Fast Numerical Computing with Python

Performing numerical computations using conventional Python methods is inefficient as complex calculations can take their toll on your system’s performance. However, NumPy, the core library for scientific computing in Python, helps keep computations fast and lets your system perform efficiently.

This course adopts a step–by–step approach where you learn through live examples. You will learn numerical computations by performing them! Gain the skills you need to become a better Python developer or data scientist. Beginning with NumPy’s arrays and functions, you’ll master linear algebra concepts, perform vector and matrix math operations, and use NumPy in Python (using quick and easy techniques) to derive numerical results faster and far more easily than with any other tool. You will understand and practice data processing and predictive modeling throughout the course.

By the end of this course, you will have developed a strong foundation in solving numerical computational problems with NumPy. You will also have a really good knowledge of Python and will be ready to advance your career as a Python developer or data scientist.

About the Author

Manja Bogicevic has a mission to help decision–makers gain more profit with machine learning insights. She is one of the first self–made women data science entrepreneurs in the world. Currently, she is pursuing her Micromasters in MIT (Data Science & Statistics). She completed her MBA (Leader Project) at Ivey Business School in London, Canada and her BA at the Faculty of Economics in Belgrade, Serbia.