Interactive Computing with Jupyter Notebook. With the help of this course you can Gain hands-on experience in data analysis and visualization with IPython and Jupyter Notebook.
This course was created by Packt Publishing. It was rated 4.9 out of 5 by approx 10981 ratings. There are approx 33222 users enrolled with this course, so don’t wait to download yours now. This course also includes 2.5 hours on-demand video, 1 Supplemental Resource, Full lifetime access, Access on mobile and TV & Certificate of Completion.
What Will You Learn?
Master all features in Jupyter Notebook
Code better: write high-quality, readable, and well-tested programs; profile and optimize your code; and conduct reproducible, interactive computing experiments
Visualize data and create interactive plots in Jupyter Notebook
Write blazingly fast Python programs with NumPy, ctypes, Numba, Cython, OpenMP, GPU programming (CUDA), parallel IPython, Dask, and more
Work with the most widely used libraries for data analysis: matplotlib, Seaborn, Bokeh, Altair, and others
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.
About the Author
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.
4.1
