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Learner Reviews & Feedback for Statistical Inference and Hypothesis Testing in Data Science Applications by University of Colorado Boulder

4.8
stars
25 ratings

About the Course

This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse. 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....

Top reviews

RK

Oct 26, 2022

In-depth course on Hypothesis testing. Course instructor is quite engaging.

GV

Jul 27, 2022

Loved the material. Content looks quite convincing and well explained!

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1 - 6 of 6 Reviews for Statistical Inference and Hypothesis Testing in Data Science Applications

By Nathan H

Jan 10, 2022

It's clear that a good bit of thought and effort went into putting the course together, but it seems unfinished.

The autograder system on the programming assignments in the three University of Colorado Boulder statistics courses that I've enrolled in is like something from a Kafka novel. It does not provide feedback on which questions it's marking incorrect, and Jupyter notebooks are unreliable in their rention of updates. That can compound with errors in the assignments themselves and a nearly deserted discussion forum for a really rough time.

There's not enough student course work to make me confident in my mastery of the material or in the retention of it.

It would be nice to have some reference material other than the lecture slides.

It's not particular to this course, but there are a lot of irritations with the Coursera UI. (For example, I would like to access the course while writing this review to confirm that my comments are accurate, but that's not easy to do.)

By Alex M

Jan 6, 2023

Probably the best course I've taken on coursera so far. It might be even an actual university level course. Be careful this course is using a lot of math. Overall the course is very entertaining and fun and the teacher is just amazing I think I would fall in love with her. A lot of things are actually mathematically derived and explained which is cool (and since you have gone this far you might as well prove everything in some "honorable students" section or smth, but you can always find some mathematically heavy proofs in books on the internet).

By Rohit K

Oct 27, 2022

In-depth course on Hypothesis testing. Course instructor is quite engaging.

By Garima V

Jul 27, 2022

Loved the material. Content looks quite convincing and well explained!

By ILYES B

Sep 7, 2022

GREAT COURSE WITH GREAT THEACHERS

By Ricardo R

Jul 11, 2022

excelentes aplicabilidades