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Working in a hands-on learning environment, led by our Jupyter Cookbook expert instructor, students will learn about and explore:
Create and share interactive documents with live code, text and visualizations.Integrate popular programming languages such as Python, R, Julia, Scala with Jupyter.Develop your widgets and interactive dashboards with these innovative recipes.
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Course Access
Unlimited Duration
Last Updated
July 29, 2021
Students Enrolled
20
Total Reviews
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Certification
Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This course is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The course starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This course contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this course, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it.
Course Curriculum
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- Installation and Setting up the Environment 00:00:00
- Introduction 00:00:00
- Installing Jupyter on Windows 00:00:00
- Installing Jupyter on the Mac 00:00:00
- Installing Jupyter on Linux 00:00:00
- Installing Jupyter on a server 00:00:00
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- Adding an Engine 00:00:00
- Introduction 00:00:00
- Adding the Python 3 engine 00:00:00
- Adding the R engine 00:00:00
- Adding the Julia engine 00:00:00
- Adding the JavaScript engine 00:00:00
- Adding the Scala engine 00:00:00
- Adding the Spark engine 00:00:00
- Accessing and Retrieving Data 00:00:00
- Introduction 00:00:00
- Reading CSV files 00:00:00
- Reading JSON files 00:00:00
- Accessing a database 00:00:00
- Reading flat files 00:00:00
- Reading text files 00:00:00
- Working with Widgets 00:00:00
- Introduction 00:00:00
- What are widgets? 00:00:00
- Using ipyleaflet widgets 00:00:00
- Using ipywidgets 00:00:00
- Using a widget container 00:00:00
- Using an interactive widget 00:00:00
- Using an interactive text widget 00:00:00
- Linking widgets together 00:00:00
- Another ipywidgets linking example 00:00:00
- Using a cookie cutter widget 00:00:00
- Developing an OPENGL widget 00:00:00
- Creating a simple orbit of one object 00:00:00
- Using a complex orbit of multiple objects 00:00:00
- Sharing Your Code 00:00:00
- Introduction 00:00:00
- Using a Notebook server 00:00:00
- Using a web server 00:00:00
- Sharing your Notebook through a public server 00:00:00
- Sharing your Notebook using nbviewer 00:00:00
- Converting your Notebook into a different format 00:00:00
- Converting Notebooks to R 00:00:00
- Converting Notebooks to HTML 00:00:00
- Converting Notebooks to Markdown 00:00:00
- Converting Notebooks to reStructedText 00:00:00
- Converting Notebooks to Latex 00:00:00
- Converting Notebooks to PDF 00:00:00
- Interacting with Big Data 00:00:00
- Introduction 00:00:00
- Obtaining a word count from a big-text data source 00:00:00
- Obtaining a sorted word count from a big-text source 00:00:00
- Examining big-text log file access 00:00:00
- Computing prime numbers using parallel operations 00:00:00
- Analyzing big-text data 00:00:00
- Analyzing big data history files 00:00:00
- Jupyter Labs 00:00:00
- Introduction 00:00:00
- Installing and starting JupyterLab 00:00:00
- JupyterLab display 00:00:00
- JupyterLab menus 00:00:00
- Starting a Notebook 00:00:00
- Starting a console 00:00:00
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