Course

Unit 1 – Programming in PythonPython programming style Plotting Data input/output Error analysis and non-dimensionalization Lagrange Interpolation – SplinesUnit 2 – Numerical AlgorithmsNumerical Integration Newton Cotes Gaussian quadrature Differentiation ODE solvers Eulers Method Fourier TransformsUnit 3 – Data analysis and visualization (Matplotlib)Generating Data Plotting a simple line graph Random walks Rolling Dice customizing plots visualizing distributions with histograms techniques for handling skewed data – Unit 4 Introduction to NumPyIntroduction to NumPy Computing using formulas – Vectorization Broadcast – Identify matrix Indexing and Slicing Fancy Array Reduction Operation Random Numbers and histograms Linear Algebra method walkthrough PCA ImplementationUnit 4 Introduction to SciPyClustering algorithms Physical and mathematical constants Fast Fourier transform routines integrate interpolate optimize signal processing sparse matrices and associated routinesLabUse NumPy to perform numerical linear algebra operations.Use SciPy to solve a differential equation.Use Matplotlib to visualize data.Use a scientific computing library to perform a specific scientific computing task, such as computing the Fourier transform of a signal or finding the eigenvalues of a matrix.

Reference:1.Scientific Computing with Python by Eric Jones, Travis Oliphant, and Pearu Peterson2.Numerical Python: A Practical Techniques Approach for Industry, Robert Johansson