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Learn MoreIntro to Scientific Computing with NumPy
What about this course?
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!
Duration
9 hours
Difficulty
professional
INE's world-class IT trainingInstructor for this course
Santiago Basulto
This course is composed by the following modules
INE's world-class IT training
Module 1
Intro to Numpy16 videos
Intro to Numpy
Create simple array
Create float array
Sum first and last elements
Sum all elements
Create matrix from table
Slicing elements from matrix
Slicing columns from matrix
Operations on vectors
Vector conversion
Intro to Linear Algebra using NumPy
Creating matrices
Matrix multiplication
Determinant of matrix
Inverse of matrix
Evenly spaced elements in array
Module 2
Vectorized operations12 videos
Intro to Vectorized Operations using NumPy
Creating matrix and vector
Adding vector over matrix
Standardization of matrix
Elements lower than in matrix
Proportion greater than in matrix
NumPy universal functions
Multiply every element of array
Absolute values within array
Exponential all elements in array
Radian values of angles
Trigonometry of radians
Module 3
Boolean Arrays and Masking8 videos
Introduction to NumPy Boolean Arrays
Slice positive elements from matrix
Slice elements with and multiple boolean filters
Slice elements with or multiple boolean filters
Boolean arrays with addition on matrix
Greater than in matrix boolean response
All elements even in matrix
Adding even numbers in matrix
Module 4
Advanced NumPy7 videos
Summary statistics using NumPy
Matrix statistics
Mean per column in matrix
Sum per row in matrix
Cumulative sum per row in matrix
Comparing NumPy efficiency vs Pure Python
Read CSV into a numpy array
Module 5
Final Project1 video
Numpy Sudoku Solver
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