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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
+- [ ] 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)
+
+---
+## Discussion 
+
+
+---
+## 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!)_**
diff --git a/tutorials/module_4/4.0 Outline.md b/tutorials/module_4/4.0 Outline.md
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+# Module 4: Outline
+
+1. Introduction to Data and Scientific Datasets
+	a. What is scientfic data
+	b. Data Processing flow work
+	c. Intro to Pandas
+		d. Manipulating data frames
+	e. Problem: Create a daraframe from Numpy arrays
+
+2. Interpreting Data
+	a. Understanding your data
+	b. Purpose
+	c. Composition
+	d. Color
+	e. Problem 1: Composing or fixing a plot
+	f. Data don't lie
+	g. Problem 2: Misleading plots
+
+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
+
+4. Statistical Analysis
+	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
+
+5. Statistical Analysis
+	a. Linear Square Regression and Line of Best Fit
+		b. Linear
+		c. Exponential and Power functions
+		d. Polynomial**m 2:** From the DataFrame, add a
+		e. Using scipy
+		f. How well did we do? (R and R^2)
+	g. Extrapolation
+	h. Moving average
+
+6. Data Filtering and Signal Processing
+	
+7. Data Visualization and Presentation
+	a. Problem: Using pandas to plot spectroscopy data from raw data.
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