Exam Review – MIT’s 6.00.2x (Comp. thinking and data science)
Merry christman and happy new year to everyone reading this post!!! (i was a bit late, i know, sorry about that). And today is Saturday. A really meh day. The most mehest day of the week (inb4 sunday). I don’t really want to study (dunno really why, truly) molecular biology, so here i am to review another mooc!
Today i’m reviewing the MITX 6.00.2x, also known as Introduction to Computational Thinking and Data Science . The course (as the previous one) has been made by MIT (love you all again guys, keep rocking). As the previous, the course was (imo) HARD, but let’s go to the points.
Course Format
Let’s start saying that the course was divided in 2 parts: one focused on statistic (with monte carlo simulations, plotting, stocastical thinking, ecc…), and the other part was an introduction in data structure (course that i will take in march), with hash tables, trees, graphs and other structures. The course (as the previous) was divided in week (8), and every week has a specific asignment (aka a problemSet). The course started a week after the 6.001, and ended the 15 of december (yeah, i’m reviewing it late, i know, but i have a tons of things to study *cries in italian*). (for more infos of the course format, see the previous review . I liked more the second part, not because the first was bad done, but i don’t really like the subject studied, and the course focused more on the theoretical statistic, and not on the programming side of that one, which imo was a really good point to focus on (i’ll review that part later). the second part, instead, was a lot more “code focused”, and i find that awesome! Truly! A lot of good exercises and marvellouses lectures!
The lectures
In 6.00.2x, the lectures were done by Jonh Guttag and by Eric Grimson (si dai, il nonno di heidi).
I liked the lectures from both (i’ve already talked about Grimson, in the previous review). John Guttag was a really nice lecturer, very easy and funny during most of the lectures, but really professional and serious at the same time. The thing that i liked less was “the code”. Usually, during the lectures, code is used to solve problems, and, after that, you must re-elaborate that code to solve a similar problem. For most of the time (from week 1 to 4), that was not “true”. The lectures were structured on “watch that code, it does that”, and nothing more, which i found not very usefull for the learner.
The second part was more on the kind of 6.001x, and i liked that part more.
I post also the complete syllabus , so anyone can see the topics covered
The Problem Sets
The problem sets were, as usually, great. Sometimes difficult and frustrating, but i really like them. A lot (in particular the optimisation finding of week 7). There are no more things to say.
Conclusions
MIT always do great MOOCs, but i preferred the 6.001x compare to that one. Not liked a lot the first par, but loved the second one. From 0 to 10 (no more python, sorry), 6 to the first part and 8.5 to the second one, the mean is something more that 7. However, good job to the MITx guys! See you to the next review 🙂
Today i’m reviewing the MITX 6.00.2x, also known as Introduction to Computational Thinking and Data Science . The course (as the previous one) has been made by MIT (love you all again guys, keep rocking). As the previous, the course was (imo) HARD, but let’s go to the points.
Course Format
Let’s start saying that the course was divided in 2 parts: one focused on statistic (with monte carlo simulations, plotting, stocastical thinking, ecc…), and the other part was an introduction in data structure (course that i will take in march), with hash tables, trees, graphs and other structures. The course (as the previous) was divided in week (8), and every week has a specific asignment (aka a problemSet). The course started a week after the 6.001, and ended the 15 of december (yeah, i’m reviewing it late, i know, but i have a tons of things to study *cries in italian*). (for more infos of the course format, see the previous review . I liked more the second part, not because the first was bad done, but i don’t really like the subject studied, and the course focused more on the theoretical statistic, and not on the programming side of that one, which imo was a really good point to focus on (i’ll review that part later). the second part, instead, was a lot more “code focused”, and i find that awesome! Truly! A lot of good exercises and marvellouses lectures!
The lectures
In 6.00.2x, the lectures were done by Jonh Guttag and by Eric Grimson (si dai, il nonno di heidi).
I liked the lectures from both (i’ve already talked about Grimson, in the previous review). John Guttag was a really nice lecturer, very easy and funny during most of the lectures, but really professional and serious at the same time. The thing that i liked less was “the code”. Usually, during the lectures, code is used to solve problems, and, after that, you must re-elaborate that code to solve a similar problem. For most of the time (from week 1 to 4), that was not “true”. The lectures were structured on “watch that code, it does that”, and nothing more, which i found not very usefull for the learner.
The second part was more on the kind of 6.001x, and i liked that part more.
I post also the complete syllabus , so anyone can see the topics covered
The Problem Sets
The problem sets were, as usually, great. Sometimes difficult and frustrating, but i really like them. A lot (in particular the optimisation finding of week 7). There are no more things to say.
Conclusions
MIT always do great MOOCs, but i preferred the 6.001x compare to that one. Not liked a lot the first par, but loved the second one. From 0 to 10 (no more python, sorry), 6 to the first part and 8.5 to the second one, the mean is something more that 7. However, good job to the MITx guys! See you to the next review 🙂
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