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Opiniones y comentarios de aprendices correspondientes a Probability Theory: Foundation for Data Science por parte de Universidad de Colorado en Boulder

4.3
estrellas
27 calificaciones
6 reseña

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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....
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1 - 7 de 7 revisiones para Probability Theory: Foundation for Data Science

por Essam S

11 de oct. de 2021

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

por Tim S

5 de sep. de 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.

por Cora M

20 de nov. de 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.

por Ke M

15 de nov. de 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.

por Jun I

13 de oct. de 2021

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

por Mauricio F

20 de jul. de 2021

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

por 상은 김

5 de oct. de 2021

H​elpful to understand data sciences basic thories