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diff --git a/tutorials/module_4/Schlieren example.md b/tutorials/module_4/Schlieren example.md
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+Summary of things to ask students to do:  
+
+- Read density images (txt files) into Python
+- Compute density gradient at each pixel in the image for a bunch of images
+- Make schlieren movie out of the density gradient files
+- For density images between 1 - 10 us cut the image over the central plane of the kernel and plot Density vs. Radius. Obtain Temperature vs. Radius for a given pressure of 1 atm using equation of state.
+
+
+Read density data.
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+Compute density gradient for each
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+Make Schlieren image for each
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+
diff --git a/tutorials/module_4/Spectroscopy problem.md b/tutorials/module_4/Spectroscopy problem.md
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+## Background
+
+
+
+Example problem on Data Processing - Optical Emission Spectroscopy
+
+
+- Import xls data into Python
+- Plot the Intensity [a.u.]  vs pixels
+- Interpolate and convert x-axis from pixels to nm (true wavelength) using Hg lamp data (using data in file: Lampa_Calibrare_Mercur.xlsx)
+- Find response function of the spectrometer using the tungsten lamp data from file: "Calibrare Intensitate Oxigen.xlsx)": R=I_measured/I_true (where True is computed by Planck's law of radiation (see notes in the pptx above)
+- Convert y-axis from Intensity [a.u.] into Intensity in [W/(cm^2*sr*nm)] by dividing the measured Oxygen spectrum with the response function: I_oxygen_true=I_oxygen_measured/R
+- Once the spectra is in real units: compute the density of one of the oxygen lines by integrating underneath one of the peaks (see equation from Slide 39 - bottom). We will give all of the constants that are in this equation (see the "Intensity_Calibration_Oxygen_Discharge_Solution.xlsx")
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+
+
+Problem 1: Import the data as a pandas dataframe.
+
+Problem 2: Using the known wavelength intensities of mercury, identify the wavelengths.
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+Problem 3: 
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