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

4.5
estrellas
25,938 calificaciones

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

YY

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.

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

Filtrar por:

4951 - 4975 de 5,724 revisiones para Introduction to Data Science in Python

por Artur I

20 de oct. de 2021

I learned A lot from this course, and I am grateful, for the effort. tutors and professors designed the course and set up autograder, delivered lectures so I appreciate all that. Having said this, though, very often end of week assignment questions are VERY ambiguous. I would say Not well communicated and very confusing. If one examines the forums for this course one could easily corroborate my notes . HOWEVER, that ambiguous nature of assignments made me work hard, search for answers, and try harder, for which I am also THANKFUL ). 3 stars is for the unclear formulation of the assignment questions.

por Robin L

18 de ago. de 2020

The assignments were interesting and challenging, but not nearly enough elementary examples were provided in the lecture videos. The in-video quizzes appeared immediately after a new concept was introduced and didn't leave enough time to think and process. For some assignments, it wasn't clear how much of my work should be based on the lecture and how much should be based on independent internet searching. I wish I had learned more about standard conventions for writing certain kinds of code given that the instructor discussed some ways being better than others but never gave sufficient examples.

por Rolf M

23 de abr. de 2021

A mixed bag, mostly because of the very poor programing assignments due to very poor task descriptions and monosyllabic answers from tutors when asking in the discussion forums. Wondering what could be the precise meaning of the exercises leads to hours of trial, error and frustration.

Besides this the content of the course is good to understand and execute data cleaning and prepare real world data from various sources for further processing like ML. Pandas is at the center here.

I wanted to take more courses from the Michigan Python series but I will not do this due to the issues mentioned.

por Andreas M

19 de jun. de 2020

I came to this course after finishing the 5-course series by "Dr Chuck" Python for Everybody and with no other Python or much else programming experience. It sounded that this is the optimal course to build on the Python for Eevrybody sequence. But it is a huge jump in difficulty, and for a learner like me the lectures way way too fast, and included a lot of specialised programming terms. Also the assignments are hard and often not very well formulated. I am not saying that this course can't be done when just coming out of the Python-for-Everybody series, but it is very demanding.

por Achal J

8 de may. de 2020

I believe that this course is one of that courses that make you realize what you are stepping into, My expectations were hurt because i thought that Data Science was more math involved and had awesome statistics involved, oh boy was i wrong. The course majorly deals with data cleansing and copying a lot of code from stack overflow. if your python is not good please don't take this course otherwise you will get frustrated. If you have planning on taking this course be prepared to be grilled this course will require perseverance.

And most importantly the lectures ,they don't help!

por Prashant S

28 de nov. de 2016

The instructor is warm and seems pretty interested.

Content is way too minimal though and it's not enough to prepare the student for the assessments.

A lot of searching is required in order to arrive at the solution which more often than not is not the most optimum one. Forums are only supported by fellow students and staff's involvement is next to zero in there. Even threads that deal with clarifying questions were not answered by the staff.

I understand that this way enforces community building and helping fellow students, but staff's participation would be highly appreciated.

por An D

6 de jun. de 2018

The material felt very brief. Felt like this was suppose to be a refresher course. The lecture videos are not very helpful in its delivery. I wished there were more visual aids to help me understand the lectures more. Most of the time, it's just the lecturer sitting there talking and some quick screens of the Jupyter codes. I walked away with a brief overall idea of the material instead of an in-depth understanding of the concept. The assignments were challenging and I felt like they were very helpful. Expect to spend a LOT of time researching for the assignments.

por MARIA F V M

28 de jun. de 2020

This course was challenging taking into account that I don't have a lot of experience in phyton. I'm not going to lie, i would have prefer that the videos and the lectures give us more tools to solve the challenging assigments. I have to confess, I spent a lot of hours solving these assigmments, firstly, they are not easy and secondly, the auto-grader doesn't give you a real feedback on which you can work to fix the code. The way I see things, the autograder needs to improve and the content of the course will be better if it is more related with the assigmments.

por Andrew I

24 de feb. de 2020

I learned a lot through this course, in particular searching the docs and skimming stackoverflow. It was very helpful. I do hope though, that the grader and materials will be updated in the future.

It caused me annoyance to battle with grader. It was not grading properly what in my offline jupyther-notebook runs just fine. I hope that this part of UX, or better - SX (student experience) will be mastered, so that students would concentrate on learning and not on trying to submit the assignment to the obsolete grader. Please, do solve this problem. It matters.

por Rakesh S

7 de oct. de 2018

Course material is good esp. the assignments force you to learn a lot more. However, the instruction is not comprehensive. A few assignments were also ambiguous. The support forum is quite good but it would have been much better 1) if instructors would cover key topics a bit more in detail, 2) Easy to find auto grader scripts so one can understand the error or provide a better feedback mechanism from autograder. I had a spelling error in the answer and it took me 4 hours to correct it. Once I had the autograder code, the bug was very easy to catch.

por Tam H

7 de ene. de 2018

I am somewhat satisfied as I did learn some python skills. I paid for this course because I wanted an efficient way to learn python programming. The far cheaper alternative is to get a python book and work through it yourself. I thought this would be a more time efficient alternative to self study. It is somewhat more time efficient. The questions in the assignments are not clear, this results in you spending a lot of time not learning python but figuring out the semantics of the question. There should be a knowledge check at the end of each week.

por Joe P

17 de ene. de 2017

I found this course useful since I had no previous experience of Pandas or the statistical features of Python. I have programming experience and feel I would have found it quite a challenge otherwise, but not prohibitively so. Chris is great at explaining things in an accessible manner, and I'm very much looking forward to going into more detail in the rest of the specialisation. I would have enjoyed a little more focus on statistics etc and a little less on the mechanics of the library, but understand why it had to be approached this way.

por Justin H

12 de ago. de 2022

F​or future tech or CS courses from U Mich, please follow Dr Charles Severance's approach in teaching. Make learning and wanting to learn truly enjoyable. Teach material that will be tested in assignments and not throw curve balls which lead students to google answers and 'stack overflow' it unnecessarily.

T​his course is not for beginners or for those that have just picked up programming.

T​he autograder system is a real pain in the butt. A lot of time is wasted just to conform and present the answer that the autograder will accept.

por Will D

13 de oct. de 2020

Great course material. Grading issues due to Python versions was a pain. The discussion forum may have been more helpful with smaller groups (18k posts in a given week is overwhelming!) Nice to have access to the larger group brain, but I bet having chats with cohorts of 10-100 people would make the course feel more intimate and engaging. Also, the pace of lectures felt like the prof was reading text off a screen rather than a "real" lecture with natural pauses, which made it difficult to follow along with code as the prof was speaking.

por Nadine R

19 de abr. de 2018

This is course is classified as intermediate and a 10h commitment per week. For me this was an under estimation. It took me much longer. The problem with the course for me was, that the skills required for the assignments were not taught during the lectures and the assignments are poorly described. I ended up googling for hours. The links in the forums are very helpful, but I usually prefer to solve assignments on my own, which was not not possible for me in this course. I did learn a lot, but this was very unefficient.

por Stacey C R

10 de feb. de 2018

Honestly, my opinion is that the material is "a little too difficult a little too early" .. not because an experienced programmers can't handle it, but because the urgency of getting the final assignment done forces a reversion back to more traditional programming techniques rather than instilling "Pandas like" programming techniques ... if anything .. "instructor solutions" should be given at the end of the course so we can go back and see "how we could have done it more elegantly" in the areas were are interested in.

por Keir M

10 de jun. de 2017

Not a bad course but would like to see more teaching of best practice solutions to some of the test and assignment questions. As most of the assignments require a lot of self-learning it would be nice to discover if our solutions are optimal or not. As it stands you can get a perfect score by writing for loops or other inefficient solution when there are quite possibly built-in pandas functions which could achieve the same thing more efficiently. Would like to learn more about pandas and best-practice techniques.

por Mike L

10 de jul. de 2019

The course materials is very practical. The lectures are very clear and self-contained. The only reason I gave 3 stars is that the homework takes too much time. I spent a lot of time digging into online forums to find out the nuts and bolts to finish the projects. Fortunately the teaching staffs are very helpful. The time spent for homework is too much for my preference. Maybe this is the way to learn this type of information. I don't know. Having said that, the materials and lecture qualities are great.

por Polina B

14 de ene. de 2017

I liked that the course was very assignment-oriented. It had a good structure and interesting additional readings. However, for people who are not familiar with pandas library it may be very challenging to pass assignments. This minimal guidance, I believe, results not in a better understanding, but in confused students writing bad code and spending hours not understanding online documentation. Overall, I really liked the idea and content of the course, but not how instructors approached the self-learning part.

por Michael C

23 de nov. de 2016

I have two general comments:

The first comment is . . . there was too wide a gap between the lecture content and the assignments. The second comment I have is . . . I spent too much time trying to figure out what the autograder wanted and not enough time learning Data Science with Python. I can only imagine the work that it takes to develop and launch a course like this. In all, I'm very excited to be part of this program. My comments are critical but hopefully helpful and all your work is appreciated.

por William J

26 de ene. de 2020

Course content was generally good although sometimes the lecturer brushes over topics that could do with more explanation. He may explain 10 things you can do in quick succession making it hard to remember all of the points. Exercises were good but there is a big jump on week 3 and 4 and relies on students to spend time themselves searching the web for solutions to the problems. Whilst it is good to be independent, asking for things that haven't been taught in the class can be hard for some.

por Jon-pierre H

26 de sep. de 2018

This course teaches some useful techniques, but suffers greatly in it's ability to teach you those things. The hw's do not contain enough information, and submission errors are very vague in terms of explaining what is actually wrong. During lectures, too much time is spent on the presenters face. They have presentation slides and code examples that do not get enough video time. It is not at all useful to talk about techniques without giving any textual info about it. It's not easy to follow

por Deleted A

8 de ene. de 2017

It was a good course where i learned about new and great tools and techniques. I learned how to approach the data science problems using Pandas and Numpy. This would not serve as a great course for into to data science. Background with Database Management and Python really helps. Overall i learn about new and great tools and would definitely require Documentation while using the skills i learnt in this course. Overall Great Job by Professor Brooks. Would love to take more courses by him.

por Konstantin M

5 de feb. de 2022

I have reasonable amount of experience in data analysis using R. I found this course to be fun and challenging. But I heavily relied on my previous experience. I imagine it would be very difficult for someone with just basic Python skills. Also, it feels like a large chunk of assignments was about data cleaning (yes, it’s important) while important pandas concepts weren’t covered in as much detail as I would’ve liked. The recommended book is really good, I’m using it as reference a lot.

por Charles W

26 de feb. de 2017

I enjoyed and found all of the lectures helpful, but lack of feedback after submitting assignments was a real problem, especially for the last assignment. A simple response of "this was answered incorrectly, points not awarded" isn't very constructive and was often frustrating. Bugs in being able to submit the assignment were also frustrating. I spent a good amount of time trying to fix my code, thinking it was incorrect, when in actuality the online submission was just not working.