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Learn to use IPython and Jupyter Notebook for your data analysis and visualization work.
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 deteistic 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 expents
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, rag 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.
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
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