Minor changes to Narrative text

Author: mike-ivs

_episodes/03-matplotlib.md

@@ -35,10 +35,13 @@ corresponds to a day in the dataset.  Blue pixels in this heat map represent low
 pixels represent high values.  As we can see, the general number of inflammation flare-ups for the patients
 rises and falls over a 40-day period.
 
-So far so good, this is in line with our knowledge of the clinical trial and Dr. Maverick's claims:
-the patients take their medication once their inflammation flare-ups begin; it takes around 3 weeks
-for the medication to take effect and begin reducing flare-ups; and flare-ups appear to drop to zero
-by the end of the clinical trial.  Now let's take a look at the average inflammation over time:
+So far so good as this is in line with our knowledge of the clinical trial and Dr. Maverick's claims:
+
+* the patients take their medication once their inflammation flare-ups begin
+* it takes around 3 weeks for the medication to take effect and begin reducing flare-ups
+* and flare-ups appear to drop to zero by the end of the clinical trial.
+
+Now let's take a look at the average inflammation over time:
 
 ~~~
 ave_inflammation = numpy.mean(data, axis=0)
@@ -51,7 +54,7 @@ matplotlib.pyplot.show()
 
 Here, we have put the average inflammation per day across all patients in the variable
 `ave_inflammation`, then asked `matplotlib.pyplot` to create and display a line graph of those
-values.  The result is a roughly linear rise and fall, in line with Dr. Maverick's claim that the
+values.  The result is a reasonably linear rise and fall, in line with Dr. Maverick's claim that the
 medication takes 3 weeks to take effect.  But a good data scientist doesn't just consider the
 average of a dataset, so let's have a look at two other statistics:
 
@@ -71,10 +74,10 @@ matplotlib.pyplot.show()
 
 ![A line graph showing the minimum inflammation across all patients over a 40-day period.](../fig/inflammation-01-minimum.svg)
 
-The maximum value rises and falls linearly, while the minimum seems to be a step function.  
-Suspicious... neither trend seems particularly likely, so either there's a mistake in our
-calculations or something is wrong with our data.  This insight would have been difficult
-to reach by examining the numbers themselves without visualization tools.
+The maximum value rises and falls linearly, while the minimum seems to be a step function.
+Neither trend seems particularly likely, so either there's a mistake in our calculations or
+something is wrong with our data. This insight would have been difficult to reach by examining
+the numbers themselves without visualization tools.
 
 ### Grouping plots
 You can group similar plots in a single figure using subplots.

_episodes/04-lists.md

@@ -21,7 +21,7 @@ list[2:9]), in the same way as strings and arrays."
 ---
 
 In the previous episode, we analyzed a single file of clinical trial inflammation data. However,
-after finding some peculiar, and potentially suspicious, trends in the trial data we ask
+after finding some peculiar and potentially suspicious trends in the trial data we ask
 Dr. Maverick if they have performed any other clinical trials. Surprisingly, they say that they
 have and provide us with 11 more CSV files for a further 11 clinical trials they have undertaken
 since the initial trial.

_episodes/05-loop.md

@@ -21,7 +21,7 @@ inflammation dataset (`inflammation-01.csv`), which revealed some suspicious fea
 
 ![Line graphs showing average, maximum and minimum inflammation across all patients over a 40-day period.](../fig/03-loop_2_0.png)
 
-We have a dozen data sets right now, though, and potentially more on the way if Dr. Maverick
+We have a dozen data sets right now and potentially more on the way if Dr. Maverick
 can keep up their surprisingly fast clinical trial rate. We want to create plots for all of
 our data sets with a single statement. To do that, we'll have to teach the computer how to
 repeat things.

_episodes/06-files.md

@@ -45,6 +45,7 @@ This means we can loop over it
 to do something with each filename in turn.
 In our case,
 the "something" we want to do is generate a set of plots for each file in our inflammation dataset.
+
 If we want to start by analyzing just the first three files in alphabetical order, we can use the
 `sorted` built-in function to generate a new sorted list from the `glob.glob` output:
 
@@ -108,13 +109,13 @@ maximum and minimum inflammation over a 40-day period for all patients in the th
 dataset.](../fig/03-loop_49_5.png)
 
 
-Hmmm. The plots generated for the second clinical trial file look very similar to the plots for
+The plots generated for the second clinical trial file look very similar to the plots for
 the first file: their average plots show similar "noisy" rises and falls; their maxima plots
 show exactly the same linear rise and fall; and their minima plots show similar staircase
 structures.
 
 The third dataset shows much noisier average and maxima plots that are far less suspicious than
-the first two datasets, however the minima plot shows that the third dataset minima are
+the first two datasets, however the minima plot shows that the third dataset minima is
 consistently zero across every day of the trial. If we produce a heat map for the third data file
 we see the following:
 
@@ -124,7 +125,7 @@ the entire dataset, and the last patient only has zero values over the 40 day st
 We can see that there are zero values sporadically distributed across all patients and days of the
 clinical trial, suggesting that there were potential issues with data collection throughout the
 trial. In addition, we can see that the last patient in the study didn't have any inflammation
-flare-ups at all throughout the trial, suggesting that they may not even suffer from arthritis.
+flare-ups at all throughout the trial, suggesting that they may not even suffer from arthritis!
 
 
 > ## Plotting Differences
@@ -213,7 +214,7 @@ flare-ups at all throughout the trial, suggesting that they may not even suffer
 
 After spending some time investigating the heat map and statistical plots, as well as
 doing the above exercises to plot differences between datasets and to generate composite
-patient statistics, we gain some insight into the twelve clinical trial datasets:
+patient statistics, we gain some insight into the twelve clinical trial datasets.
 
 The datasets appear to fall into two categories:
 
@@ -231,11 +232,11 @@ duplicated files.
 Dr. Maverick confesses that they fabricated the clinical data after they found out
 that the initial trial suffered from a number of issues, including unreliable data-recording and
 poor participant selection. They created fake data to prove their drug worked, and when we asked
-for more trials they tried to generate more fake sets, as well as throwing in the original
-poor-quality dataset a few times to try and make all the trials seem more "realistic".
+for more data they tried to generate more fake datasets, as well as throwing in the original
+poor-quality dataset a few times to try and make all the trials seem a bit more "realistic".
 
-Congratulations! We've cracked the case and proven that the inflammation datasets have been
-synthetically generated (in python no less!).
+Congratulations! We've investigated the inflammation data and proven that the datasets have been
+synthetically generated.
 
 But it would be a shame to throw away the synthetic datasets that have taught us so much
 already, so we'll forgive the imaginary Dr. Maverick and continue to use the data to learn

index.md

@@ -10,10 +10,11 @@ so this introduction to Python is built around a common scientific task:
 
 ### Scenario: A Miracle Arthritis Inflammation Cure
 
-Our colleague Dr. Maverick has invented a new "miracle drug" that promises to cure arthritis
-inflammation flare-ups after only 3 weeks since initially taking the medication! Naturally,
-we wish to see the clinical trial data and after months of asking them they have finally
-provided us with a CSV spreadsheet containing the clinical trial data.
+Our imaginary colleague "Dr. Maverick" has invented a new miracle drug that promises to
+cure arthritis inflammation flare-ups after only 3 weeks since initially taking the
+medication! Naturally, we wish to see the clinical trial data, and after months of asking
+for the data they have finally provided us with a CSV spreadsheet containing the clinical
+trial data.
 
 The CSV file contains the number of inflammation flare-ups per day for the 60 patients
 in the initial clinical trial, with the trial lasting 40 days. Each row corresponds to a