Chevron Left
返回到 Introduction to Data Science in Python

学生对 University of Michigan 提供的 Introduction to Data Science in Python 的评价和反馈

4.5
27,240 个评分

课程概述

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....

热门审阅

ME

Jul 26, 2020

Quizzes were very challenging and interesting. I learned alot. The main drawback in this course is that the materials are insufficient to answer the assignments.And some questions were not so clear.

NF

Jun 17, 2018

I thought this was course was good, and was fairly challenging for an online-only course. I thought the lectures could have been a little longer to ensure proper coverage of materials and functions.

筛选依据:

1976 - Introduction to Data Science in Python 的 2000 个评论(共 5,986 个)

创建者 David B

Apr 27, 2020

An excellent introduction to pandas using carefully crafted prompts.

创建者 Yi-Ching L

Apr 2, 2020

I have learned a lot and thanks for the excellent lessons of python.

创建者 Pablo E E D

Mar 13, 2020

Un muy buen curso. Muchas gracias por dejarme ser partícipe de esto.

创建者 poojari d

Aug 19, 2019

this course is excellent and it is very helpful for my other courses

创建者 Neillon C M M

Aug 10, 2019

Excellent!!! Maybe add some statistics or math in the qualification.

创建者 Skills G

Aug 9, 2019

one of the best website that provide the best knowledge all the time

创建者 Umer G

Apr 25, 2019

It is a good course and i learned a lot about pandas in this course.

创建者 Stanislav H

Aug 23, 2018

This course was sometimes challenging for me, that's why I loved it!

创建者 Prabhat K

Jan 13, 2018

Nice course. Specially Python is well utilised to learn data science

创建者 Konstantin B

Sep 7, 2017

Excellent course! So much practice. There are few courses like this!

创建者 saeid r t k

Dec 1, 2016

it has a very good method of teaching and it is very informative :))

创建者 Zhihang Y

Jul 7, 2021

assignment 3&4 is too difficult! but indeed can learn a lot from it

创建者 Olga D

Jan 27, 2021

Very structured lectures and examples based on real-world problems.

创建者 Eduard I B M

Nov 1, 2020

Es un muy buen curso para aprender Python, lo recomiendo muchísimo!

创建者 Dr.M. E

Oct 10, 2020

faced some difficulties in assignment submission but resolved later

创建者 Prakhar S

Sep 24, 2020

The assignments makes sure that you learnt well, highly recommended

创建者 Krerkkrai R

Jul 27, 2020

Great subject if you want to go deeper in data science with python.

创建者 venkatesh s p

Jul 24, 2020

very good course for student how have strong basics of data science

创建者 Bharat

Jul 22, 2020

Great Assignment . It really test your skills in python and pandas.

创建者 Hengky K S

Jul 17, 2020

A great introductory course and recommended for beginner in Python.

创建者 PAPARAO K

Apr 22, 2020

Very good learning platform which I first implemented successfully.

创建者 babayevasad

Apr 5, 2020

It is enough for learning basics of python for data science. Thanks

创建者 SULIVA, R S (

Mar 15, 2020

This course help me a lot of utilizing python to handle large data.

创建者 Boris S

Jan 23, 2020

Perfect level of difficulty and very comfortable and easy to learn!

创建者 Dr. A K D

Jul 6, 2019

I am highly reccommending this course for students and researchers.