Chevron Left
Volver a Introduction to Data Science in Python

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

25,758 calificaciones
5,733 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


28 de sep. de 2021

This is the practical course.There is some concepts and assignments like: pandas, data-frame, merge and time. The asg 3 and asg4 are difficult but I think that it's very useful and improve my ability.

Filtrar por:

326 - 350 de 5,684 revisiones para Introduction to Data Science in Python

por Purinut K

31 de may. de 2020

Even though, the title of the course is "Introduction" but to pass the assignment is quite complex and require a lot of understanding. You have to deal with struggling in Data in real life, such as unclean, remove header, unnecessary information in text etc. which is the most difficult part of processing the data.

I think this is a excellent course to learn how to deal with the data but will recommend it for the people who has quite strong background in programming. The first chapter is simple but do not overconfident. The second chapter can be very difficult to process data and get to know how to deal with dataframe. Once you uses to how to use "Jupyter Notebook", it will much easier though the rest of the course.

por Antonin P

15 de may. de 2018

Course is great. I have learned a lot, but I am affraid that the assignments are not for everyone. It is sometimes hard to find a way how to make an automatic grader to pass your programming assignment. I had to use a forum a lot, not to find a solution, but to find a trick how to convert my result to the correct format and so... In first assignment, it was poorly described how to submit it, for instance that the function can´t use variables from previous functions. However, thank you a lot for the course, I have learned Pandas and general Python even though I didn´t use them before. But I regret a bit of the time searching throughtout a forum when my results was right, but the grader didn´t let me pass through.

por Milan V

2 de sep. de 2019

An excellent course. Given the restrictions inherent to this kind of format of teaching (e.g. very short 'lecture' videos), I do not think that the course could have been organised any better. In other words, one gets the feeling that one has extracted the maximum of knowledge possible, within the limitations of the Coursera platform. This is probably in part due to the 'hands-on' approach to the programming assignments, which I though to be very well thought-of. I would also like to praise the course staff for being very active on the Discussion forums, and trying to answer as many student questions as physically possible. In the future I will definitely continue with other courses in this Specialisation.

por Loïc B

27 de ago. de 2019

A very good introduction to essential Python tools for manipulating data. I recommend taking this if you either know some Python but are new to data science, or if you have at least a basic grasp of how to manipulate data with other software. Users without prior knowledge in Python or data wrangling will find this course too hard.

Prof. Brooks is very clear, and the Jupyter notebook environment helps tremendously. I liked a lot the format of assignments as well, though meeting the requirements of the autograder can be tough sometimes... Another point on assignment: the version of pandas used for the course and the current updated version now differ a bit, so that some syntaxes may differ on a few functions.

por Benny P

19 de sep. de 2017

As others said, this course is fast paced, has only brief information in the videos, and has challenging programming tasks that requires students to get the required information elsewhere that was not given in the intros. Whether you like it or not depends on whether you are able to learn by yourself (with guidance on what to look for) or do you want to be fully nursed. For me, I LOVE IT! The material has enough information that I need, and I don't mind searching for references myself. The programming tasks are also challenging as it requires you to be really careful in reading the specs, and that is good. If you're not able to enjoy this course, maybe you need to take other introductory courses first.

por Paulo E N

10 de ago. de 2020

I really appreciated this course. The assignments are excellent, but they took me more time than the announced.

The ability to submit your assignments and have them automatically corrected, even if you are note paying for the certificate, is great.

I just think that maybe it is a "too hard" introduction. You must already know python, and, I'd say, should have already studied a little of pandas. The explanation of pandas is really quick, but full of valuable real world tips.

For the assignments you'll need a lot of pandas knowledge that isn't the videos, so prepare for a lot of searching in StackOverflow and in the docs. I believe it is purposeful, so the assignments mimics a real world problem.

por Karen Y

1 de dic. de 2016

This is a popular course series that many have expressed interest in taking. Rigorous and challenging course that offers excellent, high quality teaching of python pandas. The University of Michigan does not disappoint and neither does the delightful instructor Christopher Brooks. I highly recommend this course to anyone serious about python and data manipulation. Time and money worth spent. Interesting assignments and datasets are found each week. You will learn a great deal. Concise videos with sharp insights from an expert on pandas are seen throughout. Once you finish the first course of the series, it leaves you excited for the second course in the series. Rock on "pandorable" pandistas!

por Deleted A

21 de ene. de 2017

This was overall an excellent course, I very much appreciate everyone who has made this happen. However, the very last question of the very last assignment I found to be substantially more difficult than everything else, by a very large degree. Because of that one question I ended up moving my session twice and nearly dropped the course. is what finally got me over the hump. The instructions were not very clear to me but the price ratio calculation was the key to success. My guess is that missed it somewhere. Anyway, thanks! I will be moving on to the next course.

por Sabyasachi M

3 de may. de 2020

This is a very good course about the basics of data science and how python can be used to facilitate data cleaning and handling. I am a beginner with very limited knowledge of python (I had read some basics). The course takes you step by step through the use of python libraries and commands mostly used in data science. I would like to point out here that the assignments post course completion were a bit challenging for me as I am a beginner, which is good. This is because I had to research and learn a lot of stuff to complete the assignments apart from the course material. Looking forward to more such courses and assignments. Kudos to the teaching staff and Coursera team :)

por Tanmaya S

29 de jul. de 2020

Excellent course that throws you to deep-end

Good explanation of basic concepts and learning through challenging problems. This course really pushes you to utilise open resources and refer to forums and standard text which, in my opinion, helps learner utilize full potential of MOOC's. Excellent course for understanding applications of python, but be ready to scavenge forums and refer to the documentation for hours to solve assignment problems.

It would have been really awesome if some more exhaustive guide regarding fundamentals was also provided which could improve understanding of functions applied in assignments even more

por Vijay P R

27 de nov. de 2016

The programming assignments are challenging ( atleast for beginners),with each question taking about 3 hours to complete .Many topics in Pandas are covered , making us reading the docs and finding solutions,that further helps in learning . Excellent course , good support from other learners taking the course and very very informative . No other platform can give us a course ( and knowledge ) of this standard . Planning to take more courses from courseera . However a small feedback : The description for some questions are slightly confusing . Please make such questions more descriptive with examples .

por Aditya S

27 de jun. de 2018

This is one of the best courses on coursera by offering, the instructor Christopher Brooks has a great ability to deliver a lot of information/knowledge in a concise manner! He is a great teacher. I really learned a lot from this course, and reading the course blogs like : science isn't broken, following the data skeptic podcast, joining in on discussions. The discussion forum has great methods by Sophie Green , the teaching assistant, with great stackoverflow links added. This course has a steep learning curve, but as much as it was tough, by and large it was worth every minute investing in it!

por Max P

16 de nov. de 2017

This is an excellent start to Python, showing the basics of lists, dictionaries, tuples, Pandas Series and DataFrames, and numpy. The lectures are concise and hit the right elements to get a quick grasp of Python. The assignments are sometimes with real-life data, which makes the course particularly engaging. During the assignments, the hands-on approach really helps a student grasp the details and delicacies of the different Python and Pandas objects. As an improvement, I would say that some of the text within the assignments could be expanded to nip any possible confusion in the bud.

por Deep S

28 de dic. de 2020

As I was looking for an advanced python programming course with an emphasis on data wrangling, this course fully met my expectations. Assignments and quizzes were challenging and quite close to real world analysis tasks. Videos were concise and to the point and that's what I wanted. I won't recommend this course for a beginner in python as well as for a beginner in data analysis. I think this course will be great if its content is supplemented with a brief refresher of fundamental concepts of some commonly used statistical testing such as hypothesis testing, Ttest, chi-square etc.

por gaurav s

1 de jun. de 2022

Awesome course for starting the journey with Data Science. It covers Regex, Numpy and Pandas in great detail.

Weekly assignments are so good that you dont even need extra practice as part of learning. These assignments will make sure you are well acquainted with frequently used things in Data cleaning /manipulation. Must do course for every person who wants to start the journey with Data Science. This course was suggested by my friend, who did this and other 4 courses as part of Applied Data Science course and was able to switch from being Mechanical Engineer to a Data Scientist.

por Feng H

9 de mar. de 2017

As a python newbie, I found this course challenging yet so much fun to learn. Dr. Brooks presented the lectures in a very organized way and made them easy to follow. If you have experience in R, you probably would pick up Pandas real quick. Students are expected to devote a lot of time into the assignments and try to find the answer on your own. But with all the great tips and clarifications from our diligent mentor Sophie Greene, it's definitely achievable.

Will take the other courses in this specialization and definitely recommend it to anyone who's interested in data analysis.

por Praveen R

16 de sep. de 2019

"Introduction to Data Science in Python" is very good introductory course for Python DataFrames/Series and related data interpretation methods. I got to learn to read in excel/cvs/text files and clean them and extract meaningful data. The final assignment was very informative into how applied DataScience work. Overall its an intermediate level course with ample coding to do and experiment. It is a very hands on course which is most essential to understand fundamental concepts clearly. I am happy I took the course. Looking forward for the next course on visualization.

por Ricardo A L

1 de dic. de 2018

Es un muy buen curso.

Lo que lamento que es que el Autograder es Todo o Nada y es imposible tener menos de 100 puntos. El codigo puede tener cosas buenas o no tan buenas, pero no todo esta mal.

No logre aprobarlo en la ultima Q6 pero en general es muy buen curso.

Quizas por el tiempo que uno dispone , puede ser poco para profundizar mejor el estudio. Yo trabajo en area TI de Retail y en estos dias de fin de año es dificil..

Muchas gracias a todos. Quienes preparan el material y a los instructores.

Un abrazo para todos...menos para el implacable AutoGrader..



por Hao Y

30 de ene. de 2021

If you go into the lab and play with different parameters of the functions you'll get a hold of what they do. Just watching the videos is not gonna help you learn. I think people give this course a bad name because they didn't really spend the time to actually play with the functions themselves. The assignments are challenging but the materials are all covered in the lectures. I don't understand why people say the assignments require more than he teaches. Sure they are tough and yes I did consult my notes and stackoverflow. Overall great learning materials.

por Roger S

22 de may. de 2020

This is an excellent Pandas bootcamp but be prepared that you have to invest more time into the Labs than in other Coursera courses. You should know some Python. I found the Python-Specialization from UMI a good basis. Some prior knowledge on working with data can be helpful.

After some introductory videos you have to find your own way for solving the Labs. I found this very realistic. Later nobody will ask you how many Python functions you know by heart but you will get tasks and you have to find a way to solve them with Google, Stackoverflow etc.

por Z S

27 de abr. de 2018

this is a challenging course if you are just coming out of the Python Intro specialization. Much self learning is required, however that is how most programming happens, so I think overall this is a very good course to partake it. I don't know if perhaps the questions could be worded more clearly, as much time was spent trying to understand them, and I had to resort to the discussion forums to clarify their intent. In any event, that might be also reflective of difficult demands in the business world, so I still give this course a 5 star grade.

por Marianne O

15 de jul. de 2018

This is an excellent course. The professor builds concepts very naturally, lectures well, and gives good examples. Most of all, the exercises are really designed to test comprehension and the final week's assignment is an example of a real world question using real world data that must be cleaned and interpreted to test a simple hypothesis and derive an answer. This course has made me feel like I have the tools I need to take on my own datasets. Even the optional reading/listening assignments in this course are interesting and thought provoking.

por thomas m

29 de oct. de 2017

Great introduction into pandas environment in Python.

First assignment was most difficult in my opinion. There were times i had no idea where to look but stackoverflow and the pandas documentation were great references, which once i understood how to better search and interpret, i was able to do what i wanted.

One thing i liked was there was ample struggle in this course. I've done other coursera courses and found that the exact problem statement and solution were posted online, which was hard to avoid when looking for more generalized help. I

por Leo C

15 de ene. de 2017

This was a very helpful course in getting comfortable with using the pandas library and different concepts in numpy in data analysis. The fact that the instructors and course materials do not give you 100% of the tools to complete the assignments is a plus. Every data analyst and programmer inevitably will have to rely on self-guidance.

This course by itself may not be immensely useful in the professional world, but lays a strong foundation for the student to focus more on plotting, analysis, and conceptual learning, rather than on code.

por Madhu

2 de may. de 2020

The course has sufficient rigor to prepare you for what is coming in the rest of the program. My opinion is based on my experience with the many Johns Hopkins Data Science courses I completed on Coursera.

The auto grading system can be improved. The feedback on failed submissions is sparse and you have to go to the discussion boards to figure out the solution.

Warning to students who tend to get trapped into figuring out a solution on their own:

PLEASE go to the discussion often when doing the assignments and you will save a lot of time!