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.