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.

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.

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.

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!

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!

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.

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!

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 ...

Hi it is very helpful to me. Concept is properly explained. I enjoyed learning process. Expect some more courses on data science as well as on python which involves real time application.

Thanks a lot.

First 3 weeks were easy going and the last week was a bit more challenging. I think more examples could be included in the lectures to understand Bayes' Theorem at the most fundamental level.

It was a very good opportunity to go through the course, and the content was good. I can say I definitely learnt a lot in this course. Thanks team and kudos to great work you guys are doing.

Looking forward to advanced courses on Linear algebra, eculidean geometry that would make the concepts of vectors, matrices, plane and any application of those in the data science problems.

It covers all basics of mathematics and of-course intermediate concepts from Mathematics which are essential for data science in general, and very useful for data mining, data storage etc.

Good course! For the last module, probability, I will say that the videos and supporting outlines set the conceptual framework but the lack of practice questions resulted in me failing the quizzes on the first try. And I have studied probability before. That being said, I did not get discouraged. I simply devoted my time to studying the helpful "feedback" explanations for all the quiz answers (and reviewed my notes from a prior probability theory course). Then I passed the quizzes and final exam and got my certificate. I would think that for learners unfamiliar with probability theory it might be challenging to learn this in one week or so, especially if factorial counting is also new terrain (coins, dice, poker--how to compute 'exactly' and 'at least....'). Don't give up! Study closely the feedback explanations in the quizzes, that's where the real learning happens.

Week 1 and 2 are amazing. Very well explained. Week 3 and week 4 use different boards and are a bit messy. There is a disconnection between videos, readings and quizzes. Other than that, excellent way to remember or to refresh some topics. Not sufficient though. Still need to expand on Bayens theorem in particular, as well as logarithms. Good starting point. Not the best professor to explain things in week 2 and 4

This course serves as a great refresher for the mathematics used in analysis. As with any math course, practice is essential. It would benefit anyone to find a resource with supplementary problems they could practice, in order to reinforce the concepts learned in this course. I am happy that I took this course.

Weeks 1 - 3 were well presented and aligned iwth tests and exercises. Week 4 lectures were not well aligned with the test

the first 2 practices and quiz was not that challenging, but the starting from week 3 it's getting hard.

Some issues or miss-written expresions throughout the course

The lectures notes did not contained too much of supportive material followed by the video lectures. I would suggest that video notes, would contain not just the same content that has been shown in the video lectures, but in addition a further reading material that would allow student to strengthen his understanding on the topic, as well as include exercises with answers (+ solutions) so a student could be firm in his knowledge before proceeding to the next section. At least some links to fill in the knowledge gaps or relevant subject.

The video lectures, although have been very informative and useful, I found a significant difference in how subject have been taught and discussed by both teachers. I would highly recommend to address such unbalances in teaching, as it discourages from continuing and finishing the course (initial lectures were greatly useful and the quality of the lessons deteriorated as the course progressed).

Overall, I have found this course useful, however I doubt I have gained much from the week 4 (probability subject) as the material was not really intuitive and hard to follow with great jumps in knowledge which one may not be aware, unless had previous experience on the topic.

I would recommend this course to people who have already an intermediate (or above) understanding of the subjects taught and/or would like to recap areas which has been forgotten over time. I would not recommend this course if you are a beginner or have large knowledge gaps on Maths as it will make the lectures hard to follow and probably difficult to identify the gaps in one's own knowledge.

The first part of this course was great. It was the right level of material, taught simply and effectively with quizzes and exams that were on par with the taught material. The second half was not so great. The teaching style of the second teacher did not convey the material as effectively as the first teacher. Also, I felt that the week 4 probability quiz and final exam had material way beyond what was taught during the lesson. There should have been some exercises to warm us up and get us to the difficulty level of the final. It felt like going from 0-100 mph. Overall because of the stark difference in teaching and difficulty of the final exam of part 4, I can only give this course three stars for the great start.

I found the first two.5 modules very well done. However, the second half of week three and week four were very poorly done. I am still uncertain about the applications of Bayes' theorem and the combination/permutation concepts. I had a very difficult time with the last quiz and had to go elsewhere to actually learn what I needed to, and even so, I still do not understand the approach to solving some of the problems. Quite frustrating and not rewarding.

For someone with a Computer Science background at the undergraduate level, I find the contents basic. However, the intention of the course was to give a refresher for data science professionals who find the mathematical jargon frequently used in practice hard to comprehend. In this sense, the first half of the course taught by Prof. Paul Bendich were good. The second part of the course taught by Prof. Daniel Egger needs a lot of improvement in content delivery and better explanation. The quizzes on probability are challenging and enjoyable. Also, when I took the course as on March 2019, there wasn't any activity on the discussion forum. It seems there are not many students taking the course with me, and it also wasn't monitored by the course staff.

The first two weeks are good. The material is explained in a fairly intuitive way. One can easily understand the theory. It is also explained why and how a presented concept is related to data science.

The last two weeks however are to shallow and abstract in the explanations. I had to check external websites to fully understand the material. The lectures also didn't prepare me good enough for the tests. Sometimes I felt lost and the video companions also didn't really help. This wasn't the case in the first two weeks. At the end I was able to complete all tests with 100% but only because I taught the material myself with the help of external websites.

The lectures are confusing. Period. I have covered all this material and much further getting straight A's in everything from Algebra 1 and Geometry through Calculus and Statistics - and I find these lectures barely understandable. Main points and formulas are not adequately highlighted and examined and things are said in passing.... in addition to the errors being made. You would think the lectures, notes and quizzes would be thoroughly reviewed and corrected for mistakes before posting....