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Вернуться к Introduction to Data Science in Python

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

Оценки: 25,656
Рецензии: 5,715

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


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


28 сент. 2021 г.

This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.

Фильтр по:

5101–5125 из 5,666 отзывов о курсе Introduction to Data Science in Python

автор: Souvik B

8 июня 2020 г.

Not at all for beginnners. Fast-paced with more focus on self-learning and grinding,rather than focussing more upon the concepts. Dry presentation.

автор: Konstantin K

4 мар. 2018 г.

Quite bad knowledge delivery from lectures. The course is rather self learning than course. A lot of vague points and uncertainties in assignments.

автор: VARUN K

4 мар. 2017 г.

The course instructor could have been more elaborate with the examples. I felt there was a wide gap between the exercises and the course material.

автор: Justin L

6 дек. 2016 г.

Assignments are challenging, but some questions are very vague and require lots of trial and error guesswork to get the autograder to accept them.

автор: pouya S

29 июня 2018 г.

Assignments are great to reinforce your learning. But the instructor does not cover many topics and leave you with a lot of questions unanswered.

автор: Hanwen L

15 авг. 2019 г.

Please update the auto-grader such that is it compatible with current version of Jupyter notebook, very frustrating dealing compatibility issues

автор: Hemanta B

13 авг. 2019 г.

This course is a nicely organized. However assignments are not completely clear. Especially assignment 4 needs more explanation and details.

автор: Joel B

1 авг. 2019 г.

Subject matter was very good. Some of the assignments were not clear on instruction, and some of the Coursera functions were buggy or broken

автор: Paul A

5 нояб. 2018 г.

Material delivered a bit too rapidly to effectively assimilate. Often, further external research is needed to find solutions to assignments.

автор: John W

27 мар. 2019 г.

I don't think this is a good enough course to "teach" you "data-science". All this does is give you an overview of things you need to know.

автор: Ahmad A

24 июня 2018 г.

The assignment descriptions needs to be precise (with psuedo code).And the statistics part needed a lot visualization to aid understanding.

автор: Jordan K

19 мая 2018 г.

The material is valuable and taught well. The lectures are impossibly fast paced (lots of pausing) and the assignments are often ambiguous.

автор: Adam P

13 мар. 2022 г.

Assignments were more difficult than they needed to be because many of the directions were unclear. Otherwise, the class was interesting.

автор: Vipin G

16 дек. 2017 г.

Great Assignments, Great learning, but requires good "prior" knowledge of Python and Pandas. This is more of a refresher course in Pandas.

автор: Marat K

11 нояб. 2017 г.

Much more time needs to be invested into theory of the data frames. The course is too lightweight for the heavyweight topic it's covering.

автор: SHUVA M

3 сент. 2020 г.

Course materials should be scrutinized. It's like the mentor is going through a scripted page. I understood very little from this course.

автор: Tobias T

26 авг. 2020 г.

Good course for the basics, but the assignments are very difficult as lectures do not cover everything which is asked in the assignments.

автор: Greg S

4 янв. 2018 г.

Great Content. Course Auto-Grader was immensely frustrating. Videos aren't very helpful except to identify where to do your self study.

автор: Sai S B

19 июня 2020 г.

The course assignments are at a very good level. But, I feel the course doesn't prepare you for that. Most of the work is self-learning.

автор: Kelsey S

17 авг. 2018 г.

The examples used are so small it's hard to understand how to use these skills in real-world situations if you aren't as used to Python.

автор: Michal Z

5 янв. 2018 г.

There should be more Pandas API hints in lectures, it ware really hard to find optimal ways to perform operations on DataFrames I wanted

автор: Francesco L

5 мар. 2017 г.

The course lessons could have been more specific and provide more explanations on many topics that are later required in the assignments

автор: Jimi O

28 мая 2019 г.

Lectures are interesting but coursework is challenging. It requires significant external reading and understanding to stand a chance.

автор: BRUNO C D D F

6 июля 2020 г.

The demanded exercises were way harder than the content taught. Also, the main teacher isn't didatic, he speaks in a monotonous way.

автор: Morales J S

21 июня 2020 г.

is good to make students to investigate but, in the whole course i was thinking that youtube teached me more than the course itself.