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

4.5
звезд
Оценки: 24,558
Рецензии: 5,516

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:

3926–3950 из 5,466 отзывов о курсе Introduction to Data Science in Python

автор: 21_Keshav M

5 авг. 2020 г.

The Structure of Course is Great!! Although I would love to have mentors explain concepts a little more. Overall a great introductory course.

автор: Stefanie N

25 нояб. 2017 г.

The help in the forum was good, the assignments were fun although I always had some problems with the grader at first, some resolved some not

автор: Roshni G

2 февр. 2017 г.

The assignments were challenging and cool. Lot of self-study needed to crack them. The lecture videos could have been a bit more interesting.

автор: ASHISH B

3 июля 2020 г.

The assignments were very interesting and the teaching also was very good. The main help was the provision of notes in the jupyter notebook

автор: Sven E

16 нояб. 2019 г.

assignments quite challenging , way more time needed than the est. times given by coursera. happy I could finish it. on to the next course !

автор: Marc

8 дек. 2017 г.

Curso interesante para iniciarse en la librería pandas. A veces vas algo perdido pero dedicandole esfuerzo y atención aprendes muchas cosas.

автор: Jason R

16 окт. 2017 г.

Could have been more challenging and worked with more interested bigger datasets but was a great way to get up to speed on pandas abilities.

автор: Paula C R

20 июня 2017 г.

The course is really nice, hands-on all the time. Some questions of the assignments could be improved to avoid ambiguity/subjectivity tough.

автор: Ishank T

31 мая 2020 г.

Not beginner friendly, great assignments which require "stackoverflow" skill. you actually learn from assignment. Videos are not that great

автор: Dominic l H

5 мар. 2018 г.

good course overall but there needs to be more information on code profiling/optimizing it is really required to pass a part of question 4.

автор: luciano d f a

26 янв. 2018 г.

A good introduction to Pythonic data science programming tools. Just bit too fast in exposition for my learning curve. However I liked it.

автор: Juntao G

16 нояб. 2017 г.

Mostly good. However the question quality of homework 4 should be improved. The way how questions are expressed is ambiguous and confusing.

автор: Seyyed M A D

19 июля 2021 г.

Educational materials are more than great. Lectures and notebook resources are A+++.

However, programming assignments are not interesting.

автор: Matteo C

7 июня 2021 г.

G​ood but I personally found the time required for the assignments a bit unrealistic, having some basics in programming but not in Python.

автор: Sirajalam S

20 авг. 2020 г.

The course is very interesting and quite informative. I got a lot of information about Data Science and its application in various fields.

автор: Germano R

23 мая 2019 г.

Great course for beginners, as well as for those with previous data science experience with other programming languages (i.e. R Computing)

автор: Manas A

25 дек. 2018 г.

Overall a really great course with a great deal of skills and information, but i wish the coursework assignments were a little bit easier.

автор: ALISON J D

3 янв. 2018 г.

The course was challenging but fun. To complete the assignments you need to research python on the Internet and consult the course forums.

автор: Eduardo S J d P

26 нояб. 2016 г.

Autograder is hard to understand and has no feedback. Could improve the feedback mechanism, maybe with peer review. Thanks for the course!

автор: Md. N K S

23 июля 2020 г.

Assignment 3 and 4 was much difficult then others, I have to submit 3 times and have spent more then 7 hrs. Ultimately i have learn good.

автор: devansh v

21 июня 2020 г.

The course is really good but a little more insight to pandas would have made it even better and also the auto-graders is a little buggy.

автор: MUSTAFA Ö

23 апр. 2020 г.

Assignments of the course are more educative than video contents . Because videos include short and insufficent information about topics.

автор: Indranil B

8 июля 2017 г.

A good course overall.The assignments are challenging and promote a lot of self learning.The Jupyter Notebook integration is also a plus.

автор: Martin D

19 окт. 2020 г.

Very good explanations of course material, interesting assignements, but some instructions in graded assignements were not always clear.

автор: Misa

28 сент. 2018 г.

Overall very good. Like the lecture styles. Some of the questions in the assignments aren't clear, but the discussion forum helps a lot.