What you'll get
  • 14+ Hours
  • 2 Courses
  • Mock Tests
  • Course Completion Certificates
  • Self-paced Courses
  • Technical Support
  • Case Studies

Synopsis

  • Use the interactive and powerful Jupyter Notebook environment
  • Create your own notebooks in Python, Julia, R, and other supported languages
  • Run code and view results across 40+ programming languages
  • Create and visualize graphs, plots, and charts directly in the notebook
  • Work more efficiently using essential Jupyter Notebook keyboard shortcuts

Content

Courses No. of Hours Certificates Details
Jupyter-IPython Notebook Training - Beginners6h 05m✔View Curriculum
Jupyter-IPython Notebook Training - Advanced7h 5m✔View Curriculum
Courses No. of Hours Certificates Details
No courses found in this category.

Description

This beginner-friendly course introduces you to Jupyter Notebook, one of the most widely used tools in data science, machine learning, and scientific computing. Whether you are using Python, R, or Julia, Jupyter Notebook gives you an interactive space to write code, view results immediately, take notes, and create shareable documents.

Throughout this course, you will learn how Jupyter Notebook supports over 40 programming languages and why it has become essential for data-driven fields. From running quick code snippets to visualizing charts and sharing complete notebooks, you will discover how versatile and powerful this tool can be.

You will also learn basic Python while using the notebook, making this course perfect for beginners.

By the end of the course, you will feel confident using Jupyter Notebook to experiment with code, document your work, and create interactive, professional-quality notebooks you can share with others.

Requirements

  • Access to a working computer

  • Basic Python knowledge is helpful but not required

  • Stable internet connection for setup and downloads

  • Willingness to work with command-line tools.

Target Audience

  • Anyone who wants to use Jupyter Notebooks

  • Python Programmers

  • Data Scientists and Data Analysts

  • Machine Learning Engineers.