Chevron Left
Вернуться к Introduction to Data Science in Python

Introduction to Data Science in Python, Мичиганский университет

4.5
Оценки: 10,066
Рецензии: 2,398

Об этом 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

автор: SI

Mar 16, 2018

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 .

автор: AU

Dec 10, 2017

Wow, this was amazing. Learned a lot (mostly thanks to stack overflow) but the course also opened my eyes to all the possibilities available out there and I feel like i'm only scratching the surface!

Фильтр по:

Рецензии: 2,330

автор: Robert Dorrigan

Apr 24, 2019

Excellent videos with detailed explanations.

автор: 한태경

Apr 23, 2019

assignments too hards, so it's worth it.

recommend to everyone who want to be a data scientist or learn how to work python data science

автор: Kai Philipp Hagelauer

Apr 22, 2019

For me, this was a difficult course in which I learned a lot. I did not find the materials (videos etc.) provided in the course so helpful, but the assignments you get for your own programming are very close to real world problems and will give you real experience. So you will need some other material to learn, I recommend the book "Python Data Science Handbook" by Jake VanderPlas.

автор: Santhosh Kumar A

Apr 22, 2019

Course was awesome assignments need a lot of research and googling, I liked the course a lot.

автор: Ehtisham

Apr 21, 2019

This course is amazing and the instructor teach everything in very easy way

автор: Babic Sven

Apr 20, 2019

A very good for learning the basic tools in Python, needed to delve deeper into data science. However, unless you already have experience with python and pandas, I would take the time estimates in the course as a very conservative estimate of the effort needed to complete the assignments!!!

автор: Changyu Gao

Apr 20, 2019

A quick introduction to Python and Data Science. The assignments are not as easy as you might think. To those who feel the assignment of Week 4 daunting, keep going -- data cleaning per se is not a difficult task yet a somewhat tedious one.

Thanks to the course team. I shall continue towards the following courses.

автор: Marcel Kornacker

Apr 19, 2019

It would be nice if Coursera could update the Python environment used for the exercises and assignments to something recent. The version they're using (0.19) is fairly old. Every single assignment that I had running against 0.24 had to be altered in some way to work for 0.19.

автор: Paolo G. Hilado

Apr 19, 2019

I am quite unsure of where to send a mail to express my gratitude for the scholarship. As such, I guess this may be a good venue for it. Thank you for the scholarship Coursera and University of Michigan.

автор: Kevin McHale

Apr 18, 2019

This course lacked written material to accompany the videos and the reference books are presented in a much different flow, so you are left to jump through books and posts to get through anything. Having the content packaged and delivered in succinct format is what I was looking for and this did not provide that.