A comprehensive interactive reference for Exploratory Data Analysis based on the NIST/SEMATECH Engineering Statistics Handbook. 90+ pages covering graphical techniques, quantitative methods, probability distributions, and case studies.
Starter notebook demonstrating sine wave generation with noise, using pandas for data processing and spectrogram visualization in Python.
Applies computational mathematics using SymPy for symbolic, numerical, and graphical approaches to building an ECG model.
Companion Jupyter notebook for the SymPy ECG model post, providing runnable code for electrocardiogram modeling.
Discusses SciPy framework optimization techniques for data science using pandas and NumPy for improved performance.
Testing notebook demonstrating how to optimize Python code with pandas and NumPy for improved computational efficiency.
Examines Jupyter magic commands for systematically identifying and resolving performance bottlenecks in Python code.
Notebook for profiling slow performance in Python using mprun magic command with code in separate files.
Explores machine learning approaches applied to frequency domain analysis of time-series data.
Mathematical exploration of computing Fourier series from data points, with applications to machine learning using Python and Jupyter Notebook.
Discusses PCA, SVD, covariance, and dimensionality reduction techniques for large dataset analysis and image compression.
