Introduction to Data Science in Python

The auto-grader system does not provide any useful information for understanding why my answer is wrong. Many questions are not clear enough to understand what they mean, we need to adopt trial-and-error approach to find the correct answer. Finally I aborted on the second course of this specialization.

This course is excellent if you're looking to learn how to use Pandas inside Jupyter Notebooks. Assignments are autograded and feedback can be received immediately. Course is a few years old and discussion forums contain answers to common questions

Generally, very good selection of content. The explanations are insufficient for passing the assignments tho, which means that most of the course work is self-study from the web. The buggy auto-grader sometime made the submission of the assignments quite a pain as one has to find a way to change the code in a way that still produces the right answer but doesn't blow up the auto-grader.

Frankly, I did not watch the lessons at all, although they seem good.The assignments were really great ! Challenging and very rewarding.Really recommend the course !

Excellent
material. Admittedly I can see why there are so many negative reviews about the
ambiguity of the assessed tasks. It won't be an easy course for anyone who is
unfamiliar with programming. However, if you do have programming
experience under your belt, you'll likely find this course strikes an
excellent balance in terms of conciseness, practice, and theory. Each
lecture is crafted carefully to teach you about some nuance of pandas or numpy,
and the programming assignments are packed with coding questions that will help
you revise what you have learned, in a very efficient way. There is very little
"fluff" in this course, which is a major weakness I've seen in
similar courses of its kind. Too much spoon feeding often does not challenge or
engage the learner. The course is very direct about what it expects of its
students. Every week there is a comment "This week's assignment requires
more self-learning than the last". And true to its word, there is less and
less hand-holding as you go further into the course. I thoroughly enjoyed the
material and probably learned the most out of this course than any other course
I've taken on Coursera, taking in to account its length.

The course is good. There is lot of stuff to learn in python.

Some 2 years after finishing this course, I cannot stress enough how much I have gained from this course (or the full specialization for that matter). Having started this specialization as a social science researcher with a solid background in traditional statistical research (and total beginner with Python), I have actually managed to find a job as a Data Scientist halfway through this specialization. This course and specialization will teach you how to use all the commonly used Python libraries for Data Science applications (Pandas, Matplotlib, Numpy, Scikit-Learn, NLTK etc.). And comparing to some of the other specializations I've taken in the field of Machine Learning, Deep Learning and Maths after completing this one, I can say that the programming assignments are by far the best I've encountered on Coursera so far. The learning curve is pretty steep at first (it was for a total beginner like me) but you'll learn a lot quickly. And by the end you'll be able to do most Data Science tasks independently. Highly recommended!

challenging but reading forums and stack overflow really helps

incredible course

A great course, really !

Muy buen curso, sumamente práctico.

This course is NOT for the those with zero experience in programming as it assumes some familiarity with concepts like object attributes, functions etc and requires you to spend some time reading up python/pandas documentation on your own. While I think that the professor could use simpler English to communicate more complicated concepts, overall it is a good course with good assignments that cover the key concepts required in data science.