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## 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
- [ ] Module 4: Revise lecture order for 4.2 plotting philosophy
- [ ] Module 5: Go over outline
- [ ] Waiting on experimental/supplementary data from lab engineers (MECH 338)
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## Discussion
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## Actions
Module 4: Data Analysis and Processing
- 4.1. Introduction to Data Analysis (describe the Panda Toolbox) -> One Lecture
- 4.2. We should focus on effective ways of looking at data (philosophy of making that make sense) -> One Lecture
- 4.3. I/Ofile handling (different types of data files: ASCII, CSV, TXT, JPEG,...) -> Here they will learn how to read/handle: datasets including images. -> One Lecture
- 4.4. Statistical Anasis (mean/averaging, standard deviation, variance...) -> **_Reminder: Ciprian to send application from Spectroscopy and CFD -> One Lecture_**
- 4.5. Data Cleaning (linear regression, least square fitting, extrapolation, moving average) -> One Lecture
- 4.6. Data Filtering & Signal Processing (filters: Gaussian Filter, Sobel edge detection, Kalman, Butterworth, FFT) -> We need to give a lecture of what is/how it works Digital filter design (lowpass, highpass, bandpass, filter response curves, how does one select a given filter). One lecture all theory so that in the second lecture they learn how to select different filters. -> Two lectures
- 4.7 Image processing: apply some of the filtering to make images sharper/crispier, teach them how to make videos/gifs out of images. -> One Lecture. Possible application: **_from CFD density data obtain Synthetic Schlieren images (Ciprian will send you this!)_**
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