The independence requirement for the t-test is not really relevant here, as you have only one students's data (If you had data for more students, that would be more of an issue) ... but there is also an assumption of normal distribution and that is also doubtful here. You cannot use a paired test as this is not paired data. 

I would here use a **permutation test**. Under the assumption that the scoring is the same for science and non-science courses, the labels `science`, `non-science` is just like they were attached arbitrarily to the courses. So you can simulate the permutation distribution of the difference of the means, say, by permuting the labels, say, $R=9999$ times, and each time compute the differences of the means. Draw the histogram, and over that, draw the observed difference as a line. 

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After questions in comments: Why is the independence assumption not relevant in this case with data from only one person? Because of *exchangeability*. Under the null hypothesis of no difference between groups, the data is exchangeable, see for instance https://stats.stackexchange.com/questions/362072/wilcoxon-signed-rank-test-independence-assumption  and search this site.  

So with your example data: Well, my internet is so slow now, probably because of everybody homeofficing for our friend Corona ... Coming back again when it is working ...