Overview

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Build Jupyter and Python skills for data visualization, notebook productivity, and high-performance computing. You’ll begin by configuring Jupyter Notebook and using IPython for markdown, calculations, documentation, and interactive code execution. You’ll then use Matplotlib and NumPy to create and customize line, scatter, histogram, bar, pie, and polar charts. As you progress, you’ll design scientific visualizations with annotations, multiple and logarithmic axes, date formatting, Mathtext, LaTeX rendering, contour plots, and image plotting. You’ll work with IPython magic commands, configuration options, HTML and JavaScript rendering, interactive widgets, kernels, and unit testing for reliable notebook workflows. Next, you’ll focus on Python performance optimization. You’ll convert notebooks to HTML and LaTeX, handle structured data with JSON, profile code, use memory mapping for large NumPy arrays, and create real-time interactive applications. Finally, you’ll accelerate Python with Numba, Cython, and C integration; execute asynchronous, parallel, distributed, and cluster-based computing; and explore advanced visualization with Seaborn, D3.js, and Julia. Designed for beginners learning Jupyter and practitioners improving data science, research, or analytics workflows, this course provides a path from setup and plotting to optimized, scalable computing. Enroll to create clearer visualizations, work efficiently in IPython, and improve data-driven application performance.

Syllabus

Getting Started with Jupyter & IPython
- 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.
Mastering Data Visualization
- 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.
Professional Plotting Techniques
- 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.
Advanced IPython Functionalities
- 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.
Optimizing Python for Performance
- 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.
High-Performance & Parallel Computing
- 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.