CompClass
Week 1: Basics
Introduction
Basics of Programming
Week 2: Basics 2
Object Oriented Programming
Errors Accumulation
Week 3: Numerical Computing
Numerical Tools
Plotting
Week 4: Randomness
Random numbers
Random Walk
Monte Carlo
IPython Magics
Week 5: Integration and Differentiation
Integration Rules
Monte Carlo Integration
Numerical Differentiation
Week 6: Linear Algebra
Vectorization
Week 6 Day 2: Linear Algebra
String Masses Classic
String Masses Final
Worked problem: The string problem
Week 6 Day 3: Fitting
Spline Fit
Week 7: Tabular data
Week 7 Day 1: Structured data (AKA: Pandas DataFrames)
Week 7 Day 1: Worksheet
Week 7 Day 2: Cuts and histograms
Week 8: Statistics
Week 8 Day 1: Generating distributions
Week 8 Day 2: Fitting distributions
Week 8 Day 3: Fitting tools
Week 9: Statistics 2
Week 9 Day 1: Confidence intervals
Week 9 Day 2: Markov Chain Monte Carlo
Week 9 Day 2: Markov Chain Monte Carlo
Week 10: Differential Equations
Week 10 day 1: ODEs
Week 10 Day 1: (Pro)file
Week 10 Day 3: ODE problems
Week 10 Day 2: Runge–Kutta algorithm
Week 11: Fourier Series
Week 11 Day 1: Introduction to Fourier Series
Week 11 Day 2: Fast Fourier Transform (FFT)
Week 12: Assorted Topics
Cupyfractal
Pybindnumba
Week 12 Day 1: Requested topic: Fitting
Week 12 Day 1: Requested topic: GUIs
Week 12 Day 2: Signal filtering
Week 13: Review
Week 13 Day 1: Review
Week 14: Requested Topics
Week 14 day 1: Static and Dynamic Computation Graphs
Week 14 Day 2: Intro to Machine Learning
MNIST dataset
Week 14 Day 3: Sharing and documenting code
Optional
Overview of Python
Python 2 versus 3
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