Review of current state of Notebook 1

[ahmadia]

@labarba - this is significantly improved. I've got a bunch of small comments and suggested fixes below.

The sentence:

According to the University Corporation for Atmospheric Research (UCAR), there are global records of temperature available from several sources: NASA, the National Climatic Data Center (NCDC) and the University of East Anglia in the UK.

Seems out of place, along with the video, which is missing a scale. I would leave out the sources of temperature information (and the video) and spend more time talking about the data set we're using and where it came from.

The data is contained in the file: GlobalTemperatureAnomaly-1958-2008.csv with the year on the first column and 12 monthly averages of temperature listed sequentially on the second column. We will read the file, then make an initial plot to see what it looks like.

I don't know what the data file contains, but its not monthly temperature averages. It's a measurement of deviations/differences/anomalies of some form. Perhaps the difference from the monthly average over all years? Here's the first problem in the lesson. There's a general discussion earlier about rising global temperature, but now we're moving into global temperature anomalies, which are a different topic altogether. This is misleading. If the notebook is going to be an analysis of global temperature anomalies, it should start with a discussion of global temperature anomalies and move forward from there.

Now we've added something more to our import call to load the library. What's this? Well, we're actually loading pyplot, which is a component of Matplotlib, and we're telling Python that we want to rename it locally as plt ... we're just lazy and want to type less.

Also slightly misleading. We're importing matplotlib, but we're only going to be using functions from one component, pyplot, so we use the import matplotlib.pyplot as plt statement to give us a simple reference to the component which we've named (by convention) plt.

The plot function can take another input; see if you can get it worked out with the year displayed as well.

The solution is posted immediately afterwards, I assume this is the "instructor solution" version?

The temperature certainly seems to be increasing. But we're not going to stop there, of course. It's not that easy to convince people that the planet is warming, as you know.

No, the temperature anomaly (or whatever this data is, I still don't know) is increasing. We should be precise here.

which in Python start with the symbol #.

-> which follow the # symbol in Python.

You see a lot of scatter

-> You see a lot of small fluctuations?

also knows as

-> also known as

It means that we are taking the unweighted mean of the previous $n$ data points.

-> The only parameter to the moving average is the value $n$. As you can see, the moving average smooths the set of data points by creating a new data set consisting of local averages (of the $n$ previous data points) at each point in the new set.

We chose a window

-> We use a window

the scatter in the data is not random

-> The fluctuations in the data are not random

For the global temperature data, this discussion is crucial: many critics claim that since there is auto-correlation in the data, no reliable trends can be obtained!

global temperature anomaly data?

As a well-educated engineering student, who cares about the planet

Remove the comma between student and who -- it reverses the intent of the sentence.

appreciatd

-> appreciate

[gforsyth]

The target for these lessons are incoming engineering freshmen, yes? If that's the case, I think we need to be a little careful with statements like:

Then, we are going to smooth the data and perform linear regression. We are going to investigate correlation in the residuals, then correct for autocorrelation, and in each step use plots to learn from our data.

My background is atypical, but 4 years ago that would've read to me as complete gibberish. I would remove it entirely -- we can walk them through each of those steps as they get to the appropriate section.

[labarba]

Thanks, both. I've gone through all the comments. I think with the way I reorganized the presentation of the sources of data, NASA, video, etc., it is now much more clear. But I definitely want to keep that lede and the video. Ideally, I would like some media in every lesson. It's the hook. (The whole notion of "context-based" computing is that the students don't care about manipulating data, they care about solving a problem and "saving the world.")

Thanks for catching the confusions of "temperature" with "temperature anomaly" throughout. I think that's fixed now.

@gforsyth : that was a nice catch!

[ahmadia]

Excellent, let's close this issue.