By the end of this course, learners will be able to configure Jupyter and IPython environments, create professional data visualizations with Matplotlib, enhance graphs with NumPy, and apply advanced scientific plotting techniques. They will also master IPython functionalities such as widgets, magic commands, kernels, and unit testing while optimizing Python performance with profiling tools, memory mapping, and conversions. Finally, learners will accelerate Python with Numba and Cython, implement parallel and distributed computing, and explore next-generation visualization with Seaborn, D3.js, and Julia.
Jupyter & Python: Visualize, Optimize & Accelerate
Gain insight into a topic and learn the fundamentals.
2 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
What you'll learn
Configure Jupyter/IPython and create advanced visualizations with Matplotlib & Seaborn.
Optimize Python performance with profiling, memory mapping, Numba, and Cython.
Implement parallel/distributed computing and integrate next-gen visualization tools.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
October 2025
Assessments
23 assignments
Taught in English
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There are 6 modules in this course
This module introduces learners to Jupyter and IPython environments, focusing on setup, configuration, and basic operations. Students will gain confidence in using notebooks for code execution, documentation, and fundamental computations.
What's included
10 videos3 assignments
This module covers the foundations of data visualization using Matplotlib and NumPy. Learners will explore different chart types and understand how to enhance them for meaningful insights.
What's included
17 videos4 assignments
This module emphasizes advanced plotting skills, including annotations, multi-axis visualizations, and scientific plotting features. Learners will be able to design professional-quality graphs for analytical and presentation purposes.
What's included
13 videos4 assignments
This module explores advanced IPython features including HTML/JavaScript rendering, magic commands, and kernel management. Learners will also discover automated testing for reliable and reproducible workflows.
What's included
13 videos4 assignments
This module introduces optimization methods, interactive widgets, and profiling techniques. Learners will improve code efficiency and handle large datasets effectively using NumPy and real-time interactions.
What's included
18 videos4 assignments
This module focuses on high-performance computing with Numba, Cython, and parallel computing strategies. Learners will also explore advanced visualizations and Julia integration for next-generation numerical computing.
What's included
26 videos4 assignments
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