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Opiniones y comentarios de aprendices correspondientes a Introduction to Data Science in Python por parte de Universidad de Míchigan

23,156 calificaciones
5,195 reseña

Acerca del 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 reseñas

9 de may. de 2020

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 de mar. de 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 .

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3201 - 3225 de 5,117 revisiones para Introduction to Data Science in Python

por Terry A W J

29 de dic. de 2016

As compared to some other machine learning/data science courses offered "Introduction to Data Science in Python" was very pragmatic. Starting out with simple data cleaning and data structuring may not be the most exciting thing ever, but it was extremely useful to learn the basic tools needed to be a competent data scientist. One point of warning - the homework and projects took me about twice a long as suggested by the course notes.

por Carlos V

26 de nov. de 2018

Introduction to Data Science in Python is a challenging and rewarding Course, the instructor explanations are excellent, and the recommendations in relation to best practices utilizing Python, Pandas and Numpy are super valuable, the assignments are super challenging in particular because of the auto-grader and the substantial amount of pre-processing of data required for the assignments so book extra time to complete this Course.

por Hal S

27 de nov. de 2016

Lectures clear and well-organized. Homework needlessly complicated and with large gap from lecture material. Grader did not give enough info when rejecting submitted work. Weighting last problem at 50% of final week was unpleasant. Hosted platform allowed importing re and io.StringIO, but grader rejected them. Hosted platform had consistent kernel failure on my last solution, but it worked on another system and grader accepted it.

por Gowri T

27 de jun. de 2019

The course was challenging and the assignments well thought of. While I appreciate that a lot of learning was left to be done on stackoverflow with the intent of making us self reliant, a lot of us are already used to those forums and gathered around this course so information would be available in a centralized manner and time spent searching online could be minimized. I think the course designers totally did not get that point.

por Tarun S

25 de may. de 2020

I learned a lot about data handling and manipulation in python, pandas and NumPy. But I feel the course instruction was too fast to follow up, even to a python coder like me. The course expected one to learn a great deal of part from your own rather than relying on the video lectures. The assignments and quizzes were very challenging, pushing you towards your best. To conclude I think the instruction couldhave been much better.

por Pascal M

18 de nov. de 2017

Before Machine Learning comes a lot of Human Action. This Data Science course provides a solid basis for understanding and learning the inner works of manipulating very large datasets in Python. Besides the technical aspects I was pleasantly surprised to read and think about the ethical sides as well. I would rate this course 5-star if some exercices were better phrased or if more examples to make some exercises more manageable.

por mohit s

21 de nov. de 2018

A nice course to kick start Data Science. Doing the assignments will improve the learning and will boost the confidence about the topic. Tutor, TAs and discussion forums are very helpful, so, consult them if you get stuck somewhere. Coursera platform was flawless, course structure was good. But I expected more content would be covered in the course. So, overall it is good course to get an insight into the world of Data Science.

por Martin T

19 de dic. de 2016

After having taken several Data Science-related courses, this course seems like a good introduction to Python for Data Science applications. Not much 'actual Data Science' is covered in the course, however. It's more practically-oriented in the sense that it deals with data preparation (loading, cleaning and merging data). You don't get the luxury of the common perfectly-prepared csv-files in this course, which is a good thing!

por Will G

13 de feb. de 2017

Overall this was a great class. The programming assignments were the most valuable part of the course for me and were good practice for wrangling data with pandas. I did find some of the assignments asked questions in a way that were confusing and it was difficult to debug the answer based on the automatic grader. However, I'm looking forward to when the rest of the specialization is available, as this looks like a good track!

por Robert J K

9 de dic. de 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.

por Arindam D

24 de jun. de 2018

A great starting point for venturing into Data Science, for students/engineers who have some programming background. In my case I had the basics of Python covered , so it wasn't too hard to catch up.However, for enthusiasts with very limited programming experience.... Beware !!! It will appear to be too fast. My final conclusion .... spend 3-4 weeks to learn Python fundamentals and then enroll .... its very enlightening.

por Awik D

4 de may. de 2020

The lectures seem to be giving the bare minimum description of functions and stuff that makes it hard to understand the intuition behind the syntax and working of, say, a line of code that a given lecture tries to teach explaining how it helps serve a purpose. This, in turn, makes it hard to remember the syntax of functions. The assignments are very useful but take a long time since I barely learn from these lectures.

por Beda K

13 de jul. de 2017

I really liked this course. It gives a good overview of the pandas library and some associated topics. For me, it aligned very nicely with my personal interests. I would have liked some more advanced topics as well, but I understand that this is an introductory course, so it is not in its scope. The integrated Jupyter notebook feature of Coursera is very neat - both for reviewing code from lectures and for assignments.

por Eugene K

14 de dic. de 2016

Pretty good course. I have definitely learned a lot and would like to thank you the lecturer and all the people who were working to create this course. The only comment I have is following. Please, try to formulate the questions more clear in the homework assignments. The assignment # 4 is especially bad in this sense. You can look at the comments of people in the forum to understand that it is not just my own problem.

por Irene L

18 de may. de 2019

Good introduction to pandas/numpy. Requires some programming knowledge. Overall I would have liked more guidance during the videos or through course materials, assignments require a lot of self learning (mostly searching through pandas documentation and stack overflow). However the discussion forums are helpful and the assignments are very well designed to guide the student through learning the basics of data science.

por Subhrajit B

4 de dic. de 2017

The biggest reason for taking the course is it pulls together a few interesting datasets and has a data manipulation project based on the dataset.

The course also pulls together some interesting papers on ethical issues that could confront data scientists, traps data scientists fall into (p-hacking).

However, the material on dataframes covered is too sparse. User should learn dataframes from a pandas book / web sources.

por Deleted A

16 de jul. de 2019

The value in this course comes primarily from the assignments but the instructions tied to these assignments fell a bit short. It would be immensely helpful to have a short FAQ explaining how to set up your environment (i.e. which packages and versions to use) along with test files to verify assignment outputs. Digging through the discussion forum is sufficient but the ambiguity does lead to unnecessary frustration.

por Denes B

12 de nov. de 2017

IMHO I had to do too much self learning besides this course. I didn't come here to listen to instructions that I should browse stackoverflow and documentation papers -- I am doing that without this course as well. On the other hand it was very clearly undersandable and well said whichever was said during videos. Moreover examples were from real world, which made me work out practices that will come handy later on.

por Mohammed A

26 de ago. de 2018

I very much enjoyed this Data Science course!

However, I feel like there needs to be a more interactive environment between the platform and the student. I saw the mini quizzes in the videos a step in the right direction.

Also, I feel like if there were more videos, uses of functions, and providing multiple cases of real data science problems would be excellent.

Thank you for all who helped in making this course!

por Enrique J P

7 de may. de 2018

The auto grader could use some work, and it should be a bit more clear to users that this isnt't a magic bullet into data science. It requires alot of work and preferably quite a bit of experience with python.

But as a intermediate course with intro to data science I think its great and really reccomend it to people who have dabbled with data science before but never had a good roadmap to actually learning it.

por Kathirvel B

2 de may. de 2020

Positives: I really enjoyed the course and the exercises were a bit tough but helped me learn a LOT of useful information. It is a good course. I will highly recommend this.

Negatives: Some sections are rushed and is not much help. And the software version used in the course is outdated and hence we had to change the code multiple times as the syntaxes are not accepted in the auto grader. This is a shame.

por Jose C D A

26 de feb. de 2018

Although is a very nice course, it would be nice to start on easier programming basics. There are specific things that one's gotta' be inspired to be able to think on. Maybe adding a week before week 1, to introduce on certain syntax and intuition so we can put on practice what we learnt on the kernels since the content of week 1.

It was so delightful and challenging, that I want even more. Thank you.

por Vidya M S

6 de ago. de 2019

The rating 4/5 is for the assignments complexity . It requires the learner to work through the logic in pandas and self learn through the errors . This is the new way to learn w/o any spoon feeding . I reduced the rating to 4 and not 5 , beacuse I feel more content could have been given taught by the professors or leave a good piece of advice to the learners. 5/5 for Forum support by teaching staff.

por Sabina D

11 de dic. de 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 David H

19 de sep. de 2018

The course projects rely on material not yet presented and the automated grading system is very sensitive. I struggled with the assignments because when I had a problem it was difficult to pinpoint the source. Some of the blame is on myself for not using the discussion forums sooner, but this is not my first coursera specialization in data science / programming and I can honestly say I struggled.