very beginner misleading title, not a significant knowledge in Math for DS!

I took this course as a refresher for maths rules I had seen in university, so my experience does not relate to those who will encounter this topics for the first time. I honestly do not recommend this course to beginners with no background in mathematics, because there is not much space given to theoretical explanations of the principles here. The course feels more like a quick slideshow of rules (logaritmic manipulations, exponential manipulations, conditional probability manipulation) followed by exercises to practice them. This is a stark contrast to my University Experience, where mathematics is about principles, logic and the demonstrations that underly theorems and their validity (e.g. ''why is the formula for combinations of "m draws on a set of n" built this way?''): deep study of these topics provides students with the mental skills to build a MODEL of whatever they come to face in real life. You may forget the rules, but those can be freshened up by a course like this. To build Logical and thinking skills instead requires a deeper understanding of mathematics and its underlying principles. I hope the professor who recorded this course will look forward to an opportunity to devise a deeper mathematics course to tackle these topics. Good luck to all fellow learners and thanks to Coursera for this opportunity!

Too basic. Should give an example on how these math skills are used in data science.

The first 3 weeks were great. Most of the stuff I had already learned, but that was 30+ years ago. It was a good refresher course and I enjoyed myself. As I took a lot of notes I had to learn how to write mathematical formulas in markdown in Obsidian using MathJax, which was also fun. The fourth week was a bummer. The presentations weren't any good at all, the handwriting was bad, the explanations barely scratched the surface and I struggled with the quizzes.

Do you learn the necessary math skills for data science with this course? I have no idea.

Did I learn something? Yes, it was a good refresher course during the first 3 weeks, so much so that I want to study more math. As for the last week, I will have to look elsewhere for a book or course about statistics and probability.

Positive: The first 3 weeks were great and I really enjoyed them.

Negative: The last week was a waste of time, and I will have to find other sources. Compared to some other courses the videos and presentations lack professionalism. This course has been orphaned, as in the creators have abandoned this course. If you have a problem, the best you can hope for is the help of a fellow student.

Each of the first 3 weeks, despite some shortcomings: 4 stars

The fourth week: 1 star.

Average: 3 stars

The first two weeks of the course were great! The instructor was very clear in his explanations and made the material very intuitive. The video companion pdf's were also very well written. But from the third week onward, when the other instructor took over, not only did the explanations suffer significantly, the video companion material also ceased to be of much help. He did not explain any of the intuition behind any of the formulas and he didn't even try to explain the intuition behind when and where the formulas would apply. I didn't take this course just to be given a bunch of formulas. I really wanted to understand the material because I knew these are foundational concepts that needed to be mastered. Khan Academy explains a lot of the material of weeks 3 and 4 much better. I really wish someone had explained how the version of the binomial theorem that was presented in this course is related to the traditional version that we learned in school while doing binomial expansions in algebra.

WEEK 4 needs to be covered with more examples and more clarity.

Overall the course was great. Shored up my knowledge on a number of subjects. Could have used better explanations of certain topics though. There were a couple of instances where the instructors simply show you how to use a particular equation to deal with a particular kind of problem without explaining why it works. For concepts to really sink in you need to get a deep intuition for how they operate. I think these intuitions were provided for the main concepts, but there were some sub-concepts, where they essentially just gave you the method without getting into how it works. It's fine to know how to use it, but without a deep intuition it's just memorization.

Overall I really liked this course though. The quizzes and tests were challenging and fun and I came away with a much better knowledge of the subject matter, especially probability. I'm not a scientist, so I can't really speak to whether this will be all I need, but it was certainly helpful in the data science concepts I'be been exposed to so far.

A tremendously useful primer on the fundamentals of data science math. This course is a particularly good option for individuals who have seen some amount of calculus and algebra but haven't used those methods in a long while and need to review. Thorough, easy-to-understand material.

I would suggest to the course facilitators that they develop the provided lecture notes -- already a useful tool -- into a full-fledged text. I'm not suggesting something much longer than what they already have, but simply taking that document and adding a bit more rich content. While the notes are useful for more carefully studying the math being done in the lectures, even a bit of effort putting some pedagogy into the notes and combining them into a single document (which I did for the sake of printing) would create a hugely valuable resource.

This course is designed for those either without a college level math background (calculus, probability, etc) and thus need an introduction to fundamental math skills or for those who need a refresher. This is not a course that teaches data science, nor the math of data science (linear algebra, random processes, algorithms, etc). But rather it teaches the math behind the math of data science. It reviews the basics of sets, plotting, sigma notation, derivatives, logarithms, mean and variance, Bayes theorem, etc. It is a gentle introduction to basic math skills that everyone should have. This is a course to definitely take as a refresher or before venturing into more higher topics such as collegiate math, data analysis, machine learning, computer science, engineering, etc.

I have some mixed feelings about the course. I think the course is both too easy and too hard in different ways that pull against each other. The first two weeks are basically just an introduction to set theory and some really basic high school level algebra. It was too easy to the point of being almost absurd. At the third week, the logarithm unit was more difficult. While this is also high school math, a lot of people don't use it regularly and may find that the video lectures weren't super helpful in explaining (or refreshing) this material. The quiz questions seemed much harder than any of the examples discussed in the videos. The fourth week covered probability, which is something that I felt like I knew fairly well going into this. The quality of the lectures in the fourth week is lower. Too many different formulae are tossed at the audience without a lot of explanation and very few examples are given. Once again, the quiz questions were way more difficult than the examples used in the lecture.

There are tons of mistakes in the lectures, with a lot of popup boxes added in later on to correct them. But honestly, there are enough that these lectures should just be rerecorded. Write out decent scripts, work out the problems in advance, and remove the errors.

I would like to see more discussions about the math itself and how it is applied in data science.

I felt like this course was just okay, especially when compared with the Intro to Calculus course from the University of Sydney. The problem I had was that the quizzes often required applying more difficult scenarios than the examples in the video explained. I think it should be the other way around - videos should prepare you to solve difficult problems, but quizzes should not present scenarios that the student has not had experience with. It would've been helpful if they had provided notes to go along with the examples. I also felt like equal amounts of time were spent on very easy concepts and difficult, complicated concepts. I don't need an entire video on calculating the slope of a line, but I do probably need more background on all of these probability scenarios and how they play out in real life situations. I found on the Week 4 quiz I was just scratching my head and trying random formulas.

Last part of the course (Bayes) is poorly explained, and the quizzes barely test (the little) taught on video.

I was planning to rate it as 4 stars but the last week was terrible.

At the end of week 3 they introduced a new instructor. All things considered week 4 and the new instructor are terrible.

More specifically, the instructor lacks the ability to put across information and explain it in an understandable manner. His handwriting is quite bad as well and sometimes its hard to make out what is written on the board. Plenty of times he does calculations without explaining or even skips calculations and moves on. He takes many things for granted and doesn't care a lot about explaining topics and problems further or deeper.

There were times where he just changed slides to a slide full of pre-written information/calculations etc.. Why do that? Why not write them while on camera and go through your process of thinking so the viewer can understand better? Oh and there was also an instance where in the middle of a problem he started doing calculations with numbers that appeared out of nowhere and only after he finished did he explain how he got those numbers. But i - and probably many more- were already confused by then.

I suggest this course only if you want to remember old junior high - high school math topics. If you want to learn actual data science mathematics i personally believe that you should avoid this course. That's what i would do, now that i have completed the course.

It helped me to refresh my knowledge from high school (26 years ago) before studying again. Other than that, I found it not really engaging. It's taught the way it has always been taught in schools and universities, a teacher delivering knowledge. Also I expected that more connections would be shown between the content and Data Science world. Examples with dices and urns are good to get the concept but it should be more connected to Data Science.

Don't waste your time with this. The first 3 weeks are insulting, teaching you basic highschool math (like what is "<", what is a function, etc.). The last week is more interesting as it gets to probabilities, and the quizzes are fun.

Reading materials are given and completely remove the need to look at the videos (pure waste of time). Formulas are given without any rigour.

一些非常基礎的高中數學，而且不完整。

課程一開始還會講解得比較細部，後面愈跳愈多。

對於有數學基礎的人來說根本不用浪費時間，對於沒有數學基礎的人來說，看了也沒辦法真的學到多少東西。

I would recommend Duke University's Data Science Math Skills course to students who are interested in learning the math skills needed for data science, but who do not have a strong math background. The course is a good way to brush up on basic math skills and to learn some of the more advanced math concepts that are used in data science. However, students should be prepared to do some extra work if they want to learn all of the math skills that they need for data science.

If you have gaps in your math knowledge this course right here fills them up and prepares you for more challenging courses. Simple yet precise, this is not an advanced course but more of a pre-data science requirement. If you're someone that struggled with calculus in high school but is willing to give it a second shot, this is the course you're looking for.

I learned many new things, ideas, knowledge,and skills from this course.I am very much thankful to both professors for teaching about all of those interesting lessons,providing many more things. now, I am able to give all of the answers frequently which I learned from this beneficial course.

The course has limited resource to understand. This course content is not sufficient to understand the topics. But that is fine as we come to know what we need to learn .Then , We have to put additional effort to understand the topics in external sources like textbooks, Internet, youtube.

cannot believe I took a programming course without doing this - the math was taking me so long and it was because I hadn't finished high school math a decade ago (our school didn't require it) - really thankful to have found this course!

Every part covered gives a good introduction to the world of data science as intended. However in my personal opinion the part on probability was covered a bit hastily, though the quizzes will force you get some in-depth understanding

This is a very interesting course for those who have not used math for many years and now want to pursue the field of data science. The basic concepts are presented coherently and understandably attracted me throughout the course.

It is really good data science math course, all described topics are highly important to know for everyone who need to know about data science. if anyone want to know about Data Science I will recommend to join this course.

As a non-native speaker, the first three parts are helpful in getting into math terminologies and reviewing basic math knowledges. The essence is all about the last part, which might be a little tough for new learners I guess.