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## Updates
- https://pythonnumericalmethods.studentorg.berkeley.edu/notebooks/Index.html
- Numerical Methods for Engineers Steven C Chapra
---
## Topics
and questions
- [ ] Decide on textbook
- [ ] Spectroscopy problem
- [ ] Module 4: Revise lecture order for 4.2 plotting philosophy
- [ ] Module 5: Go over outline
---
## 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
---
## Action
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