Chevron Left
Вернуться к Introduction to Data Science in Python

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

Оценки: 24,685
Рецензии: 5,538

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

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:

4351–4375 из 5,483 отзывов о курсе Introduction to Data Science in Python

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

автор: Deleted A

5 авг. 2018 г.

It has been an awesome experience, I have learnt a great deal.

автор: Anudeep A

23 июня 2017 г.

The course is properly structured and tutorials are very good

автор: Gaurav R J

19 апр. 2017 г.

The teach and assignment are not in line in difficulty levels.

автор: V V

1 апр. 2017 г.

hard assignments, lecture was not enough to cover assignments.

автор: Mohammed A A

20 июня 2020 г.

Really good course. Just gotta dig up stuff on your own alot.

автор: Christopher J

19 июня 2020 г.

Excellent course, though the interpreter needs to be updated.

автор: SHIVANG S

4 мая 2020 г.

Very difficult for those who are new to this numpy or pandas

автор: Sahil S

2 февр. 2020 г.


although last week lectures need to be improved.

автор: Kartik K

2 авг. 2019 г.

Bit difficult for one who is not from programming background.

автор: David B

16 сент. 2018 г.

Interesting exercise if you're willing to trawl StackOverflow

автор: PRAKASH S

1 мая 2020 г.

There is a lot of scope for the improvment of vedio lessons.

автор: Nasir H

7 янв. 2020 г.

The content was high level and good. You should explain more

автор: Hannah

1 сент. 2019 г.

Good courses and exercices but difficult to submit sometimes

автор: Andreas B

27 дек. 2018 г.

Great course. Tasks being not precise is sometimes annoying.