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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
estrellas
25,652 calificaciones
5,715 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

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

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51 - 75 de 5,666 revisiones para Introduction to Data Science in Python

por Jun-Hoe L

9 de oct. de 2020

Decided to rate this course after I've gone through all 5 courses in the Speclisation. I originally completed this course in January 2020.

So from someone who has completed this Specialisation, I'd say this 5 courses are not worth it.

Here's how I would rank the courses from best to worst:

1. Social Network Analysis: 4.5 stars

2. Applied Machine Learning: 3.5-4 stars

3. Applied Text Mining: 3.5 stars

4. Intro to Data Science: 2

5. Plotting:1

Note that that worst courses are those handled by Professor Brooks himself. His video lectures tend to very superficial (or once in a while, unnecessarily detailed like going into the backend of matplotlib). The assignments on the other hand, are somewhat challenging and go way beyond the video lectures. And that's why you see many comments asking what's the point of purchasing this course when you spend 95% of the time googling? Which is made worse by the outdated autograder which uses and old panda version, and makes googling harder since you had to revert to outdated code.

My advice: Unless you really want the Specialisation cert, I think you should look elsewhere to learn pandas.

por Bart C

19 de ago. de 2018

This course provides very little instruction. I really like learning by trial and error, and I think that is how coding is typically learned. Learning python from stack_exchange, however, is how I was already learning it, and I was doing fine. The whole problem of learning from stack exchange is that you don't know if you are doing things in the best possible way, which can be important for big datasets. There was no discussion of the best practices for complete an assignment, after it was turned in, and, in general, may functions were required to pass the course that were never discussed in the course. The entire weeks lecture could also be watched in about 30 minutes, which seems low to me. Most courses I have taken have at least three hours a week of lecture. I have friends who have taken this same course, and had a similar assessment.

por Carl G

9 de abr. de 2018

Not my style of course. Lectures is a mostly just a list of code snippets without any slides. Instead there is a background of 2 people just staring at their screens the whole time. Does not inspire one to enjoy Data Science as a field. Prefer a narrative explaining why and how with practical tips thrown in. Learning to code is more than just syntax. Good examples are the first chapter in Think Stats by Allen Downey and Andrew Ng's Machine Learning course. In this course the assignments took quite a bit of time to complete since lecture code snippets not very useful. Had to self-learn from web to complete assignments. Also took extra time by some trial and error to get right format of results. A more productive approach was assignments in A

por Bas R

10 de feb. de 2020

Topics covered are interesting as next steps when you have some basic programming skills in Python. However, the introduction and explanation of new concepts feel very rushed; a one minute video on map(), then lambda with a quick exercise without further explanation, followed by list comprehension at the same pace. I often found myself stopping the videos and googling for further explanation to understand what is really going on. If instructors feel that such concepts should be familiar to someone participating in the course, then I'd recommend not covering them at all, rather than rapidly rushing through.

por Muhammad A

19 de abr. de 2020

I would not recommend this course at all. This is for a number of reasons.

The lectures are not really lectures, they are more of a narration of someone else writing code on screen, the intructor just whizzes through what's happening without giving any proper explanation (I cannot stress this enough). The limited explanation provided is just on what's happening on the screen rather than why we're doing it this way compared to any other way. There is also not enough guidance given in the lectures but told to just figure it out and go post on Stack Overflow. Anyone familiar with Stack Overflow should know, they *really* do not like beginners posting repetitive questions - so I find that advice from the instructor really odd.

The courses makes use of Numpy, but gives zero explanation on what Numpy is and why we use it. It just dives into it by using Numpy arrays and expects you to either magically understand it or go learn what/why Numpy, from someone else.

Speaking about assignments, a lot of the excercises require you to do something which hasn't been covered in the sessions at all. I understand giving a challene in assignments, but I would much rather prefer those challenges be related to things taught or from resources given / pointed to. But, unfortunately, you have to figure a lot out on your own and the videos are of no help.

It also doesn't help that the assignment feedback is very lacking. The grader also does not tell you what answer it expects, so you have no way of knowing how far off your answer is.

This is further not helped by the out-dated version of Pandas running (0.19.2). It has a 4 year old version. I tried to do the assignments locally, but then coming onto Coursera to find the methods I've used aren't supported. This causes further frustation with the "go learn on your own" approach, as every resource you'll find will be using methods/functions from the latest versions. You then have to spend hours more finding legacy methods for what you're trying to do (which, in practice, will be useless as you will always be working on updated packages)

In my opinion, this course is not worth the money. I would highly recommend you trial its contents before deciding whether to pay for it or not.

por Amir M O

9 de jun. de 2019

Wish I could give it zero star.

1- The lectures are extremely poor (read the most helpful reviews and you see that a lot of people share this opinion).

2- Assignments are super difficult and not related to the lectures.

3- Assuming that you manage to solve the questions, now you have to deal with their defective auto grader which is royal pain.

4- They insist on using Jupyter (in my opinion a really messy environment). I used PyCharm which is the default IDE for python nowadays but their auto grader caused me so much headache.

Overall, this course requires significant changes and more respect towards the students who spend a lot of time on it. For me personally, it killed my motivation for pursuing Data Science and taking more courses from this instructor.

por Rahul R

2 de feb. de 2020

This course is very difficult. This is first of all not a introductory course. The instructor teaches basic stuff but the assignments were look like mountain. It is quite impossible for a beginner to solve this type of assignment problems without having a very good background in python programming and data structure handling.

I should recommend, the instructor should revise the course content. Please bring balance between what you are teaching and what you are expecting from student.

After taking this course, I personally demotivated from taking further courses in this specialization.

*********** I will recommend going for IBM data science specialization.********

por Maria K

22 de nov. de 2020

Task formulations and goals explanation in assignments description are extremely bad, I've almost turned grey trying to understand what is the main purpose of exercise.

May be you should be able to read minds of Christopher Brooks or to be some sort of psychic to complete these assignments. :)

The only way to deal with this is to googling and searching for the answer in discussions forums, StackOverflow and Github.

But, unfortunately, I haven't find anything better on coursera for my current programming level.

P.S. If you are beginner in python programming (as me), I highly recommend you to try DataQuest, it is much more understandable.

por Marshall J V

25 de feb. de 2018

Would give this class a half star if I could. The material is covered way too fast and the assignments require knowledge of items not even mentioned in the class (let alone discussed). If you know the material well enough to get through this class, you don't need the class. The prof and TA refer to using Stack Overflow to figure it out early and often! Found this class to be a waste of time and money. I wanted to learn the material, but had to drop the class because I had no clue how to do the assignment after watching the lectures multiple times.

por Kyung H K

25 de feb. de 2018

I have no idea who rated this class five stars. The lectures do not prepare you for the assignments and the auto-grader will grade your answer as incorrect if you return a 17 dtype='float64' and they were expecting a 17 dtype='float'. Also, there's absolutely no feedback on your work except from the auto-grader, so there's no opportunity to go back and see a more elegant way of writing your code. I managed to get 90%+ for every assignment, but it was only because I spent over 10+ on the homework assignments for the last two weeks.

por Thileepan P

3 de abr. de 2018

This is definitely not an introductory course. This is more of an intermediate level course. The teachers explain complex techniques in one or two sentences. The notebook demonstration in the video lectures are also very fast.

There is a huge gap between the contents in the lectures and the assignment questions. These points should be kept in mind while choosing this course. I think, I will not take other courses in this specialization.

por Benjamin G H

13 de ago. de 2020

Serious issues with the assignment grading system will result in you pulling out your hair due to only getting credit for erroneous assignment grading system instead of learning.

Literally have to read the discussion forums to figure out how to replicate the errors the grading system is looking for. Huge waste of your time.

Wait atleast a year from this review to consider taking the course and pray they have finally edited it by then.

por David S

21 de dic. de 2019

This course is poorly organized, the instructor doesn't clear the most important basic concepts and pitfalls, instead just gives a brief through what can be done. The assignments are terrible, cannot state the problem clearly, didn't say anything about text files issues which causes submission problems, waist a lot of time on it.

por Dan D

15 de may. de 2018

This was the WORST course I have ever taken on Coursera. The final exercise questions were not specific enough and the autograder SUCKED ASS. I couldn't even refer to a column in my dataframe after I closed the browser 3x and rebooted my machine and it still did not work. This course is a WASTE OF TIME. MOVE ON!!!

por Saulet Y

13 de dic. de 2019

Very disappointed! The assignments are unclear. To complete the assignments, you need to google on each question especially in Week 2 and 3. If you go to "Forum" page, you could see that there are more than 1400 threads in Week 2,3, which means a lot of students ask questions. The course is really really bad!

por Nicole B

26 de sep. de 2020

This course is for knowing what you can do with python in Data science, definitely is not a course to learn python or for people like me who only had basic knowledge of python.

por Tural H

5 de mar. de 2020

Very fast pace, no clarity of the scope and poor leacturing

por Dale S

6 de mar. de 2020

The autograder wars made this a bad experience

por Wei L

16 de jul. de 2020

Introduction to searching Stackoverflow

por Zhenxun Z

11 de ene. de 2017

I really like Prof. Brooks's way of teaching. He developed a very good introductory level course. Apart from some talks about data science in a whole, he concentrated on the preparatory work in this field -- data cleaning. Instead of delving into theories, he paid most of his attention to how to make things work by using python. I actually have a background in C, and I was a bit reluctant to learn python at first since C is already strong enough to attack most tasks. However, I have fallen in love with python now, and I think it is a much more suitable language for daily use especially when your projects aren't very large. Among its many merits, the best thing about python is of course its numerous libraries like numpy and pandas which free us from tedious low-level programming. I am quite convinced that I will move to python from now on.

In addition to lectures, I truly recommend you go over extra reading materials. Those articles are very thought provoking. For example, the first one "50 Years of Data Science" totally changed my previous view towards this field. It made me realize that data science is not a simple combination of statistics and machine learning, that it is a distinct way of obtaining new knowledge, and that its advancement shall benefit the whole science society.

About the assignments, those taught in the lecture are not enough and you should refer to python documents and stack overflow. I think knowing how to solve problems and where to find help is more important than solving problems itself, and that's why I consider those assignments well designed.

Finally, thanks to all the efforts made by the teaching staff.

por Fabiano B

12 de ene. de 2019

If you are looking for in-depth theory, you may be looking at the wrong place. The videos skim through some fundamentals, and sometimes give you some valuable hints.

But if you are looking for a challenging experience that emulates the real world, this course is definitely for you. The assignments will throw you to the wolves very early. You will have to research way beyond the videos to finish them in a elegant manner. It also encourages you to code in a "pandorable" way, which is a valuable skill.

por Bruno S P P

14 de jul. de 2020

My background: Industrial Engineer with a decent programming background (including Python), but rusty with statistics.

My review: The instructors clearly know what they are talking about and explains useful concepts. However, the videos are very short, and some concepts feels rushed. The assignments are pretty challenging, which is a nice thing. The last one in particular is very nice and don't feel fabricated - you actually test an interesting hypothesis based on some data you have to extract and manipulate. To be able to finish the assigments, I had to use Google a lot. It kinda felt like cheating, but the course is pretty clear that you should look in the documentations and ask questions on Stack Overflow.

Suggestions:

Include more exercises to practice what was taught in the videos.

Include a solution for each assignment - some questions I got it right, but I am sure my answer was not the most efficient or "pandorable" one. It would be nice to have a benchmark to compare after we pass the assignemnts.

por Günter G

4 de ene. de 2021

This course is really tough, especially the assignments, which are never doable in the estimated 3 hours. That is very frustrating when one is experiencing this.

The course material is mainly a book and a few videos. I needed lots of hours studying on my own to tackle the assignments.

Now I got the certificate and when I look back I can say it was really a tough time but I learned a lot.

por Pragyan

21 de sep. de 2020

Overall the course is fine. Much of the work is left out to the user, which would be a good thing if the lectures actually spent time discussing a topic. The instructor picks up a topic and shows us one example and is done with it.

I was disappointed with the teaching style. That being said, I did learn a lot in this course. I learnt a lot of stuff, but I wasn't taught much. Some of the topics were really interesting but they are concluded in 5 minutes max.

I really wish the programming walkthrough were more comprehensive and not just "here's how you do this thing, let's move on".

The assignments are challenging, but are poorly worded. Half the time I had to figure out myself what the assignment was asking me to do.

por Mr. Q A

26 de dic. de 2020

The assignments took too long for me to complete .