# Profile

## Cezar Serban

Student

**3**reviews**3**completed

This was a fantastic calculus course. PROs: Beautifully designed videos with
excellent non-trivial examples, a huge array of homework problems to test
understanding, and amazing levels of interest and participation in the
discussion forums by both students, TAs, and the professor himself. CONS:
Almost nothing I can think of; except possibly adding more written content to
the Course Wiki in order to help solidify student's understanding of the
concepts, but this was such a minor issue that I expect they'lI have it done
in the next iteration (they had already started adding content to the wiki in
the 1st iteration). Also some people may not have the necessary prerequisites
(i.e. some previous calculus experience) to handle the speed of this course,
but I don't consider this a con. Another potential con, which again depends on
a student's prerequisites, is that the first 3 week unit is all about taylor
series, which is a quite advanced starting point, meaning that most people
will not feel like they're lightly easing into the course as it so often
happens in other courses. However the good news is that the next two units
which span about a month, are a lot easier and tend to move slower so you do
get a bit of a break for a couple of weeks. For my background, I had studied
calculus in both high school as well as in my engineering classes in college,
and after 10 years I wanted to get a refresher. I have to say the level of
quality and effort put in by the instructor and TAs in this course far
surpasses what I was exposed to at a prestigious ivy university. This is
likely one of the best video presentations in all of 10-15 MOOCs I've taken in
the last year. One word of warning: Most of the 15 minute videos move at hyper
speed, so you will likely find yourself pausing and replaying certain pieces
of them to cement your grasp of all the calculations. Still this is not a
problem; it just shows how efficient the instructor is in his ability to cover
approximately 1 hour of typical class lecture material in the span of 15-20
minutes with an amazing degree of clarity. Also for this reason, it is highly
recommended that someone with no calculus background take a different calculus
course and come back to this one when they're ready for the speed of this
material. For example, the first 3 week unit is on series, and it's one of the
most difficult units simply because this material is typically not done until
the mid to end of a one year introductory calculus course.

This 5-week follow up to Stat2.1x was another wonderful course in the Stat2
sequence. The content and delivery was all very high quality; analogous to
Stat2.1x (see my review for that). This one was more focused on the nature of
randomness than the first one, and I can say that I enjoyed the content even
more than the first. The primary focus in this course was on the binomial and
hypergeometric distributions, in the context of coin tosses, dice, gambling
expectation, and samples from 0-1 boxes. So of course, the problems were all
fun and required thinking and not just plug and chug. I really feel that I
finally got some good probabilistic intuition out of it, and can't wait for
2.3x. I think having seen some probability in various classes in college, I
found the course easy overall, but I would say that for some people this could
be considered in the medium or even hard range since the concepts are a little
tricky to grasp if you're seeing randomness and probability concepts for the
first time.

This class was an amazing introduction to statistics. I was blown away by Prof
Adhikari's fantastic lectures. Teaching elementary stats is not supposed to be
very exciting for first time students, but she was nothing short of brilliant
in her delivery, presenting the subject with clarity, lots of intuition, and
even a good degree of depth given the lack of almost any prerequisites needed
for the course. Her lectures are very engaging in that she has a remarkable
ability to play with the concept, presenting it in different ways so you can
grasp it. I have an engineering degree but I've never had the need to take a
stats class (though I've had my fair share of probability), so many of the
concepts were not entirely clear to me before I enrolled, although I had
briefly heard of many of them before. I was initially skeptical and debating
whether to take this class because it seemed too easy based on the
description, ('descriptive statistics'... what does that mean anyway, was my
initial thought). I quickly realized that the concepts of the course were
tricky to describe without a full math background in calculus, but she did a
great job without that prerequisite, even adding optional slides for those who
were interested. The lectures were self contained, but the online textbook (by
Philip Stark) was a good add-on if you wanted more practice with the lecture
concepts. Although the textbook seemed a bit wordy, it has some interactive
Java demos that let you explore with histograms, normal curves, scatter plots
and such. It also has full videos of Stark teaching the lectures live at
Berklee, so it's truly a great bonus to get another take by a different
professor on any parts that may have been confusing in required lectures
(although this was not common in my case; I again have to stress that the
required lectures were simply fantastic). Finally a bit about problem sets:
Despite the lectures being very understandable, the Prof has a knack for
making some of the exercises tricky, so in this sense the problem sets are not
trivial. the midterm and final were also pretty tricky, so you really needed
to understand the lecture material well to do well on them. I would say the
difficulty level of this course was easy to medium for me with most of the
concepts being easy, except for the regression portion in the last 2 weeks
being quite confusing for me (this was something I had never seen before,
although I was a bit familiar with the related least squares fitting concept
from back in school). I definitely eased into the course, but by the last two
weeks, I had to pour a lot of effort into it in order to end up doing well on
the final. This course was the first 5 weeks of a full semester Intro to Stats
class given at Berkeley, and I'm currently taking the second part of this
course (Stat 2.2x) which so far has focused mostly on probability and
randomness. It is I would say even better and more enjoyable than the first,
but I digress. In conclusion this is an absolute hit course in the MOOC land
given the subject's ubiquity and vast importance in science and engineering
and today's data driven world. Its accessibility and lack of any real
mathematical prerequisites make it ideal for literally anyone to take it, and
given that they can stay motivated enough to get through the exercises and
exams and complete it, they will have a great understanding of statistical
concepts. One final thing I just thought of: Last year I had started taking
the first version of Udacity's Intro to Statistics course taught by Sebastian
Thrun, and although it initially started out entertaining (like most Udacity
courses), I found it kind of confusing and jumpy on topics after about 2 weeks
worth of material and I ended up losing interest and not continuing with it.
Based on my experience I would definitely recommend this edX version to get
the core understanding, or at least waiting until the Udacity stats course is
revamped, as that is supposedly in the works.