MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

June 28, 2019 | ISBN: 9781838823603 | English

Duration: 67 Lessons (9h 7m) | Size: 7 GB

Learn to use IPython and Jupyter Notebook for your data analysis and visualization work.

Learn

Visualize data and create interactive plots in the Jupyter Notebook

Write fast Python programs with NumPy, ctypes, Numba, and other libraries

Analyze data with Bayesian or frequentist statistics

Simulate deterministic and stochastic dynamical systems in Python

Get familiar with math in Python using SymPy and Sage

Profile and optimize your code and conduct reproducible interactive computing experiments

About

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.

This course is equipped with several ready-to-use, focused recipes for high-performance scientific computing and data analysis to help you write better and faster code. You’ll be able to apply your learnings to various real-world examples, ranging from applied mathematics, scientific modeling, to machine learning. The course introduces you to effective programming techniques such as code quality and reproducibility, code optimization, and graphics card programming. You’ll also learn how to use different features of IPython and Jupyter Notebook in data science, signal and image processing, and applied mathematics.

By the end of this course, you’ll learn how to easily analyze and visualize all types of data in Jupyter Notebook.

Features

Use Jupyter Notebook for interactive data science and visualization

Get up to speed with high-performance computing and scientific modeling

Learn best practices and tricks to write high-quality, readable, and well-tested programs