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

4.5
звезд
Оценки: 24,665
Рецензии: 5,537

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:

3876–3900 из 5,482 отзывов о курсе Introduction to Data Science in Python

автор: Michael L

18 авг. 2019 г.

Great course! By this time of August 2019, it is a bit dated. I wish University of Michigan would update it with current project and updated version of Pandas.

автор: Danny P

20 февр. 2017 г.

The course was structured well, there were a lot of assignments that really help with learning pandas. The lectures were ok, could've been a bit longer though.

автор: Mikael F

11 дек. 2016 г.

Very good intro into how to use Pandas for working with data sets and get insights from your data by using various aggregating functions and statistical tests.

автор: Yufan L

11 июля 2019 г.

the assignment is discounted from the lecture, it is way harder. But I learnt the most from the assignment and the forum. Overall, it improved my skill a lot!

автор: Chris R

12 янв. 2019 г.

Sometimes my time was spent trying to get to grips with the ambiguity of the question. this was quite often frustrating, but overall good course, learn a lot.

автор: Kristin K

20 сент. 2017 г.

A challenging and fast moving course. I recommend studying up on basic python before taking the course and to pause the videos often to understand each piece.

автор: Adelson D

12 февр. 2017 г.

The course is very good to introduce people with pandas usage, however, the autograder and little data issues on the assignments costs a lot of learning time.

автор: Xuening H

8 янв. 2020 г.

The assignments is awesome!

But it requires too much self learning.

The course will be better if the professor can teach a little bit more about the functions~

автор: Dalton P

28 дек. 2017 г.

Excellent instructor with high level talk that goes into the details just enough. Recommended to have some programming experience but you can get by without.

автор: Syahrul F G

14 июля 2020 г.

The course explained in brief, rich in theory but not much in practice, so a lot more individual learning is needed more than the time taking of this course

автор: Bryan R

26 мая 2019 г.

This course is a fairly good introduction. Expect to spend a good amount of time searching forums for guidance on completing some of the weekly assignments.

автор: Deleted A

22 апр. 2018 г.

The coursework was amazing but if the student start without having strong python knowledge, it can turn out to be very difficult especially the assignments.

автор: Chunxiao L

4 апр. 2018 г.

I like the assignment a lot, but the course material part contains too many talking. I would like to suggest it includes as much as coding, and less talking

автор: agenis-nevers

4 янв. 2019 г.

Good course.

Some trouble with the assignments, difficult to understand what's wrong with one submission (could be good to have someone look at your code).

автор: Jim K

18 февр. 2017 г.

Nice course. I really liked how I could proceed at my own pace. The computer-graded assignments were very helpful and the Forums were good sources of info.

автор: Mario K

13 окт. 2018 г.

Some task descriptions in final assignments were not fully clear so I had to resubmit my work to pass. Otherwise, was good to refresh my pandas knowledge.

автор: Salamat B

13 февр. 2018 г.

Content of this course is very practical and good. I find it bit difficult to understand language since explanations are fast and contains a lot of terms.

автор: Robert P

10 апр. 2020 г.

Great intro to handling data with python tools. I love the fact that "why" is answered and not just "how" to do your research. Easy to listen and follow.

автор: Jithesh K B

9 февр. 2020 г.

Very good course and I learned all the new stuff like pandas, numpy and hypothesis testing etc. Assignments were very good to concrete my understandings.

автор: Jose P

14 нояб. 2018 г.

Good introductory course, challenging to basic level and forcing to use new technics and think out of the box. Recommended for first contact with Python.

автор: John A M

24 июля 2017 г.

Maybe 3.5 stars, the material is good, but unless you are active in the forums (and others are as well), the assignments can be frustrating to complete.

автор: Michał K

3 июня 2017 г.

Very well prepared. 4 stars because it only scratches the surface of data wrangling with Pandas. I'd love to see more comprehensive course about Pandas.

автор: raghav b

21 окт. 2020 г.

Some of the assignments were very hard compared to what what our instructor teaching,

Our instructor teached us well but the assignments very quite hard

автор: Rashmi k

29 апр. 2020 г.

I feel, lessons are very brief. Should have some better coverage of topics. Assignments are good to challange your mind. Overall it was good learning .