Learn how to use NumPy for scientific computing and build the foundations of Data Science and large data processing
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NumPy was the library that got started the Data Science / Python revolution. In this course we'll explore all the fundamental concepts it introduced: low level bindings (C, Fortran), multidimensional arrays, vectorized operations, etc. The final project will involve matrix processing and a lot of thinking writing a Sudoku Solver!
"For this course, I focused on drawing the parallelism between basic Python data structures and NumPy arrays, and specially, how to apply it to scientific computing, without forgetting about performance: how faster is NumPy compared to Python?"
Intro to Numpy
Boolean Arrays and Masking
Summary statistics using NumPy
Comparing NumPy efficiency vs Pure Python
Introduction to NumPy Boolean Arrays
Boolean Arrays Exercises 1
Boolean Arrays Exercises 2
Intro to Vectorized Operators using NumPy
Vectorized Operators Exercises
NumPy universal functions
Universal functions Exercises
Intro to NumPy
Basic NumPy Exercises 1
Basic NumPy Exercises 2
Intro to Linear Algebra using NumPy
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Data Scientist at RMOTR
Santiago is an experienced Data Scientist and Python programmer with more than 10 years of experience in the field. He started as a Java developer working as a consultant on high performance and critical systems before moving to Python. In 2012 was hired as the CTO of Athlete.com, a startup analyzing data from runners. He then fell in love with Data processing with Python and in 2015 founded RMOTR, to provide expert-level Data Science training.