Download Create and Share Analytics with Jupyter Notebooks Course for Free

Create and Share Analytics with Jupyter Notebooks course. This course covers important aspects of working with Jupyter Notebooks, including installing and role kernels, magic functions, and running shell commands. Additionally, the power of cloud-hosted Jupyter Notebooks on AWS, Microsoft Azure, and Google Cloud Platform is explored. Python has grown in popularity in recent years, mainly because it makes analyzing and working with data so simple. Jupyter is more of a runtime than a full-blown IDE, but notebooks nonetheless have several important features that are worth fully understanding. In this course, Creating and Sharing Analyzes with Jupyter Notebooks, you’ll learn how Jupyter Notebooks is a major driver of Python’s popularity, providing an interactive and highly intuitive environment for running Python programs. First, you’ll learn how to work with Jupyter Notebooks and how to use features like Markdown to increase the readability of your code. Then, you’ll learn how more advanced features like magic functions work and how the next-generation Jupyter offering, JupyterLab, goes even further toward a fully-fledged development environment. Finally, you’ll round out your knowledge by working with cloud-hosted Jupyter Notebooks on any of the major cloud platforms. After completing this course, you will have the skills and knowledge to fully exploit the power of Jupyter Notebooks and JupyterLab, particularly in cloud-hosted notebooks for distributed and collaborative use cases. What you will learn How to set up and work with Jupyter Notebooks Effective use of Markdown features to improve code readability Understanding the magic functions and advanced features of Jupyter Working with JupyterLab as a more complete development environment Using Jupyter Notebooks hosted in the cloud (AWS, Azure, GCP) Running Python programs in an interactive environment Share and collaborate on Jupyter Notebook projects This course is suitable for people who They are interested in learning Python and data analysis. Looking for an interactive environment to run and test Python codes. Want to learn about Jupyter Notebooks and its advanced features. Plan to use Jupyter Notebooks for larger, collaborative projects. Looking for a way to increase productivity in data analysis.