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Paul Resnick
This course teaches you to fetch and process data from services on the Internet. It covers Python list comprehensions and provides opportunities to practice extracting from and processing deeply nested data. You'll also learn how to use the Python requests module to interact with REST APIs and what to look for in documentation of those APIs. For the final project, you will construct a “tag recommender” for the flickr photo sharing site.
Read more
This course teaches you to fetch and process data from services on the Internet. It covers Python list comprehensions and provides opportunities to practice extracting from and processing deeply nested data. You'll also learn how to use the Python requests module to interact with REST APIs and what to look for in documentation of those APIs. For the final project, you will construct a “tag recommender” for the flickr photo sharing site.
Read more
This course teaches you to fetch and process data from services on the Internet. It covers Python list comprehensions and provides opportunities to practice extracting from and processing deeply nested data. You'll also learn how to use the Python requests module to interact with REST APIs and what to look for in documentation of those APIs. For the final project, you will construct a “tag recommender” for the flickr photo sharing site.
The course is well-suited for you if you have already taken the "Python Basics" and "Python Functions, Files, and Dictionaries" courses (courses 1 and 2 of the Python 3 Programming Specialization). If you are already familiar with Python fundamentals but want practice at retrieving and processing complex nested data from Internet services, you can also benefit from this course without taking the previous two.
This is the third of five courses in the Python 3 Programming Specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/mond3r/data
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What's inside
Syllabus
Nested Data and Nested Iteration
In week one the video lectures and activities from the Runestone textbook will cover more complex data structures. By the end of this week, you will have learned how to process json formatted data, traverse nested data using nested iteration, and extract values from nested data.
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Syllabus
Nested Data and Nested Iteration
In week one the video lectures and activities from the Runestone textbook will cover more complex data structures. By the end of this week, you will have learned how to process json formatted data, traverse nested data using nested iteration, and extract values from nested data.
Read more
Traffic lights
Traffic lights
Read about what's good
what should give you pause and
possible dealbreakers
Develops core Python skills for data fetching and processing from services on the Internet
Builds on prior courses in the 'Python 3 Programming Specialization' series
Introduces the Python requests module for interacting with REST APIs and API documentation
Emphasizes list comprehensions and provides opportunities to practice extracting and processing deeply nested data
Culminates in a practical project of building a tag recommender for Flickr
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Reviews summary
Practical python data handling
According to learners, this course provides a solid and practical foundation in data collection and processing using Python. Students particularly appreciate the focus on nested data structures, list comprehensions, and utilizing the requests module for APIs. The hands-on coding and projects, especially the final project, are frequently highlighted as highly beneficial for solidifying understanding. While largely well-structured and clear, some learners note the pace can be fast or certain assignments challenging, occasionally requiring external searching. Overall, it's seen as a strong continuation for those in the specialization.
Some content may need updating.
"Some examples felt slightly dated, although the core concepts are still relevant."
"APIs covered were good but might benefit from more modern or diverse examples."
"Could use more coverage on complex data structures or error handling practices."
"Small parts seem a bit out of sync with the latest Python practices."
Builds well on prior Python courses.
"This course is a perfect follow-up to the first two courses in the specialization."
"Requires a solid understanding of Python fundamentals, as stated in the prerequisites."
"Good progression from the basics covered earlier, building more complex ideas."
"Fits well into the overall Python programming specialization path."
Explains complex topics clearly.
"The explanations of nested data and list comprehensions were very clear and helpful."
"Instructors did a good job breaking down the API concepts into manageable steps."
"Found the lectures easy to follow and the concepts well-explained."
"Good introduction to how to handle JSON data effectively."
Builds practical data handling skills.
"The hands-on coding and projects are the strongest part of the course for me."
"I learned how to use practical tools and strategies that I could apply immediately to my work."
"The final project was challenging but really helped solidify the concepts."
"Applied skills are key here, and the exercises deliver."
Assignments can be tricky.
"Assignments sometimes required searching outside the course materials for clarification."
"The labs were good practice but occasionally frustrating due to lack of hints."
"Wish there were more practice exercises before the graded assignments."
"Some assignment instructions could be clearer."
Can be challenging for some learners.
"Some sections, especially the API part, moved quite fast."
"Needed to spend extra time reviewing lectures and materials to keep up."
"Could be challenging without prior programming experience, even with the prerequisites."
"The learning curve felt steep at times."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Data Collection and Processing with Python with these
activities:
Compile a list of useful resources for data extraction
Show steps
Create a valuable reference for yourself and others by organizing and expanding on the course materials on data extraction.
Browse courses on
Python
Show steps
-
Review the course materials and identify key resources
-
Search for additional resources on the Internet, such as tutorials, articles, and tools
-
Organize the resources into a structured format, such as a document or website
Read 'Web Scraping with Python' by Ryan Mitchell
Show steps
Gain a comprehensive understanding of web scraping techniques and best practices.
View
Web Scraping with Python: Data Extraction from...
on Amazon
Show steps
-
Read through the book's chapters on data extraction and web scraping
-
Apply the concepts to your own web scraping projects
Practice using Python list comprehensions
Show steps
Refresh your understanding of Python list comprehensions to prepare for extracting and processing data from Internet services.
Browse courses on
Python
Show steps
-
Review the syntax and usage of Python list comprehensions
-
Complete practice exercises involving extracting data using list comprehensions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow online tutorials on using BeautifulSoup for web scraping
Show steps
Expand your data extraction skills by learning to use BeautifulSoup for web scraping.
Browse courses on
BeautifulSoup
Show steps
-
Find reputable online tutorials on BeautifulSoup
-
Follow the tutorials and practice using BeautifulSoup
-
Apply your newfound knowledge to extract data from websites
Complete Python coding challenges related to data extraction
Show steps
Solidify your understanding of Python data extraction techniques through practice.
Browse courses on
Data Extraction
Show steps
-
Find coding challenges that focus on data extraction
-
Attempt to solve the challenges using Python
-
Review solutions and learn from your mistakes
Contribute to open-source projects related to data extraction
Show steps
Gain practical experience and contribute to the community by volunteering on data extraction projects.
Browse courses on
Open-Source
Show steps
-
Find open-source projects that focus on data extraction
-
Identify areas where you can make contributions
-
Collaborate with the project team and submit pull requests
Build a Python script to extract data from a specific API
Show steps
Apply your knowledge of Python and APIs to create a practical tool for extracting data from the Internet.
Browse courses on
Data Extraction
Show steps
-
Identify an API that provides data relevant to your interests
-
Use Python's requests module to interact with the API
-
Extract the desired data and store it in a structured format
-
Test your script to ensure it retrieves data accurately
Participate in a hackathon focused on data extraction and analysis
Show steps
Challenge yourself and collaborate with others to apply your data extraction skills in a practical setting.
Show steps
-
Find a hackathon that aligns with your interests
-
Form a team or join an existing one
-
Develop a project proposal and build a solution involving data extraction
-
Present your project to a panel of judges
Compile a list of useful resources for data extraction
Show steps
Create a valuable reference for yourself and others by organizing and expanding on the course materials on data extraction.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the course materials and identify key resources
- Search for additional resources on the Internet, such as tutorials, articles, and tools
- Organize the resources into a structured format, such as a document or website
Read 'Web Scraping with Python' by Ryan Mitchell
Show steps
Gain a comprehensive understanding of web scraping techniques and best practices.
View
Web Scraping with Python: Data Extraction from...
on Amazon
Show steps
Show steps
Show steps
- Read through the book's chapters on data extraction and web scraping
- Apply the concepts to your own web scraping projects
Practice using Python list comprehensions
Show steps
Refresh your understanding of Python list comprehensions to prepare for extracting and processing data from Internet services.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the syntax and usage of Python list comprehensions
- Complete practice exercises involving extracting data using list comprehensions
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow online tutorials on using BeautifulSoup for web scraping
Show steps
Expand your data extraction skills by learning to use BeautifulSoup for web scraping.
Browse courses on
BeautifulSoup
Show steps
Show steps
Show steps
- Find reputable online tutorials on BeautifulSoup
- Follow the tutorials and practice using BeautifulSoup
- Apply your newfound knowledge to extract data from websites
Complete Python coding challenges related to data extraction
Show steps
Solidify your understanding of Python data extraction techniques through practice.
Browse courses on
Data Extraction
Show steps
Show steps
Show steps
- Find coding challenges that focus on data extraction
- Attempt to solve the challenges using Python
- Review solutions and learn from your mistakes
Contribute to open-source projects related to data extraction
Show steps
Gain practical experience and contribute to the community by volunteering on data extraction projects.
Browse courses on
Open-Source
Show steps
Show steps
Show steps
- Find open-source projects that focus on data extraction
- Identify areas where you can make contributions
- Collaborate with the project team and submit pull requests
Build a Python script to extract data from a specific API
Show steps
Apply your knowledge of Python and APIs to create a practical tool for extracting data from the Internet.
Browse courses on
Data Extraction
Show steps
Show steps
Show steps
- Identify an API that provides data relevant to your interests
- Use Python's requests module to interact with the API
- Extract the desired data and store it in a structured format
- Test your script to ensure it retrieves data accurately
Participate in a hackathon focused on data extraction and analysis
Show steps
Challenge yourself and collaborate with others to apply your data extraction skills in a practical setting.
Show steps
Show steps
Show steps
- Find a hackathon that aligns with your interests
- Form a team or join an existing one
- Develop a project proposal and build a solution involving data extraction
- Present your project to a panel of judges
Career center
Learners who complete Data Collection and Processing with Python will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists leverage their extensive mathematical, coding, and statistical skills alongside knowledge of computer science, business, and industry-specific knowledge to find insights in data. This course's focus on data collection and processing with Python can be a useful addition to the skillset of a Data Scientist, who will likely be involved in all stages of the data pipeline including data collection, storage, and processing.
Machine Learning Engineer
Machine Learning Engineer
Closely related to Data Scientists, Machine Learning Engineers focus on developing and refining algorithms to advance machine learning models. Being well-versed in data collection, data processing, and working with nested data is essential for Machine Learning Engineers. Because this course covers these topics, it may be useful for working professionals looking to transition to the field of Machine Learning.
Data Analyst
Data Analyst
Data Analysts work with data to collect, transform, and analyze it. The insights derived from this work are then used to make informed business decisions. Knowledge of data collection and processing are key for Data Analysts, and they may find the contents of this course helpful to expand their skills and knowledge in these areas.
Web Developer
Web Developer
Web Developers work with markup and programming languages to develop website functionality. This often includes integrating with external APIs to create features in the website. This course may be useful for Web Developers who wish to gain additional skills in data collection and processing from APIs.
Software Engineer
Software Engineer
Software Engineers build and design software applications. The knowledge and skills developed in this course, namely data collection and processing with Python, will be helpful for Software Engineers who may need to work with data from APIs in their projects.
Data Engineer
Data Engineer
Data Engineers are responsible for the design, construction, and maintenance of data pipelines. This often includes work with data collection and processing. This course, therefore, may expand the skills of a Data Engineer and be useful in demonstrating competency.
Business Analyst
Business Analyst
Business Analysts help companies understand how to improve their performance by leveraging data to identify opportunities. Much of the work that Business Analysts do requires data collection and processing skills. This course can be helpful for Business Analysts who wish to build upon these skills and apply them.
Statistician
Statistician
Statisticians collect, analyze, interpret, and present data. This work can require a strong understanding of data collection and processing, which this course covers in depth.
Research Analyst
Research Analyst
Research Analysts conduct research to provide insights and make recommendations to clients. Data collection and processing skills are essential for Research Analysts, and this course can help to develop and refine these skills.
Database Administrator
Database Administrator
Database Administrators ensure the smooth operation of databases. This often involves data collection and processing, skills that are covered in this course. Working professionals in this field may find this course useful for expanding their knowledge and skills in these areas.
UX Designer
UX Designer
UX Designers focus on improving the user experience of products and services. Data collection and processing skills can be useful for UX Designers who want to gain insights into user behavior. This course may be helpful for UX Designers who want to develop their skills in these areas.
Information Security Analyst
Information Security Analyst
Information Security Analysts plan and implement security measures to protect an organization's computer networks and systems. Data collection and processing skills may be useful for Information Security Analysts who need to collect and process data to identify and mitigate security risks.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. Data collection and processing skills can be useful for Product Managers who want to understand user behavior and make informed decisions about product development. This course may be helpful for Product Managers who want to build upon these skills.
Financial Analyst
Financial Analyst
Financial Analysts make investment recommendations to individuals and organizations. They typically collect and process data to make these recommendations. This course can help to develop the data collection and processing skills that Financial Analysts need to succeed in this field.
Market Research Analyst
Market Research Analyst
Market Research Analysts gather and interpret data to understand consumer behavior. Data collection and processing skills are essential for Market Research Analysts. This course may be helpful for working professionals in this field to expand their knowledge and skills in these areas.
For more career information including salaries, visit:
OpenCourser.com/course/mond3r/data
Reading list
We've selected nine books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Data Collection and Processing with Python.
Save
This comprehensive guide to Python 3 provides in-depth coverage of the language's features and capabilities. It valuable resource for intermediate and advanced Python programmers.
Provides a concise and practical guide to the core principles of JavaScript. It must-read for anyone who wants to write clear and maintainable JavaScript code.
Comprehensive and up-to-date guide to Python, providing valuable background and reference material for this course. It covers essential Python concepts, libraries, and tools and is commonly used as a textbook at academic institutions.
Save
This cookbook provides a collection of recipes for solving common Python programming problems. It serves as a valuable resource for finding quick solutions to specific challenges.
Provides a practical and hands-on introduction to Python programming. It great resource for beginners who want to learn the basics of Python quickly.
Save
Provides a comprehensive overview of web APIs and how to use them with JavaScript. It valuable resource for front-end developers who want to learn more about web APIs.
Save
Covers best practices and idioms for writing efficient and maintainable Python code. It provides valuable insights and tips for Python programmers of all levels.
Provides a practical guide to creating responsive web designs that work well on all devices. It valuable resource for anyone who wants to design websites that are accessible to a wide range of users.
This pocket reference provides a quick and easy way to look up jQuery syntax and functions. It useful tool for front-end developers who want to use jQuery in their web applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/mond3r/data
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Rating
4.7
Effort
Approx. 20 hours
Level
Intermediate
Via
Coursera
Institution
University of Michigan
Instructor
Paul Resnick
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English
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آذربایجان دیلی
This course belongs to a
collection
called
Python 3 Programming. It's comprised of five courses:
- Data Collection and Processing with Python (viewing)
- Python Project: pillow, tesseract, and opencv
- Python Classes and Inheritance
- Python Functions, Files, and Dictionaries
- Python Basics
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