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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: 26,498

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:

5351–5375 из 5,809 отзывов о курсе Introduction to Data Science in Python

автор: Shivam P

26 июня 2020 г.

The assignment is too difficult compared to what they teach in course

автор: tushar s

20 июня 2020 г.

Not providing in-depth knowledge of functions through video lectures.

автор: Chirag S

18 мая 2020 г.

Expected a detailed explanation instead got a very brief explanation.

автор: Christopher C

30 мар. 2020 г.

Only a few resources. Each Jupyter Notebook lack context and comments

автор: Stefani N

29 мая 2018 г.

I think the assigments are a bit difficult for an introduction course

автор: Jaepil L

26 мар. 2018 г.

nice intro, but requires background knowledge and self-study,,,,a lot

автор: Thanasis M

26 авг. 2017 г.

Very handy exercises, but the lesson lacked in examples and guidance.

автор: Cunquan Q

11 мар. 2020 г.

Too quickly for me! I need to stop and type the codes on my laptop!

автор: nitish k p

15 июня 2020 г.

the course should be updated and assignment questions are unclear

автор: Azadeh T

8 июня 2020 г.

The assignments are waaaay more difficult than the class material

автор: Subiksha P

3 июня 2020 г.

Last week assignment involved topic which was not taught in-depth

автор: Torsha M

4 сент. 2020 г.

It has helped in building data structure and understanding of so

автор: riffault f

12 мая 2019 г.

I would like to have more precisions during the video courses

автор: Tan L M

13 мая 2020 г.

Course assignment is not on the same level as content taught

автор: Alessandro M

28 июля 2020 г.

Very good assignments shame the video lessons are very poor

автор: Asheesh L

28 янв. 2019 г.

Course was ok. Submission of assignments is really painful.

автор: Rounak C

28 апр. 2020 г.

Amazing content but needs to be a little more structured

автор: Giorgi B

19 апр. 2017 г.

Much more difficult assignments than taught in lectures

автор: Swathi P P

2 июня 2020 г.

this course could have covered much more deeper topics


29 мая 2020 г.

Its very useful for me. thank you so much for help me.

автор: Viren S

7 июня 2020 г.

Need basics to be cleared before entering this course

автор: Galen S

26 сент. 2017 г.

Thought assignments could have been better designed

автор: Michael T

27 дек. 2016 г.

To much disconnect between assignments and lectures

автор: Samuel L

4 июня 2020 г.

Please update the course pandas and numpy version!

автор: 18P917 V R M

5 июня 2020 г.

Assignment questions are not clearly communicated