16. Mai 2020
Effective way to refresh and add the Data Science math skills! Thanks a lot! At the time of the study some of the quizzes content were not rendering correctly on mobile devices (both iPad and Android)
5. Mai 2020
This was mostly review for me though probability especially Beyes Theorem derivation was new. The instructors provided clear often refreshing ways to look at material.\n\nThank you for a great class!!
von Vidya S•
23. Sep. 2020
This course syllabus is great. It starts wonderfully. Week 1 to 4 is taught by Paul Bendich, and Daniel Egger the instruction is awesome. Effective way to refresh and add the Data Science math skills!
15. Apr. 2020
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!
von Numsap S•
21. März 2017
Too basic. Should give an example on how these math skills are used in data science.
von Susmito R•
13. Juli 2019
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.
von Kartik S•
25. Mai 2020
WEEK 4 needs to be covered with more examples and more clarity.
von Stéphane F•
19. Juni 2020
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.
von ChunChieh L•
20. Sep. 2019
von Shawn T R•
8. Aug. 2018
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.
von Benjamin L•
26. Apr. 2017
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.
von Kostas H•
9. März 2018
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.
31. Jan. 2019
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.
von manivel s•
23. Apr. 2020
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.
von Miriam C•
23. Juli 2017
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!
von Abhishek R•
7. Nov. 2019
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
von Ned T•
11. Sep. 2017
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.
von Sehresh M•
12. Apr. 2020
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.
von Lingde K•
7. März 2019
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.
von Jessica J•
16. Dez. 2017
A great refresher course and a range of interesting and foundational concepts. Would recommend to anyone who has prior experience with calculus and probability theory and is just looking to remind themselves of key concepts.
von Jhon R•
2. Mai 2020
Great option to get back to the Math worked, reviewing the basics of what needs to be known when working on data science and see where you need to put more effort. Hoping this helps while I continue taking other DS courses.
von Daniel G T P•
13. Feb. 2020
It helped me reviewing and learning interesting mathematical points that will help me understand more about my Machine Learning course.
I believe the last week, about probability. could be more extensive and made more clear.
von Angelica D•
17. Feb. 2020
This was a great beginner course on some of the math you might see in Data Science. I'd recommend this course to anyone that might not be confident in math who want to start a career in this field. A great refresher!
von Jayson S•
4. März 2018
Fantastic course, especially when paired with or done before Andrew Ng's Machine Learning course as it matches up quite well! Thank you for the detailed guidance in the practice quizzes on incorrect answers as well!
von Zhenqing H•
17. Dez. 2017
This course gives me the basic conceptions about the mathematics, especially parts about calculus and possibilities, however, if would be great if there are samples or basic practices related with the data science.
von Subramanian N•
20. Aug. 2017
This was an excellent review of the basic mathematical concepts useful in data science and machine learning. Thank you very much for the very concise and clear explanations of the various topics! Much appreciated!
von Frank G•
29. Mai 2017
very nice course, and a good starting point to catch up data science and computer science math-skills. Helps to bring some of those rusty concepts back into memory, and from there you can expand further ...