Course Summary
Learn how to extract and analyze data from the web using Python. This course covers web scraping, XML, JSON, and SQL for data analysis.Key Learning Points
- Understand the basics of web scraping and data extraction using Python
- Use XML and JSON for data processing
- Learn how to store and query data using SQL
Related Topics for further study
Learning Outcomes
- Ability to extract and analyze data from the web using Python
- Knowledge of XML and JSON for data processing
- Understanding of SQL for data storage and querying
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Access to a computer with internet connection
Course Difficulty Level
IntermediateCourse Format
- Online
- Self-paced
Similar Courses
- Python for Data Science
- Data Analysis with Python
Related Education Paths
Related Books
Description
This MOOC – a joint initiative between EIT Digital, Université de Nice Sophia-Antipolis / Université Côte d'Azur and INRIA - introduces the Linked Data standards and principles that provide the foundation of the Semantic web. You will learn how to publish, obtain and use structured data directly from the Web. Learning the principles, languages and standards to exchange Data on the Web will enable you to design and produce new applications, products and services that leverage the volume and variety of data the Web holds.
Outline
- PRINCIPLES OF A WEB OF LINKED DATA
- Introduction
- Welcome video by Fabien Gandon
- Historical Introduction to the Web Architecture
- Separating Presentation and Content
- From Pages to Resources
- Linked Data Principles
- Stack of Standards and Languages
- The BBC Web site uses linked (open) data
- DBpedia at the heart of the linked open data cloud
- Searching the Web of data
- Open Calais: From natural language to linked data
- When software agents access the Web of data
- Linked Data for Exploratory Search
- Take Away Week 1
- Start of the course "Web of Data"
- Welcome and week 1 to-do-list
- Internet vs Web
- Documents about the World Wide Web Consorsium (W3C)
- References
- XML parsers
- References
- Namespace and URIs
- References
- Discover who rents a domain name
- Choosing a scheme for your URIs
- To go further
- The many names of the Semantic Web
- References
- Using semantic web and linked data approaches - The BBC website
- Open data cloud - DBpedia
- The Semantic Web Dog Food eating its own Dog Food...
- Search engines on the Web of Data
- Open Calais: Language processing techniques and machine learning techniquesL
- Getting HTML and XML data
- Exploratory search engine
- Slides of The 1st Week
- To go further
- Important: A Web of Linked Data
- Summary of Week 1 - Principles of a web of linked data
- Ideas and inventors
- Standards and Recommendations
- Find the missing values in this piece of XML
- Linked Data Principles
- Surfing DBpedia
- Using the CURL command
- The RDF data model
- Describing Resources
- Triple Model and Graph Model
- Serialization Syntaxes
- Values, Types and Languages
- Representing groups
- Naming Graphs
- RDF Schema
- Validating and Translating RDF Data
- Visualization of RDF Graphs
- Take Away Week 2
- Introduction to week 2 and to-do-list
- To go further
- Composition Rules for RDF Triples and Graphs
- to go further
- An Example RDF Graph Serialized in Various RDF Syntaxes
- Transform RDF Statements from one RDF Syntax to Another
- To go further...
- Turtle and RDF/XML Codes to Type Literal Values and Resources
- XML Schema Built-in Datatype Hierarchy
- To go further
- Turtle and RDF/XML Codes to Represent RDF Bags and Lists
- To go further...
- TriG and N-Quads Codes to Represent Named Graphs
- To go further...
- Turtle and RDF/XML Codes to Declare RDF Classes and Properties
- To go further
- RDF Validation Services
- RDF Visualization Service
- Guided our of the Web of Data
- Slides of the 2nd Week: the RDF Data Model
- The RDF Data Model
- Summary of Week 2 - The RDF Data Model
- RDF / XML
- An RDF Graph in the Turtle Syntax
- An RDF Graph in the RDF/XML Syntax
- Advanced Level Exercise
- Modeling and Formalizing an RDF Schema
- SPARQL Query Language
- RDF Graph Pattern Matching
- Statements
- Filter, Constraint and Function
- Pre and Post Processing
- Several Query Forms
- Results and Update
- Flint SPARQL Editor
- Corese
- Gephi and Corese to analyze data on the Web
- QAKIS.org
- Take Away Week 3
- Introduction to week 3 and to-do-list
- SPARQL Queries from the video "RDF Graph Pattern Matching"
- SPARQL Queries from the video "Statements"
- SPARQL Queries from the video "Filter, Constraint and Function"
- Solve SUDOKU with SPARQL...
- SPARQL Queries from the video "Pre and Post Processing"
- SPARQL with FLINT interface
- Several Query Forms
- Slides of Week 3: SPARQL Query Language
- Train yourself
- Continue eating your own dog food
- Links and References
- SPARQL Query Language
- Summary of Week 3 - SPARQL Query Language
- SPARQL statements and Complex Patterns
- Filter the name
- Post processing and Other query forms
- Advanced level exercise
- Integration of other data formats and sources
- RDFa: an RDF syntax inside HTML
- GRDDL: extract RDF from X(HT)ML
- JSON-LD: JSON syntax for RDF
- Tabular data and metadata (CSV)
- R2RML: integration with databases
- LDP: a REST API to linked data
- Augmenting Web browser with data in the pages
- RDFa distiller
- JSON-LD from Google Knowledge Graph API
- Licentia: a Web site to choose the license of your data
- Take away Week 4
- Conclusion
- Introduction to week 4 and to-do-list
- Data from the video "an RDF syntax inside HTML"
- RDFa Lite Cheat Sheet
- schema.org
- Twitter cards
- RDFa.info Play tool
- References
- GRDDL example in HTML and XML
- References
- Data from the video "JSON-LD: JSON syntax for RDF"
- References
- Data from the video: "Tabular data and metadata (CSV)"
- References
- Data from the video: "R2RML mapping"
- References
- Data from the video: "LDP: a REST API to linked data"
- References
- Microformats
- Google Knowledge Graph Search API
- Slides of the 4th Week
- References
- End of the week and end of the MOOC exercices
- Summary of week 4
- Support slides to the video
- Linked data
- RDF with holes...
- SPARQL
- From RDFa to Turtle/N3
- GRDDL
- JSON-LD in the Google Knowledge Graph API
- CSV on the Web
- R2RML - Specifying a transformation
- LDP - Call to a container
Summary of User Reviews
Learn to extract and analyze web data with Coursera's Web Data course. Users praise the course for its hands-on approach and practical application. Overall, the course receives high marks for its informative content and engaging instruction.Key Aspect Users Liked About This Course
Practical application of web data analysisPros from User Reviews
- Hands-on approach to learning
- In-depth coverage of web data analysis techniques
- Excellent instructor with real-world experience
- Clear and concise explanations of complex topics
Cons from User Reviews
- Some users found the course content too basic
- Limited focus on advanced topics
- Course requires some prior programming knowledge
- No certificate of completion included with free enrollment
Catherine Faron Zucker
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