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Introduction to Data Science in Python, University of Michigan

4.5
8,343 calificaciones
2,103 revisiones

Acerca de este Curso

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....

Principales revisiones

por 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 .

por 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!

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2,040 revisiones

por Nikhil Yadav

Dec 13, 2018

Thank you Coursera for providing such a wonderful course.

por Alejandro Suarez

Dec 13, 2018

The videos are pithy and the exercises are challenging for beginners. This class is an introductory course, but not from scratch. If you haven't dabbled with Pandas before you may find this class extremely fast paced.

por Saman Hosseini Ashtiani

Dec 13, 2018

This course zeroes in on the best prerequisites to start towards data handling with python.

por Adi.Vamsi Sairam

Dec 12, 2018

It's good but I'm unhappy with the programming tutorials ie. Jupiter note book section no explanation is given

por Sabina Dobrer

Dec 12, 2018

For someone knows basic python only some of the things are explained really fast. However, you can catch up during the assignments. A lot of time for the assignments spend on finding possible solution on the forums. From one point of view it is good - so you can find you own style. Form another, if you work full time and have busy schedule - it will take much longer to finish all the assignments.

por Sarah Hagan Hudspeth

Dec 11, 2018

I really enjoyed this course, but i am so glad I came prepared and had completed other Data Science tracks on other online sites (Codecademy, Dataquest, DataCamp, etc). I had to put all the skills learned elsewhere to good use in this course. The course was challenging--which is why I wanted to take it! I feel like I had to problem-solve, code, and work with data at a high level.

por Weinan Hu

Dec 11, 2018

It is a very nice course, providing you are able to pour a LOT of individual efforts into it, especially into the assignments. The knowledge required to finish the course is far beyond what they taught. I spent 70% of my time in the course on Stackoverflow or Google, or asking my friends. However, I admitted I gained huge amount of knowledge and experiences, as well as "individual research skills" and confidence, from this course. I would recommend going through this course in a much shorter period of time than recommended (like in a week), during your vacation or week off.

por Syed Danyal Hassan

Dec 10, 2018

Best Course ever!!

por Min Li

Dec 10, 2018

Good exercise, very practical course, teach and self learning combined.

por Robert J King

Dec 10, 2018

Even though I am already a heavy user of Pandas in my daily work, this course forced me to learn several useful features that I had never knew about or bothered to learn. The exercises were challenging enough that it took a decent amount of time and effort to complete them. There were many technical challenges with the autograder and the coursera hosted notebooks that made this more of a challenge than it should have been.