## Updates
- https://pythonnumericalmethods.studentorg.berkeley.edu/notebooks/Index.html
- Numerical Methods for Engineers Steven C Chapra

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## Topics
and questions

- [ ] Decide on textbook
- [ ] Spectroscopy problem
- [ ] Module 4: Revise lecture order for 4.2 plotting philosophy
- [ ] Module 5: Go over outline

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## Discussion 

- Slight changes to module 4 outline

> Module 4: Outline
>1. Introduction to Data and Scientific Datasets
> 	a. What is scientific data
> 	b. Data Processing flow work
> 	c. Intro to Pandas
> 		d. Manipulating data frames
> 	e. Problem 1: Create a dataframe from Numpy arrays
> 	f. Problem 2: Selecting data from a dataframe to calculate work done.
> 
> 2. Interpreting Data
> 	a. Understanding your data
> 	b. Purpose
> 	c. Composition
> 	d. Color
> 	e. Problem 1: Composing or fixing a plot. Apply PCC
> 	f. Data don't lie
> 	g. Problem 2: Misleading plots by changing axis limits or omitting context. Explain *why* it's misleading.
> 
> 3. Importing, Exporting and Managing Data
> 	a. File types
> 	b. Importing spreadsheets with pandas
> 	c. Handling header, units and metadata
> 	d. Writing and editing data in pandas
> 	e. Problem: Importing time stamped data pressure and temperature data. Convert timestaps to datetime and plot timperature vs. time
> 	f. Problem: [TBD]
> 
> 4. Statistical Analysis I
> 	a. Engineering Models
> 	b. Statistics Review
> 	c. Statistics function in python (Numpy and Pandas describe)
> 	d. Statistical Distributions
> 	e. Spectrocopy (basics) 
> 	f. Problem: Statistical tools in Spectroscopy readings (intensity vs wavelangth) to compute mean, variance and detect outliers.
> 	g. Problem 2: Fit a Gaussian distribution to the same data and overlay it on the histogram.
> 
> 5. Statistical Analysis II: Regression and Smoothing
> 	a. Linear Square Regression and Line of Best Fit
> 		b. Linear, Exponential and Power functions
> 		d. Polynomial
> 		e. Using scipy
> 		f. How well did we do? (R and R^2)
> 	g. Extrapolation and limitations
> 	h. Moving averages
> 	i. Problem 1: Fit a linear and polynomial model to stress-strain data. Compute R^2 and discuss which model fits better.
> 	j. Problem 2:  Apply a moving average to noisy temperature data and compare raw vs. smoothed signals.
> 
> 6. Data Filtering and Signal Processing
> 	a. What is it and why it matters - noise vs. signal
> 	b. Moving average and window functions
> 	c. Frequency domain basics (sampling rate, Nyquist frequency)
> 	d. Fourier transform overiew (numpy.fft, scipy.fft)
> 	e. Low-pass and high-pass filters (scipy.singla.butter, filtfilt)
> 	f. Example: Removing high-frequency noise from a displacement signal
> 	g. Example: Removing noise from an image to help for further analysis (PIV)
> 	h. Problem 1: Generate a synthetic signal (sum of two sine waves+random noise). Apply a moving average and FFT to show frequency components.)
> 	i. Problem 2: Design a Butterworkth low-pass filter to isolate the funcamental frequency of a vibration signal (e.g. roating machinery). Plot before and after.
> 
> 7. Data Visualization and Presentation
> 	a. Review of PCC framework
> 	b. Plotting with Pandas and Matplotlib
> 	c. Subplots, twin axes, and annotations
> 	d. Colomaps and figure aesthetics
> 	e. Exporitn gplots for reports (DPI, figure size)
> 	f.  Creating dashboards or summary figures
> 	g. Problem 1: Using pandas to plot spectroscopy data from raw data. Add labels, units, title, and annotations for peaks
> 	h. Problem 2: Create a multi-panel figure showing raw data, fitted curve, and residuals. Format with consistent style, legend and color scheme for publication-ready quality.
> 	


Module 5: Outline

Lectures
1. Supervised learning: Classification
2. Supervised learning: Regression
3. Unsupervised learning: Clustering
4. Unsupervised learning: Dimensional reduction

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## Action