook
- Help us learn more about you!
- Welcome to Python 3
- Notes on Choice of Text Editor
- Notice for Auditing Learners: Assignment Submission
- Regular Expressions (Chapter 11)
- 11.1 - Regular Expressions
- 11.2 - Extracting Data
- Bonus: Office Hours - Den Haag
- Bonus Interview: Bjarne Stroustrup - C++
- Python Regular Expression Quick Guide
- Regular Expressions
- Networks and Sockets (Chapter 12)
- 12.1 - Networked Technology
- 12.2 - Hypertext Transfer Protocol (HTTP)
- Worked Example: Sockets (Chapter 12)
- Using the Developer Console to Explore HTTP
- Bonus: Leonard Kleinrock - The First Two Packets on the ARPANET
- Bonus Video: Robert Cailliau - co-Inventor of the Web
- Bonus: Office Hours - Atlanta GA (Buckhead)
- Fun: Dr. Chuck @ CNN Reading the News
- If You Want to Learn More
- Networks and Sockets
- Programs that Surf the Web (Chapter 12)
- 12.3 - Unicode Characters and Strings
- 12.4 - Retrieving Web Pages
- Worked Example: Using Urllib (Chapter 12)
- 12.5 - Parsing Web Pages
- Worked Example: BeautifulSoup (Chapter 12)
- Bonus: Office Hours - Montreal
- Bonus Interview: Tim Berners-Lee - Inventing the Web
- Fun: I Got My Mojo Working - Geneva, Switzerland
- Notes Regarding the Use of BeautifulSoup
- Reading Web Data From Python
- Web Services and XML (Chapter 13)
- 13.1 - Data on the Web
- 13.2 eXtensible Markup Language (XML)
- 13.3 - XML Schema
- 13.4 - Parsing XML
- Worked Example: XML (Chapter 13)
- Interview: Roy Fielding - Understanding the REST Architecture
- Bonus: Office Hours - Boston
- Bonus Video: Ian Horrocks / RDF / OWL (Advanced)
- eXtensible Markup Language
- JSON and the REST Architecture (Chapter 13)
- 13.5 - JavaScript Object Notation (JSON)
- Worked Example: JSON (Chapter 13)
- Interview: Douglas Crockford - Discovering JSON
- 13.6 - Service Oriented Approach
- Video: Service Oriented Architectures
- 13.7 - Using Application Programming Interfaces
- Worked Example: GeoJSON API (Chapter 13)
- 13.8 - Securing API Requests
- Worked Example: Twitter API (Chapter 13)
- Bonus: Office Hours - Melbourne, AU
- Bonus: Office Hours - Santa Monica, CA
- Bonus: Class Reunion at Bletchley Park
- Please Rate this Course on Class-Central
- Post-Course Survey
- Keep Learning with Michigan Online
- REST, JSON, and APIs
Summary of User Reviews
Discover how to leverage the power of Python to collect, analyze, and visualize complex data. This course is highly recommended for anyone interested in learning how to use Python for network data analysis.Key Aspect Users Liked About This Course
Many users found the course to be well-structured and easy to follow.Pros from User Reviews
- The course provides a comprehensive overview of Python for network data analysis.
- The instructors are knowledgeable and engaging.
- The course is well-structured and easy to follow.
- The course offers practical exercises that help reinforce the concepts covered.
- The course provides a good foundation for further study in the field.
Cons from User Reviews
- Some users found the course to be too basic.
- Some users felt that the course did not cover enough advanced topics.
- Some users found the programming exercises to be too easy.
- Some users felt that the course could have been more interactive.
- Some users found the course to be too theoretical and not practical enough.
Charles Russell Severance
4.8 Raiting
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