Principles of Computing (Part 1)

Disclaimer: The course has been split into a Part 1 and Part 2; I've completed Part 1 only at this point.

Building on their first course, Introduction to Interactive Programming in Python (IIPP), this class aims to set a higher standard of programming and conceptual understanding (of the role of certain math topics in programming, in particular) for students.

The class differs from IIPP in several respects. As mentioned above by Gregor, almost immediately students are introduced to math concepts like combinatorics, probability, and growth rates (although the topics are treated at a relatively elementary level; the professors assured us that a more detailed discussion of the role of math in algorithms would commence in "Algorithmic Thinking 1 & 2). As opposed to IIPP, programs are now machine graded and must meet exacting programming guidelines (designed to mimic real-world restrictions). In addition, the projects are more ambitious than in IIPP, and require a more robust programming repertoire.

The highlight of the course are, as seems to be with this specialization, the projects themselves. The themes are fun and engaging and don't at all dilute the rigor of the concepts themselves. The homework problems are were personally more of an annoyance for me, but the do serve to cement concepts, practice coding, and prepare one for the projects.

My one real frustration with the course is that the instructions for the projects were not nearly as clear as in IIPP (although, in their defense, the projects in PoC were more complex). However, the instructions were just clear enough to get me through completion.

So far I've learned a great deal regarding object oriented coding in python, list comprehensions, manipulation of data structures, and much, much more. The course is well worth taking and well worth the money.

A big step up from IIPP. Does a great job of starting to transition students from introductory, highly guided projects toward more independent research and experimental exploration of the scientific underpinnings of coding with Python. The homework (== quiz from IIPP) continues to build on the lectures in preparation for the weekly mini-project. There is no longer any UI component to the assignment, instead to focus is on problem solving methodologies using mathematics, statistics, and probability. I havent taken a math course in 20 years, so I had to spend a couple of extra hours per week reading and sometimes researching the material, but ultimately nothing required any truly advanced math beyond high school trig or first year college level math. Overal I thought the supporting materials to be way more applicable and helpful than they were in IIPP, but missed having Greiner's dedicated lectures on common gotchas and pitfalls. The mysterious, highly elusive Wong was thankfully replaced by Luay who has a great sarcastic, understated sense of humor that really shines in each week's introductory lecture (which streams at a much higher production value than the remaining videos). Testing plays a prominent role in this series, and to a lesser degree, graphing. Im hoping I can keep up with Part 2, as some of the materials really require some dedication to fully understand! There is enough material presented that you could probably take this course twice and still learn a ton of new things on the second pass.

On the mathematical side, Principles of Computing covered arithmetic sums, basic functions in order to describe growth rates, basic probability and a bit of combinatorics. Some assignments focused on algorithms, including standard topics like searching and sorting. Breadth-first search was covered in a rather amusing assignment where you had to write logic for ‘humans’ that would make them avoid slowly moving ‘zombies’. Further, minimax was covered in a disappointingly short assignment that was merely a variation of a previous one, an AI for Tic-Tac-Toe. The assignments themselves were the highlight of the course, though, covering the logic behind games such as 2048, Cookie Clicker, Yahtzee, the just mentioned Tic-Tac-Toe (Monte Carlo and tree search), and the 15-puzzle. Overall, they helped solidify the concepts.

In my opinion it is one of the best-designed MOOCs in computer science you can take.

I had great anticipation for this course after completing their Introduction to Interactive Programming, and was rather disappointed.

While I understand that there is a need to remove scaffolding so that participants become more independent, the instructors did it rather suddenly and not particularly clearly.

The machine grader is alright, but unhelpful when facing certain bugs. The feedback provides only very obscure hints to the issue at hand. In fact, the instructors never actually taught how to read and understand the feedback messages. It was a lot of time spent debugging, which we could have just experienced on our own without paying money for this course.

Overall, it felt like the intent of this course was really the math (nothing beyond high school level combinatorics and probability) and to "immerse" participants in the deep end of a software engineering process, where you need to write your own test suites and be used to less-structured instructions.

This Part 1 course is more of something that we need to get out of the way before we get to Part 2, which looks more fitting to the title "Principles of Computing".