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

4.5
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23,117 calificaciones
5,186 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

PK
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

SI
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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3151 - 3175 de 5,110 revisiones para Introduction to Data Science in Python

por ANISH V N

25 de jun. de 2020

The videos were little fast paced. The explanation were good. It would be better if more detailed explanation on pandas has been given. Some more functions of pandas must be given because in assignment part the questions were little tricky and the question of that type has not discussed. You have to learn more from other resources. However overall course was good, it gives a good explanation on how to use python to manipulate data in table and many more. Before opting this course the individual should have a good grasp on basics of python.

por Faris

28 de ene. de 2017

A lot of the harder questions were unclear or not covered by the course. Many were passed onto "look this up yourself", which is fine, but sometimes a different approach resulted in a slightly different answer marked as wrong. The group_by stuff was especially tricky, as it was unlike its SQL counterpart and could have used a better example, eg with a function to split into quarters or group columns. Other than that, very interesting overall and very neat datasets. I would encourage having students check out kaggle.com for extra credit.

por Dusan Z

5 de mar. de 2020

Small criticism - either requirements.txt should be published (version dependencies) or the course should be updated for to reflect modern versions of pandas, or perhaps both. Autograder has a tendency not to be able to process subtle differences in dataframe shapes from time to time and this is discussed at length in the forums but nothing much is done to rectify. Possibly related to obsolete versions and dependencies. Questions could be slightly better formed. All in all, a great course, which has kept me busy for a couple of weeks!

por Bhavin P

11 de nov. de 2018

Introduction to Data Science in Python is a nice introductory course to jumpstart your career in the field of data-science while working with python. The course assumes you have basic knowledge of python programming including its syntax and semantics. Later, the course quickly dives into topics like list comprehensions, lambda expressions, numpy arrays, pandas series/dataframes. Most, of the common tasks involved in the field of data-science such as data cleansing, data analysis and manipulation are thoroughly covered in this course.

por Robert S

6 de dic. de 2017

More about Python skills related to Data Science than Data Science itself.

Good quality of lectures, although they are very dry and brief. Assignments are challenging and require some googling/extra study. Supplementary materials are very inspiring - I enjoyed them the most.

If you want to be well prepared to use Python in data science field and have some time to spend working on the assignments - this is the course for you.

If you want the gentle introduction to the whole discipline - this is not the starting point I would recommend.

por Francois

20 de nov. de 2016

This was way above my level knowledge. One will definitely a strong level of knowledge in stats and data analysis to begin with. The Python part is but only a tool in this module / course.

For that reason I found the tutorials way too fast paced. I had to go back on everything and test, so a 5 mins tutorial video would sometimes take me 30 mins or longer to complete.

The 3 hour / 4 hour assessments took me days!

Yet - a great way to jump into something new! The course itself is great. The presentation and content is just as great.

por Danteswara R P

28 de oct. de 2017

Excellent course challenging at every stage. Especially the emphasis on assignments without spoon feeding is really brilliant.

One suggestion is during the assignments, especially week4 there are situations where Pandas was giving lot of trouble. For beginners it is becoming trial and error in between. Will be good if you can collate such challenging aspects and include optional tutorial for students to specialize in those topics. YOu need not cover everything but some comprehensive on such topics. Otherwise course is excellent

por Cheryl M

20 de nov. de 2017

Good information but the auto-grader could be re-tooled to provide more feed back, especially for common issues. I would like more reference material for basic python/panda operations with examples. I should not need to dig into a forum to get a link to commonly used syntax. Having a basic reference guide with clear examples would have saved me hours of time and my retention and the overall experience would have been much improved (please use your class to share what you know, it does not have to be this painful)

por Álvaro T J

20 de nov. de 2016

The theory and examples are very good and cover a lot of functionalities. I like most the questions that pop up in the middle of the videos, it is a great way to check whether you understood the concepts.

Some parts require refinements, the questions in the middle of the videos must be synchronized with the content (there is a little time gap) and the questions in the weekly assignments must be reviewed in depth, I dislike wasting a lot of time trying to understanding what is required to answer in each question.

por Ramazan A

28 de jun. de 2020

Very technical course, heavily focused on working with data, oriented on self-study. It is more about Python how to deal with datasets in it, in the very end, you got introduced to t-test co make basic statistical test, without any underlying math concepts.

Get ready to visit official Python websites, libraries documentation, forums like GeeksforGeeks, Stack, etc. Assignments are hard but very useful, however, I would prefer more lectures on Python functionality, since course material does not cover much.

por Peng J

10 de oct. de 2019

The material covered is solid, however the one missing star in my rating is attributed to poor design of the assignments. To be specific, there's a lack of clarity in the instructions, also there is a HUGE leap between the learning examples and the last assignment. Understood that it takes some self-learning and searching over the internet, but for the fees I paid I would expect the course to provide the materials readily. Otherwise, what is the point of me coming here, I could have get a book and learn!

por Joel

27 de may. de 2019

The content of this course is excellent and the assignements deliver a good learning experience which encourages independant problem solving. The autograder can be frustrating at times due to version errors which occasionally arise when working offline. This can be demoralising at the outset, but all of the information required to pass the assignments can be found in the lecture materials and discussion posts. 5-stars for an updated autograder or some more obvious information regarding its limitations.

por Paweł Z

17 de mar. de 2018

It's really good but the assignments are super ambigous and you will have to browse through the forum to get the info what exactly you are supposed to do (not exactly a bad thing, but may be very frustrating), couple of times I just changed something ambigous in my code to the "other option" and passed this way submission - probably tutors would like their students not to do that, but it's a good strategy in this situation. The forum is essential in passing the course, I guess it shouldn't be this way.

por Subham K S

10 de ene. de 2020

Very Good content. Quite challenging for someone new to data processing. Great Experience. Thank you Coursera and University of Michigan.

Suggestion: I had to google many stuffs to find some python methods and data processing tricks. These things can be provided as reference material. But It is not a big deal as searching and working out my way to the completion was fun.

Also more exercises can be provided before assignment to make the students a bit more acquainted before attempting assignments.

por Christopher H

20 de nov. de 2016

Great! Even for someone with some python / pandas experience, it was a nice refresher / found new tips / tricks. I actually enjoyed the fact that the assignments didn't hold your hand and forced students to solve problems with the forum and other resources. Look forward to the rest of the courses in this specialisation!

Two aspects you could improve is teaching is (1) Multiindex and (2) Regex. It seemed like you purposely avoided discussing these. These should be a mandatory subjects to learn.

por Abigail H

2 de ago. de 2017

This was a great introduction to the material which gave me enough of a background to start doing small projects myself. I didn't find the videos necessarily that helpful - I mostly learned by reading documentation and stackoverflow responses - but the assignments were well-structured and challenging.

My only problem with this was that the automatic grader took some getting used to, and I think that the first programming assignment should have come with some basic notes about how to handle it.

por Soo X W

2 de ago. de 2020

I learned a lot thanks to the challenging assignments, lectures were a little too fast and confusing at times, but the jupyter notebooks provided are very useful to interact with and learn.

Would have given 5 stars if they update their python version and pandas library version, some new functions I used on my local machine don't work on the coursera jupyter notebook, and it is really annoying to have to go out of my way to change code that works in latest versions but not in older version.

por Ritu R K

3 de jul. de 2020

This course helped me get started with data science. It contains a lot of useful methods and functions which prove to be worthy in day to day python programming as well. One must have a good understanding of basic python before enrolling for this course and must study the reference books mentioned to ace the assignments. The only CON of this course is that the assignments are difficult when compared to the tutorials but, as I said above, one must study the references to keep up with it.

por Robert E

2 de oct. de 2018

I found the exams, especially the final 2, to be very challenging. The estimated exam completion time was 2 hours. I must have averaged 20 hours. The exam difficulty is evinced by the numerous questions posted on the forum. Kudos to staff member Yusuf Ertas for his helpful and timely responses! Much material necessary for the exams was NOT covered in the lectures. Although the lectures were excellent, I feel the majority of my learning took place on outside research for the exams.

por Aditya V

2 de oct. de 2019

A good course to start Data Science. However, it would be better if the videos could be more explanatory. This course has very short videos and assumes that the candidate should do EVERYTHING (in terms of research on external resources or reading elsewhere). The assignments are challenging enough for you to learn the technology pretty well but again, there could have been better, longer and more in-depth explanations to cover the assignments. A lot of time goes in doing your research.

por Dheerendra P

8 de dic. de 2020

I found this case to be incredibly useful in helping me understand the data analysis techniques using Python. My suggestion would be that the week 1 and week 2 assignments could be updated to have multiple simple questions around various methods to be applied for selecting, inserting, transforming data frames and series. Also, more assignment questions could be included for numpy. Week 3 and Week 4 examples were identical to real world problems data scientists face when merging data.

por João T B P

24 de mar. de 2017

The video classes are informative and very clear. The exercises during the classes were thorough and allowed me to apprehend the concepts at a good pace. My only problem were the auto-graded exams. Sometimes the questions are not crystal clear and the auto-grader isn't intelligent enough to discern between a logical mistake and some detail about table formatting. Fortunately these problems are discussed in the forums and usually one can get clues on what they can be from the mentors.

por Ben B

10 de nov. de 2020

Overall, a good introduction to data science. You need some prior experience with Python and statistics - otherwise the course might be a bit hard. If you have these prerequisites the assignments are not too easy and not too hard. I especially loved the auto-graded jupyter notebook assignments.

What I did not like are the quizzes. These are done using the UM-website and you get your grade for a multiple choice quiz 24 hours later without any feedback (at least I could not find it).

por Manuel O A

6 de abr. de 2020

Even when I have experience with python, sometimes I feel explanations of pandas functions where not explained with enough dedicated time. Some questions in the homework have ambiguous parts, difficult to interpret. The autograder has technical problems to grade answers. Some information would be very useful to have in the instructions of the assignments, not located in the forums.

I liked a lot the examples themes, ie. energy and economics. Videos are very easy to comprehend.

por Vinodhraj M

27 de sep. de 2019

Loved the course. Thought it did not teach every technique needed to complete the assignment, it definitely gave directions how to complete it. The forums were very useful to submit assignments. The assignments were quite challenging and interesting. It taught me ways to manipulate data. I started looking data in a different sense now and understand how much information we can mine from a boring looking data. I believe I will be able to apply the knowledge in my job. Thanks much