Learner Reviews & Feedback for Understanding and Visualizing Data with Python by University of Michigan
About the Course
Top reviews
VV
Aug 2, 2020
Great course to learn the basics! The supplementary material in Jupyter notebooks is extremely valuable. Really appreciate the PhD students who took the time to explain even the simplest of codes :)
MR
Oct 31, 2020
Well organized material. The Discussion forum was the best one I've experienced in my Coursera education. All my questions were answered within one day. The best statistics class I've taken yet!
401 - 425 of 561 Reviews for Understanding and Visualizing Data with Python
By Satrio T S
•Oct 2, 2019
Excellent
By Sareen S
•Nov 23, 2020
AMAZING!
By Nedal
•May 25, 2020
v
e
r
y
g
o
o
d
By Gabriel A A C
•Feb 5, 2020
Excelent
By Elsayed A
•Mar 28, 2023
love it
By Israel F
•Jun 25, 2020
Amazing
By 周晓
•Apr 7, 2020
Thanks!
By Justin H
•Sep 24, 2023
brutal
By Euna S
•Jul 20, 2021
체계적인 학
By KAYDAN P R
•Jun 30, 2020
awssem
By Wei w
•Sep 25, 2022
good!
By J. B
•Mar 15, 2022
Nice.
By Frank S Y R
•Jan 17, 2019
Nice!
By 廖堃宇
•Mar 9, 2025
good
By Tuncay Q
•Sep 21, 2023
good
By Hugo S A
•May 24, 2021
fun!
By Durga S
•Apr 15, 2021
Good
By Chang L
•Aug 31, 2020
good
By GUNDA S K G
•Mar 3, 2020
good
By ATHIPATLA S N
•Feb 24, 2020
nice
By BODIREDDI S
•Feb 23, 2020
nice
By PUPPALA B A
•Feb 20, 2020
GOOD
By PEDASINGU T K
•Feb 23, 2020
gud
By Debasis D
•May 12, 2021
.
By Ronobir D
•Jul 16, 2024
Good course but definitely wish the practice material was a little stronger or more challenging. I quite like the lectures and the professors and teaching staff definitely know their stuff being UMich's Stats department of course the content itself is great. The lectures are great, the solution sets they give are great but how exactly they did those solutions... well let's just say I personally wouldn't just rely on week 1s coverage of the basics to get to there. I would strongly recommend people have at least a passing understanding of Python like through the Python 3 Specialization from UMich or Py4E from UMich. AND I would say this shouldn't be the first time you use numpy, Pandas or seaborn. I would suggest going through the Numpy Tutorial on the numpy site, the Pandas tutorial on the Pandas site and follow up with Kaggle's micro courses on Pandas, seaborn and data cleaning. This course, true to its name of the stats specialization is really an application of basic descriptive statistics like for Exploratory Data Analysis done with python. Which is what I was looking for so this is exactly what I wanted. Again lectures solid and the solution to the exercise notebooks are GREAT. They don't explain in great detail besides linking documentation how they got there so knowing Pandas indexing, shallow/deep copy, the pandas stats functions, Pandas pivots like melt and stack etc. This really takes someone who knows the basics of Pandas, teaches them the very basics of stats like stuff from high school early college, and applies it to a real dataset as you would in an everyday EDA setting. And it is EXACTLY what I wanted to teach that. Just wish there was more practice on this stuff. Youtube tutorials don't go as indepth imo.