Learning IPython for Interactive Computing and Data Visualization [Video]

Delve into the fundamentals of the platform: Python, IPython, and the Jupyter Notebook while exploring data analysis tasks on real-world datasets

Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while Jupyter Notebook is a rich environment, well-adapted to data science and visualization. Together, these open-source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This course is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this course, you will be able to perform in-depth analyses of all sorts of data.

The code bundle for the video course is available at – https://github.com/PacktPublishing/Learning-IPython-for-Interactive-Computing-and-Data-Visualization

Style and Approach

This is a hands-on, beginner-friendly guide to analyzing and visualizing data on real-world examples with Python and Jupyter Notebook.

What You Will Learn

  • Install Anaconda and code in Python in Jupyter Notebook
  • Load and explore datasets interactively
  • Perform complex data manipulations effectively with pandas
  • Create engaging data visualizations with matplotlib and seaborn
  • Simulate mathematical models with NumPy
  • Visualize and process images interactively in Jupyter Notebook with scikit-image
  • Accelerate your code with Numba, Cython, and IPython.parallel
  • Extend the Notebook interface with HTML, JavaScript, and D3
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Authors

Cyrille Rossant

Cyrille Rossant, PhD, is a neuroscience researcher and software engineer at University College London. He is a graduate of École Normale Supérieure, Paris, where he studied mathematics and computer science. He has also worked at Princeton University and Collège de France. While working on data science and software engineering projects, he gained experience in numerical computing, parallel computing, and high-performance data visualization.

He is the author of Learning IPython for Interactive Computing and Data Visualization, Second Edition, Packt Publishing.

 

 

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