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Отзывы учащихся о курсе Introduction to Data Science in Python от партнера Мичиганский университет

4.5
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Оценки: 24,137
Рецензии: 5,409

О курсе

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python....

Лучшие рецензии

PK
9 мая 2020 г.

The course had helped in understanding the concepts of NumPy and pandas. The assignments were so helpful to apply these concepts which provide an in-depth understanding of the Numpy as well as pandans

SI
15 мар. 2018 г.

overall the good introductory course of python for data science but i feel it should have covered the basics in more details .specially for the ones who do not have any prior programming background .

Фильтр по:

5001–5025 из 5,329 отзывов о курсе Introduction to Data Science in Python

автор: ABHIJEET K R

10 сент. 2020 г.

not so good!!!!1

автор: uday s

14 апр. 2020 г.

to boring course

автор: Dheeraj s

20 июня 2020 г.

asfsdgdfgdxgghj

автор: Damini R

19 мая 2020 г.

GOOD EXPERIENCE

автор: Daya_Jin

20 июля 2018 г.

作业描述不清楚,有歧义!!!

автор: Shubhi v

25 июля 2020 г.

less hands on

автор: Sayyaparaju N V V V

9 мая 2018 г.

Nice one :)

автор: GIRIRAJ B

28 янв. 2019 г.

Good course

автор: abhishek

10 июня 2020 г.

very brief

автор: MariaStephan J

11 мая 2020 г.

very fast

автор: Arya P

2 июля 2020 г.

Too fast

автор: Weerachai Y

29 июня 2020 г.

thanks

автор: CHILUKOTI N A

28 сент. 2020 г.

good

автор: Govardhani S

6 авг. 2020 г.

good

автор: Aayesha N

30 июля 2020 г.

Nice

автор: Aansh S

10 июля 2020 г.

good

автор: Bicky G

13 июня 2020 г.

nice

автор: GOWTHAM M

22 мая 2020 г.

good

автор: xiao h

22 окт. 2019 г.

太难了8

автор: DELA C J K (

12 окт. 2019 г.

HARD

автор: Mohammad J

5 авг. 2017 г.

good

автор: Pranav P

17 июня 2021 г.

ok

автор: Yash V B

20 мая 2020 г.

ok

автор: Irfan S

4 окт. 2017 г.

A

автор: Richard H

29 июля 2019 г.

Truly horrible delivery of the material - even worse than Coursera's old Intro to Machine Learning course from Univ of Washington. This course will discourage nearly anyone from pursuing Data Science.

And it's not even an intro to data science. It's a course on Pandas for dataset manipulation. (In fairness, cleaning up ingest data is like 95% of the work in data science, but the course doesn't even tease the student with some exciting machine learning examples of where this is all headed.)

It's not delivered like you'd expect an intro course. It does an awful job of progressing the student through the Pandas toolset, building concepts incrementally. The whole topic of object types, methods, returned objects, and chaining gets barely a mention, but it's essential to the assignments. Examples are rapid-fire and sparse - very few techniques needed in the assignments can be found in the examples. The Week 2 quiz tests on techniques not introduced until Week 3, and the Week 3 and 4 assignments cite "individual study" which is academic-speak for "We didn't teach you about this - go Google it".

Then, there are errata that the student needs to pick out of the discussion forums to pass the assignments because some key questions are vague. The errata are 1-2 years old and they can't be bothered to correct errors.

The auto-grader could be the highlight of the course, but it provides limited feedback on wrong answers and no guidance toward the right answer; just "wrong". You're not allowed to post code or discuss answers in the forum - you have to go to StackOverflow to do that. (It'd be awesome if several of the exercises provided the student with the answer and challenged them to match it, but instead it's very sink-or-swim.)

Even when your answer is right, the auto-grader throws errors and warnings for, say, returning a numpy.float64 (which you should) when the grader is expecting a Python float type. Or it's expecting a float64 for a counter value (!!) when you provide an int64 (which is correct). These behaviors should have been fixed long ago.

It claimed to be a 15-hour course; I did it intensively and invested more than 30 hours before pulling the plug on the final project. That was claimed to be a 4-hour project, but experience with the rest of the course says it'd be more like another 12 hours - and that's for a guy who's not new to coding.

Bottom-line: I paid for educational material and I don't feel like this course delivers. What it does deliver is Pandas exercises and an "OK" auto-grader; truthfully, most of what I learned was via Google searches while trying to do the assignments - effective, but very slow and very frustrating. The real disappointment is seeing that the issues I encountered have been well-known for 2 years in the discussion forums; the course could be a lot better by now if they cared to nurture it.

Finally, a frustrating aside that's on Coursera, not the instructors... Coursera's online Jupyter notebook platform is really unstable and constantly drops connections even when you're actively editing and executing cells. (Including from 2 Fortune 100 companies - it's not the connection.) Once dropped, the notebook can't be re-connected, and has to be re-launched from the syllabus at the risk of losing your most recent edits. (But beware, if you run Jupyter offline for stability, this course also has defective input filenames that will cause grading to fail - read the discussion forums first.)