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Richard Ye
Jupyter Notebook is a popular, streamlined application for analyzing data and creating data science projects. However, developers often experience difficulty when attempting to share the files created on Jupyter Notebook.
Jupyter Book is a powerful tool you can use to build widely shareable, interactive HTML documentation, including interactive visualizations in books using data from your Jupyter Notebook projects.
Read more
Jupyter Notebook is a popular, streamlined application for analyzing data and creating data science projects. However, developers often experience difficulty when attempting to share the files created on Jupyter Notebook.
Jupyter Book is a powerful tool you can use to build widely shareable, interactive HTML documentation, including interactive visualizations in books using data from your Jupyter Notebook projects.
Read more
Jupyter Notebook is a popular, streamlined application for analyzing data and creating data science projects. However, developers often experience difficulty when attempting to share the files created on Jupyter Notebook.
Jupyter Book is a powerful tool you can use to build widely shareable, interactive HTML documentation, including interactive visualizations in books using data from your Jupyter Notebook projects.
You will learn how to build, customize, and share your first Jupyter Book in under an hour. You will also learn how to display LaTex block style math, display Plotly plots, and exclude certain files from the final build. Completing this project will provide you with practical experience and provide you with fundamental Jupyter Book skills.
Ready to start? We have a workspace waiting for you. Get started fast using a pre-configured cloud-based IDE lab environment. You’ll find all the required software needed to get started, such as Jupyter Book, preinstalled. All you need is a recent version of a modern web browser.
Register for this course and see more details by visiting:
OpenCourser.com/course/hg5wj9/guided
What's inside
Learning objectives
- After completing this project, you'll be able to:
- List use cases for jupyter book.
- Explain jupyter book setup.
- Build your own jupyter book.
- After completing this project, you'll be able to:
- List use cases for jupyter book.
- Explain jupyter book setup.
- Build your own jupyter book.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Helps learners build interactive and shareable documentation from their data analysis and data science projects
Suitable for beginners looking to create interactive documentation for their data science projects
Offers a practical and hands-on approach to building Jupyter Books, providing valuable experience for learners
Taught by instructors with experience in data analysis and data science
Save this course
Save Guided Project: Create Engaging Reports using Jupyter Book to your list so you can find it easily later:
Save
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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 Guided Project: Create Engaging Reports using Jupyter Book with these
activities:
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
This book provides a practical guide to deep learning implementation using popular libraries like Scikit-Learn, Keras, and TensorFlow.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
-
Read Chapters 1-4 to understand the basics of machine learning.
-
Work through the exercises in Chapters 5-7 to apply the concepts to deep learning.
Join a Study Group
Show steps
Engaging in peer study sessions fosters collaboration, knowledge sharing, and a deeper understanding of the course material.
Show steps
-
Find a study group or create your own.
-
Meet regularly to discuss the course material.
-
Work together on assignments and projects.
Follow Coursera's Deep Learning Specialization
Show steps
Coursera's Deep Learning Specialization offers a structured approach to learning deep learning through hands-on projects and expert guidance.
Browse courses on
Deep Learning
Show steps
-
Enroll in the Deep Learning Specialization on Coursera.
-
Complete the five courses in the specialization.
-
Submit all assignments and quizzes to demonstrate your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Deep Learning Coding Problems
Show steps
Solving coding problems reinforces deep learning concepts and improves coding skills.
Browse courses on
Deep Learning
Show steps
-
Find a platform or resource that offers deep learning coding problems.
-
Start solving problems regularly.
-
Analyze your solutions and learn from your mistakes.
Read Deep Learning
Show steps
Reading Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville provides a comprehensive foundation for deep learning concepts and techniques.
View
Deep Learning
on Amazon
Show steps
-
Read Chapters 1-4 to understand the basics of deep learning.
-
Work through the exercises in Chapters 5-7 to apply the concepts.
-
Complete the review questions at the end of each chapter to assess your understanding.
Attend a Deep Learning Workshop
Show steps
Workshops provide hands-on experience and expert guidance, complementing the theoretical knowledge gained in the course.
Browse courses on
Deep Learning
Show steps
-
Find a reputable deep learning workshop.
-
Register and attend the workshop.
-
Engage with the instructors and participants.
Build a Deep Learning Project
Show steps
Developing a deep learning project allows you to apply the knowledge and skills acquired in the course to a practical problem.
Browse courses on
Deep Learning
Show steps
-
Identify a problem that can be solved using deep learning.
-
Gather and prepare the necessary data.
-
Select and train a deep learning model.
-
Evaluate the performance of your model.
-
Deploy your model to solve the problem.
Contribute to an Open Source Deep Learning Project
Show steps
Contributing to open source deep learning projects provides practical experience and exposes you to the latest advancements in the field.
Browse courses on
Deep Learning
Show steps
-
Find an open source deep learning project that aligns with your interests.
-
Review the project's documentation and codebase.
-
Identify an area where you can contribute.
-
Make a pull request to the project.
Read Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Show steps
This book provides a practical guide to deep learning implementation using popular libraries like Scikit-Learn, Keras, and TensorFlow.
View
Hands-On Machine Learning with Scikit-Learn,...
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-4 to understand the basics of machine learning.
- Work through the exercises in Chapters 5-7 to apply the concepts to deep learning.
Join a Study Group
Show steps
Engaging in peer study sessions fosters collaboration, knowledge sharing, and a deeper understanding of the course material.
Show steps
Show steps
Show steps
- Find a study group or create your own.
- Meet regularly to discuss the course material.
- Work together on assignments and projects.
Follow Coursera's Deep Learning Specialization
Show steps
Coursera's Deep Learning Specialization offers a structured approach to learning deep learning through hands-on projects and expert guidance.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Enroll in the Deep Learning Specialization on Coursera.
- Complete the five courses in the specialization.
- Submit all assignments and quizzes to demonstrate your understanding.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Deep Learning Coding Problems
Show steps
Solving coding problems reinforces deep learning concepts and improves coding skills.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a platform or resource that offers deep learning coding problems.
- Start solving problems regularly.
- Analyze your solutions and learn from your mistakes.
Read Deep Learning
Show steps
Reading Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville provides a comprehensive foundation for deep learning concepts and techniques.
View
Deep Learning
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 1-4 to understand the basics of deep learning.
- Work through the exercises in Chapters 5-7 to apply the concepts.
- Complete the review questions at the end of each chapter to assess your understanding.
Attend a Deep Learning Workshop
Show steps
Workshops provide hands-on experience and expert guidance, complementing the theoretical knowledge gained in the course.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find a reputable deep learning workshop.
- Register and attend the workshop.
- Engage with the instructors and participants.
Build a Deep Learning Project
Show steps
Developing a deep learning project allows you to apply the knowledge and skills acquired in the course to a practical problem.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Identify a problem that can be solved using deep learning.
- Gather and prepare the necessary data.
- Select and train a deep learning model.
- Evaluate the performance of your model.
- Deploy your model to solve the problem.
Contribute to an Open Source Deep Learning Project
Show steps
Contributing to open source deep learning projects provides practical experience and exposes you to the latest advancements in the field.
Browse courses on
Deep Learning
Show steps
Show steps
Show steps
- Find an open source deep learning project that aligns with your interests.
- Review the project's documentation and codebase.
- Identify an area where you can contribute.
- Make a pull request to the project.
Career center
Learners who complete Guided Project: Create Engaging Reports using Jupyter Book will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
A Data Analyst is responsible for collecting, cleaning, and analyzing data. They use this information to help businesses identify trends and make better decisions. This course can help you build the skills you need to become a successful Data Analyst. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Data Scientist
Data Scientist
A Data Scientist is an expert in collecting, analyzing, and interpreting data. They use this information to help businesses make better decisions. This course can help you build the skills you need to become a successful Data Scientist. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Business Analyst
Business Analyst
A Business Analyst is responsible for understanding the needs of a business and developing solutions to meet those needs. They use data to help businesses make better decisions. This course can help you build the skills you need to become a successful Business Analyst. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Software Engineer
Software Engineer
A Software Engineer is responsible for designing, developing, and maintaining software applications. They use data to help improve the performance and functionality of their applications. This course can help you build the skills you need to become a successful Software Engineer. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Marketing Analyst
Marketing Analyst
A Marketing Analyst is responsible for analyzing marketing data to help businesses make better decisions. They use data to track the effectiveness of marketing campaigns and identify opportunities for improvement. This course can help you build the skills you need to become a successful Marketing Analyst. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Product Manager
Product Manager
A Product Manager is responsible for managing the development and launch of new products. They use data to help make decisions about product features, pricing, and marketing. This course can help you build the skills you need to become a successful Product Manager. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Financial Analyst
Financial Analyst
A Financial Analyst is responsible for analyzing financial data to help businesses make better decisions. They use data to evaluate investment opportunities and identify risks. This course can help you build the skills you need to become a successful Financial Analyst. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer is responsible for designing, developing, and maintaining machine learning models. They use data to help businesses make better decisions. This course can help you build the skills you need to become a successful Machine Learning Engineer. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Operations Research Analyst
Operations Research Analyst
An Operations Research Analyst is responsible for using data to improve the efficiency and effectiveness of business operations. They use data to identify problems and develop solutions. This course can help you build the skills you need to become a successful Operations Research Analyst. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Statistician
Statistician
A Statistician is responsible for collecting, analyzing, and interpreting data. They use data to help businesses make better decisions. This course can help you build the skills you need to become a successful Statistician. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Data Engineer
Data Engineer
A Data Engineer is responsible for designing, developing, and maintaining data systems. They use data to help businesses manage and process their data. This course can help you build the skills you need to become a successful Data Engineer. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Quantitative Analyst
Quantitative Analyst
A Quantitative Analyst is responsible for using data to make investment decisions. They use data to identify trends and develop investment strategies. This course can help you build the skills you need to become a successful Quantitative Analyst. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Researcher
Researcher
A Researcher is responsible for conducting research and developing new knowledge. They use data to help them understand the world around them. This course can help you build the skills you need to become a successful Researcher. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Consultant
Consultant
A Consultant is responsible for providing advice and guidance to businesses. They use data to help businesses make better decisions. This course can help you build the skills you need to become a successful Consultant. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
Technical Writer
Technical Writer
A Technical Writer is responsible for creating documentation for software and other technical products. They use data to help them understand the products they are writing about. This course can help you build the skills you need to become a successful Technical Writer. You will learn how to use Jupyter Book to create interactive, shareable reports that can help you communicate your findings to others.
For more career information including salaries, visit:
OpenCourser.com/course/hg5wj9/guided
Reading list
We've selected 11 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
Guided Project: Create Engaging Reports using Jupyter Book.
Offers an in-depth dive into creating interactive web-based data visualizations. It is an excellent reference or companion to this course, especially as it includes many specific Jupyter Book examples and use cases.
Comprehensive guide to data science with Python, covering essential data science concepts and techniques. It is an excellent reference for those who want to expand their understanding of data science beyond what is covered in this course.
Provides a comprehensive introduction to machine learning with Python, covering a wide range of machine learning algorithms and techniques. It valuable resource for those who want to apply machine learning in their Jupyter Book projects.
Classic in the field of statistical learning, providing a comprehensive overview of statistical learning theory and methods. It valuable resource for those who want to deepen their understanding of the statistical foundations of data science.
Save
Provides a practical guide to predictive modeling, covering a wide range of predictive modeling techniques and applications. It valuable resource for those who want to apply predictive modeling in their Jupyter Book projects.
Provides a comprehensive guide to using Python for data analysis, covering a wide range of data analysis techniques and tools. It valuable resource for those who want to use Python for data analysis in their Jupyter Book projects.
Provides a comprehensive guide to machine learning with Python, covering a wide range of machine learning algorithms and techniques. It valuable resource for those who want to apply machine learning in their Jupyter Book projects.
Provides a comprehensive guide to interpretable machine learning, covering a wide range of interpretable machine learning techniques and applications. It valuable resource for those who want to build interpretable machine learning models in their Jupyter Book projects.
Provides a comprehensive guide to machine learning with Scikit-Learn, Keras, and TensorFlow, covering a wide range of machine learning algorithms and techniques. It valuable resource for those who want to apply machine learning in their Jupyter Book projects.
Provides a comprehensive guide to deep learning with Python, covering a wide range of deep learning algorithms and techniques. It valuable resource for those who want to apply deep learning in their Jupyter Book projects.
Provides a comprehensive guide to natural language processing with Python, covering a wide range of natural language processing techniques and applications. It valuable resource for those who want to apply natural language processing in their Jupyter Book projects.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hg5wj9/guided
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Effort
1 hours per week for 1 week
Level
Introductory
Via
edX
Institution
IBM
Instructor
Richard Ye
Session
April 30, 2024
- April 30, 2025
Language
English
Transcript
English
Good to know
Helps learners build interactive and shareable documentation from their data analysis and data science projects
Suitable for beginners looking to create interactive documentation for their data science projects
Offers a practical and hands-on approach to building Jupyter Books, providing valuable experience for learners
Taught by instructors with experience in data analysis and data science
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