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

Оценки: 25,176
Рецензии: 5,620

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

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.

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

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4401–4425 из 5,567 отзывов о курсе Introduction to Data Science in Python


24 июня 2021 г.

it was a nice course helped me understand more about data science

автор: Sangram K S

13 июля 2020 г.

It was good. The session can be more interactive and interesting.

автор: Gian C T G

25 июня 2020 г.

Need more excersices examples, some homeworks are quite difficult

автор: Subha M

31 мая 2020 г.

The course was good. But few points were not that up to the mark.

автор: Anil K C

6 мая 2020 г.

Good assignments to get you to learn the Pandas library in python

автор: Sadia a S

21 мар. 2020 г.

It is a helpful course for working with python for data analysis.

автор: Roy Y

17 июля 2019 г.

A great introduction. But assignment grading a little bit tricky.

автор: hadeer a

15 июля 2018 г.

The course was helpful, but the assignments weren't clear at all.

автор: Ahmed E B

27 янв. 2018 г.

It is a challenging course specially for beginners and assignment

автор: Dhruba s R C

17 авг. 2017 г.

A very detailed course. A must do for Data Science enthusiasts...

автор: Partha S

23 янв. 2021 г.

Its not an easy course and requires a lot of focus and attention


15 июля 2020 г.

The instruction of the homework is sometimes hard to understand.

автор: Julian L

16 июня 2020 г.

It would be much better if all the videos had Spanish subtitles.

автор: sourav g

21 мая 2020 г.

If you could also provide written material, that would be great.

автор: Bharath k M

8 дек. 2019 г.

Good Experience and learning. Overall gained a good confidence!!

автор: Andrew T

25 мая 2017 г.

There were some gaps that were tough to bridge, but I got there.

автор: Fan Z

19 сент. 2020 г.

The course is good in general but the grading system is a pain.

автор: Daniel

1 июля 2020 г.

Falto poner o darle incapie más a fondo a la libreria de pandas

автор: Natalia d p R C

20 апр. 2020 г.

bastante bueno aunque mucho tema fue buscado independientemente

автор: Shunjiang X

8 июля 2018 г.

Very vigorous course. A little hard but will learn quite a bit.

автор: Filip J

1 сент. 2020 г.

It was great but I lack suggested solutions after assignments.

автор: Igor A G Q

6 июля 2020 г.

great course. I would add more classes about the distributions

автор: Ananay S

19 мая 2020 г.

Great explained and top level content to learn the skills from

автор: Shashank S

6 сент. 2019 г.

Assignment 2 was way too tough, as per the concepts covered :(

автор: Mark P

14 июня 2019 г.

challenging but reading forums and stack overflow really helps