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Interactive Computing with Jupyter Notebook

Video Description

Gain hands-on experience in data analysis and visualization with IPython and Jupyter Notebook

About This Video

  • Leverage Jupyter Notebook for interactive data science and visualization
  • Become an expert in high-performance computing and visualization for data analysis and scientific modeling
  • Comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanations

In Detail

Python is one of the leading open source platforms for data science and numerical computing. IPython and the associated Jupyter Notebook offer efficient interfaces to Python for data analysis and interactive visualization, and they constitute an ideal gateway to the platform.

Interactive Computing with Jupyter Notebook, contains many ready-to-use, focused recipes for high-performance scientific computing and data analysis, from the latest IPython/Jupyter features to the most advanced tricks, to help you write better and faster code. This course covers programming techniques: code quality and reproducibility, code optimization, high-performance computing through just-in-time compilation, parallel computing, and graphics card programming.

In short, you will master relatively advanced methods in interactive numerical computing, high-performance computing, and data visualization.

The code bundle for the video course is available at - https://github.com/PacktPublishing/Interactive-Computing-with-Jupyter-Notebook

Table of Contents

  1. Chapter 1 : A Tour of Interactive Computing with Jupyter and IPython
    1. The Course Overview 00:04:28
    2. Introducing IPython and the Jupyter Notebook 00:07:30
    3. Getting Started with Exploratory Data Analysis in the Jupyter Notebook 00:06:25
    4. Introducing the Multidimensional Array in NumPy for Fast Array Computations 00:08:10
    5. Creating an IPython Extension with Custom Magic Commands 00:03:54
  2. Chapter 2 : Mastering the Jupyter Notebook
    1. Architecture of the Jupyter Notebook 00:03:12
    2. Converting a Jupyter Notebook to Other Formats with nbconvert 00:05:05
    3. Mastering Widgets in the Jupyter Notebook 00:06:53
    4. Creating Custom Jupyter Notebook Widgets in Python, HTML, and JavaScript 00:03:06
    5. Configuring the Jupyter Notebook 00:02:54
  3. Chapter 3 : Profiling and Optimizing
    1. Evaluating the Time Taken by a Command in IPython 00:03:02
    2. Profiling Your Code Easily with cProfile and IPython 00:04:09
    3. Profiling Your Code Line-by-Line with line_profiler 00:04:11
    4. Profiling the Memory Usage of Your Code with memory_profiler 00:03:40
    5. Understanding the Internals of NumPy to Avoid Unnecessary Array Copying 00:10:25
    6. Processing Large NumPy Arrays with Memory Mapping 00:02:52
  4. Chapter 4 : High Performance Computing
    1. Using Python to Write Faster Code 00:07:01
    2. Accelerating Pure Python Code with Numba and Just-In-Time Compilation 00:03:52
    3. Accelerating Array Computations with NumExpr 00:03:12
    4. Accelerating Python Code with Cython 00:04:37
    5. Releasing the GIL to Take Advantage of Multi-Core Processors 00:03:33
    6. Writing Massively Parallel Code for NVIDIA Graphics Cards (GPUs) 00:07:04
    7. Distributing Python Code Across Multiple Cores with IPython 00:04:13
    8. Interacting with Asynchronous Parallel Tasks in IPython 00:03:02
    9. Performing Out-of-Core Computations on Large Arrays with Dask 00:07:10
  5. Chapter 5 : Data Visualization
    1. Using Matplotlib Styles 00:03:32
    2. Creating Statistical Plots Easily with Seaborn 00:03:04
    3. Creating Interactive Web Visualizations with Bokeh and HoloViews 00:03:52
    4. Creating Plots with Altair and the Vega-Lite Specification 00:03:38