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Вернуться к Probability Theory: Foundation for Data Science

Отзывы учащихся о курсе Probability Theory: Foundation for Data Science от партнера Колорадский университет в Боулдере

Оценки: 97

О курсе

Understand the foundations of probability and its relationship to statistics and data science.  We’ll learn what it means to calculate a probability, independent and dependent outcomes, and conditional events.  We’ll study discrete and continuous random variables and see how this fits with data collection.  We’ll end the course with Gaussian (normal) random variables and the Central Limit Theorem and understand its fundamental importance for all of statistics and data science. This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at Logo adapted from photo by Christopher Burns on Unsplash....

Лучшие рецензии


15 июня 2022 г.

This is a great course on probability. Although I felt like it was too easy and should include more PDFs (such as Beta and Gamma) and random variable transformations.


9 дек. 2022 г.

This is an excellent course to review foundational probability concepts. The instructor speaks clearly and goes through examples thoroughly for each concept.

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1–25 из 28 отзывов о курсе Probability Theory: Foundation for Data Science

автор: Cora M

20 нояб. 2021 г.

My rating applies to the first week, as I'm dropping after my experience with the first assignment. This is not a commentary on Prof. Dougherty, who seems like a teacher I'd really like to have in an in-person setting. It refers instead to the Gilliamesque homework submission and grading system. Before you join the class, be prepared:

All homework is submitted in an ipynb using an R kernel, and homework is autograded. The grader gives zero feedback regarding what was incorrect, not to mention why or what the correct answer is. All you get is the number of cells that didn't pass; when you reload the assignment, there is no indication of what was wrong.

As a math nerd troll, however, it's magnificent—the grading mechanism itself is a probability problem that provides one with hours of fun. By which I mean frustration.

I joined this class as a refresher, because I love probability. I'm dropping this course before that changes.

автор: Mattia G

18 дек. 2021 г.

peer review assignments are useless

автор: Ke M

15 нояб. 2021 г.

Sorry, but I can't learn R by myself. I know how to do all the calculations, just don't know how to put it in the R language.

автор: Essam S

11 окт. 2021 г.

The instructor is very good, more examples need to be added, there are mistakes in the evaluation

автор: Michelle W

30 апр. 2022 г.

The professor's instruction is clear and concise, but I wish there were more videos to expand on topics not discussed. The auto-graded assignments are painful since there is no feedback on which problem was wrong (hint: only do one problem at a time and submit to grader. it is painfully slow but this way you know how you did on each question). This course assumes you have basic familiarity with R and can do basic differentiation & integration. I would not recommend this as a first course in probability - this course is best for those who have had some exposure to probability already (E.g., undergraduate level course).

автор: Tim S

5 сент. 2021 г.

This was a very good course. The material was well thought/planned out such that the readings, lectures, and homeworks built off each other in a constructive manner, which reinforced the material. I highly recommend taking this course as an introduction to probability.

автор: Derek B

18 июня 2022 г.

Overall I thought this course was very good. The lectures were clear. I was even more impressed by the work that was put into designing different kinds of assignments. After completing them, I felt like I understood concepts and techniques much better than before.

That said, I have two big criticisms. First, I really did not like the textbook that was provided. It is supposed to be different from a traditional text book, in a way that makes it easier to understand, I guess. But honestly I thought it had the opposite effect. The non-traditional style made it harder to look up information I wanted to review. I ended up searching for other online sources for better explanations of what was going on.

Second, while I think the class is great on its own, it is part of the Statistical Inference Specialization, and it feels like there was a lack of coordination between the people designing this course and those designing the second course in the series. The second course seems to presuppose much more advanced understanding of probability distributions than this course provides. So while I think the course is great on its own, if you are expecting it to prepare you for the second course in the series, it honestly fails to do so.

автор: Paul R P

18 апр. 2022 г.

Need to brush up integral calculus for thios course. Something I haven't looked at for 40 years.

автор: Jun I

13 окт. 2021 г.

Great course which covers from fundamental probability theory with good examples for better understandings.

автор: Ping Q

22 янв. 2022 г.

Very logical arrangement, proper speech rate, crystal clear!

автор: P A

17 янв. 2022 г.

Great intro and very well presented by the prof

автор: Nathan H

23 мар. 2022 г.

It's pretty basic material, but that's not a bad thing. I had no trouble with the content.

It took a month, or something like that, for Coursera to let do the peer grading that's required by the course.

автор: Kevin H

14 мая 2022 г.

Not enough participants for peer review, not quite enough time spent on curriculum

автор: Alex H

26 авг. 2022 г.

I felt this course was challenging, in a good way. I really appreciate the number and depth of the exercises for each module. The only downside is the auto-grading of the homework doesn't tell you which question you got wrong, so that can be frustrating. But overall I feel very lucky to have access to this course for Coursera price, and I plan to finish this specialization, because I really feel it's beneficial for working toward mastery of probability and statistics.

автор: Michael B

16 июня 2022 г.

This is a great course on probability. Although I felt like it was too easy and should include more PDFs (such as Beta and Gamma) and random variable transformations.

автор: Joseph B

10 дек. 2022 г.

This is an excellent course to review foundational probability concepts. The instructor speaks clearly and goes through examples thoroughly for each concept.

автор: Mauricio G F

20 июля 2021 г.

It was a great course. Good combination between theory and practice.

автор: 상은 김

5 окт. 2021 г.

Helpful to understand data sciences basic thories

автор: Daniel C

3 февр. 2022 г.

Exactly the probability course I was looking for

автор: Hidetake T

30 мар. 2022 г.

Good course with sufficient amount of practice.

автор: Claudia G D

3 мар. 2022 г.

The course is very good.

автор: ILYES B

12 сент. 2022 г.

great course thank youu

автор: BING X

14 янв. 2023 г.

Very nice course!

автор: Kyle A

21 февр. 2022 г.

Great Course!

автор: Matthew E

8 мая 2022 г.

Lots of fun