Chevron Left
Retour à Probability Theory: Foundation for Data Science

Avis et commentaires pour d'étudiants pour Probability Theory: Foundation for Data Science par Université du Colorado à Boulder

4.3
étoiles
54 évaluations
18 avis

À propos du cours

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 https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash....
Filtrer par :

1 - 19 sur 19 Avis pour Probability Theory: Foundation for Data Science

par Cora M

20 nov. 2021

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.

par Mattia G

18 déc. 2021

peer review assignments are useless

par Essam S

11 oct. 2021

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

par Ke M

15 nov. 2021

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.

par Tim S

5 sept. 2021

T​his 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.

par Jun I

13 oct. 2021

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

par Ping Q

22 janv. 2022

Very logical arrangement, proper speech rate, crystal clear!

par P A

17 janv. 2022

G​reat intro and very well presented by the prof

par Michelle W

30 avr. 2022

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).

par Nathan H

23 mars 2022

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

I​t took a month, or something like that, for Coursera to let do the peer grading that's required by the course.

par Paul R P

18 avr. 2022

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

par Mauricio F

20 juil. 2021

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

par 상은 김

5 oct. 2021

H​elpful to understand data sciences basic thories

par Daniel C

3 févr. 2022

Exactly the probability course I was looking for

par Hidetake T

30 mars 2022

Good course with sufficient amount of practice.

par Claudia G D

3 mars 2022

T​he course is very good.

par Kyle A

21 févr. 2022

Great Course!

par Matthew E

8 mai 2022

Lots of fun

par Kevin H

14 mai 2022

N​ot enough participants for peer review, not quite enough time spent on curriculum