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Отзывы учащихся о курсе Introduction to Data Science in Python от партнера Мичиганский университет

4.5
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Оценки: 25,652
Рецензии: 5,715

О курсе

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....

Лучшие рецензии

PK

9 мая 2020 г.

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

YY

28 сент. 2021 г.

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.

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101–125 из 5,665 отзывов о курсе Introduction to Data Science in Python

автор: Ran B R

29 нояб. 2020 г.

Lots of useful content, and a promising structure. But, the overall level of polish was distractingly low, especially in assignments (unclear & buggy)

автор: Erico L

1 мар. 2019 г.

I don't think I've learned much along the course. I had to pick a few concepts here and there, but I don't think that the way in which those are explained would stick.

Also, the course seems rushed: I'm not sure what the end game of these courses is, but I think it's an incredible wasted opportunity when it comes to MOOCs, as there could be more lengthy videos and more and better ungraded exercises (something that in this particular course do not exist) and much, much better explained assignments (I guess adding there the info from the forums by the teaching stuff would not hurt).

For being a course of intermediate level, the videos and explanations are too short; there are even places where things are left totally unexplained.

Even if it's supposed (and even encouraged) that the students seek information on their own, the lack of context in some places makes it rather difficult. this is specialy more so with the questions that are interwined in the videos, as normally in order to answer them corretly you have to go out and find the related info (something that totally disrupts watching the videos).

finally, the assignments are a wreckage; some of the questions are incredible difficult to understand, if not out right impossible. The fact that there's a lot of information added to the forums by the etaching stuff, up to the point that the more complicated questions are easily answered with that same infromation, proves this.

I do think there are examples of courses in Coursera: I recently completed "Mathematics for Machine Learning: Linear Algebra" and even thought I don't think it's not without its issues, I find it a much more challenging, entertaining and fun course, that covers in a good way its subject.

I have to commend the people from the teaching stuff that are in the forums, thought, as it's the only course in which I found people from the teaching area activelly participating, and helping the students.

автор: Zayd A

28 мая 2019 г.

I had done "Python for Everybody" from Charles Severance which I had found excellent, with the instructor being passionate and the pace being just about right. I had assumed it would be similar for "Introduction to Data Science in Python", but that wasn't case. The delivery of the course is at a very very fast pace, you don't even have time to stop and absorb the functions and methods that you are supposed to learn. The instructor and the research assistant will list the functions and methods one after the other without pausing. The assignment is then extremely hard with no resemblance to the material in the course (I couldn't do it even after having reviewed the videos). After holding on for the first 2 weeks (it's a very useful topic after all), I gave up and decided to learn from the "learning the Pandas library book", which is a very good summary of the main Pandas functions and methods (and which was recommended by Dr Christopher Brooks), and I was able to follow it very easily.

автор: Sanwal Y

14 мар. 2021 г.

This course is not very well structured. A lot of the things that are on the assignments/quizzes are relegated to readings in the books and never discussed in the videos. The book readings are overwhelming for a week worth and require at least 2 times more to finish than what is suggested in the course. That is assuming you want to run the code in the book and not just do a hacky job of just reading it and not understanding the code.

The instructor is fine and does well enough but the structure of this course needs to be reevaluated and the time allotment needs to be made by someone actually doing those readings/assignments and not just an idealized number that they expect unreasonably from their students.

There are better courses to start with your data science journey and this isn't the one to go to, in my opinion.

автор: William B

25 нояб. 2020 г.

Was not a fan of this course at all. The first assignment is completely on regex which I understand that it is an important topic, but that's a fairly advanced topic in data science so to have as the first assignment of the first course in this specialization seems a little ridiculous. Not a single question on the assignment was on numpy which we spent the vast majority of the week learning. I did not get much out of the other assignments either. Dr. Brooks is really not the best teacher. Very knowledgeable, but not good at relaying that knowledge to others in a clear manner. If I could go back a month I wouldn't have taken this course.

автор: Aaron B

19 мар. 2019 г.

Really appreciate this course. Got me started in Python, Pandas, and Jupyter. First week felt like magic. I am giving it a low score because the assignment questions were so ambiguous that it required constant resubmits an scouring the forums. The ratio of learning of course content to required Stack Overflow internet research was way off balance.

I learned a lot but was extremely frustrated and burned a lot of time it what I felt was all the wrong places.

Still grateful for this opportunity. I think the questions can be better explained and tightened up.

автор: charles

25 мая 2020 г.

The assignments are fine, they are pretty tedious at times, but it is this kind of situations that forces me to self taught myself. Something really bad about this course is the lectures. They assume we know everything, I wouldn't be able to follow if i haven't done python in data analysis before, g, so they go fast and doesn't explain how everything/every function works. But if they assume we know everything, there is no need for the lecture videos. Just give us the assignments and just ask us to look at stackoverflow. The videos are 90% useless.

автор: Daniel A

20 авг. 2018 г.

This is not really a course. 2h of lectures in total. I have been in longer one-day university lectures. You have to attend other courses in order to be able to complete the assignments because 90% of what they ask is not in the lectures. This is a compilation of exercises, not a course.

On the other hand, the assignments and exercises are OK, that's why I gave it 2 stars.

автор: Mahmoud F

4 мар. 2020 г.

the course speed is very highand assuming high level of knoweldg

автор: José C V

28 апр. 2021 г.

too fast .... needed to pause the video constantly

автор: Jeffrey D R

7 мая 2018 г.

Like many others, I give this course a high rating while lodging a minor complaint that there wasn't much instruction provided. The lectures were excellent, if brief; it's hard to imagine anyone having objections to the instructor. But in terms of teaching the material, it was a bit of a drive-by. The lectures show a few examples, while not explaining the syntax or the various parameters. You have to draw that out of web sites and cheat sheets. If you're not adept at doing that, proceed with caution here. In the end, I was worn out from the effort, but felt that I had gained a lot.

The assignments were challenging for me because this was my first hands-on experience with Python, much less with Pandas. I did not find Stack Overflow as helpful as the instructor suggested. Nor did I find much help in the forums, but that's not quite my style.

My bottom line is that the course was time well-spent, but it could easily have been a six-week course with a more deliberate pace through the various pandas mechanisms such as merging and grouping.

FWIW: My recommendation is to get to know Jupyter Notebook early and follow along with the lectures by opening the Week[x] files in the course download folder. You can pause the lecture while you go play with the code to make sure you understand it. Also, I recommend working with a local version of Jupyter and keep your files local. Otherwise, Jupyter loses connection to the kernel, and stops being able to save your work. The messages are disconcerting, and if you've worked yourself into a frenzy, they can cause panic and confusion. So do all the work on your machine and then upload the whole assignment when you are finished. You upload on the "Create a Submission" screen; it takes only a sec. You won't even have to worry about details like file paths; they'll be the same either way. Once you get the hang of Jupyter, you can settle into a work routine. Learn some of the keyboard shortcuts.

автор: Maria Z

29 нояб. 2020 г.

It's a really good course for those who start working with data, but I must warn you that for those who has a beginner level in programming that can be a tough one. I really like the approach when you are given the basics and algorithms but you have to investigate the topic yourself to solve tasks - it's the most effective way to learn something. However I understand why some people may not like it.

I would like to mention the forum support - all the questions are solved very-very quickly, thanks a lot to the teachers!

The thing I didn't really like was the last assignment - 4 Qs out of 5 are the same... So if you manage to solve Q1 - others just require some boring data preparation, I understand that it happes in real life, but why here, it only takes time and annoys you?

I would recommend this course for those who has already worked with Python and knows all the basic classes and structures. If not - it's better to take some introductory course (it will be useful anyway, better to start with the fundamentals) .

PS: I really don't understand the comments here of people wh0 complain that they had to go to stackoverflow or read documentation - that's what you do when you code

автор: Steven C S

6 авг. 2020 г.

This is a hard course. It takes much more time than what is listed. It is frustrating because you need to do a lot of work on Stackoverflow or other sources to find solutions to assignments. The lectures aren't lectures, just quick talks about what can be done with Pandas, scipy and numpy. That being said, the professor treats you like a grown-up professional, gives hard real world problems with dirty real world data and asks for you to come up with questions to problems. That being said when you're done you look back and think, darn that was hard but I can actually apply data cleaning with python/pandas to data you might have lying around. As Poe said, It was the best of times and the worst of times, I couldn't decide if I loved the teaching style or hated it, but all in all I can say I learned a lot, though I complained a lot along the way.

автор: Haikal Y

13 сент. 2020 г.

This course is really good for getting your feet wet in Data Science! Foundational Data Science theories & techniques were introduced by Prof Brooks. It would be good if you had some foundational knowledge in Python so you can better navigate the course! (In the older version of the course, they assumed you knew RegEx - Regular Expressions & other nifty tricks like strip & split, but I saw that they'll be covering these in the newer version of the course, so a good introduction if you didn't know about these topics!). The course gives you the basic foundations, most of which are necessary to solve the course, but there are some methods & expressions that you'd have to Google for yourself. Similar to a college course, there isn't much hand-holding but still doable. In doubt, ask in the Discussions! The TA's are helpful :)

автор: Zhengyi S

23 февр. 2020 г.

The contents of the course are concise and it fulfilled basic requirements for fundamental data manipulation. Specifically, the exercises are excellent as they are real problems, which has many untidy problems to overcome during the process, and it's such a pragmatic train on me. Two suggestions: 1 is to add the answers of the assignments, because even though students pass the assignments, there might be better codes to refer and learn; 2 is to strengthen the problem description, as there're several negligence in those assignments. Overall speaking, the course helped me sort out the basic manipulation about numpy and pandas systematically.

автор: Florian M

3 февр. 2019 г.

I did this course as a 2nd year CS student with limited exposure to Python before the course. I had a basic understanding of syntax and knew basic structures like Dicts., Lists, Tuples. It took me 30h to fully complete the course - I did it in 2 weeks. I would recommend the book 'Python for Data Analysis 2nd' as supplementary literature. The course material is very very limited, which is by no means a bad thing. It just requires you to find answers by yourself. I really enjoyed it personally and would recommend this course for anyone who is interested in Data Science! Just make sure you know your Python basics beforehand.

автор: zqin

26 мар. 2019 г.

Honestly, I didn't want to rate the 5 star while I was learning the course, because the assignments of this course was challenging and the course videos didn't talk too much about the coursework. But after I finished the course, I found I have already learned almost all of the knowledge of the book "Python for Data Analysis" by Wes McKinney, which is also the recommended book in the course. And I can do data analysis work with python right now. You might think why do I have to register a course and then learn by myself, but what if this is a good chance to push you out of the comfort zone?

автор: Mohammadmoein T

6 нояб. 2020 г.

This was indeed an amazing introduction to Data Science. I should accept that I found the assignments kind of challenging and had to spend lots of time on some of them, but that would only make you learn more. Also, a proper background with Python is required for this course. Make sure you have enough background with Python Data Structures. If not, I'd recommend the following course first:

Python Data Structures - Charles severance

Good luck on your journey!

автор: Sourav S

4 июня 2019 г.

The quality of the assignments is really good but the instructions for assignments is really poor.

I had to do read through the discussions to solve almost each and every problem. The assignments are really time consuming and challenging.

Also, I had to refer to stackoverflow for countless number of times to derive the logic.

The instructor has only touched upon the material but additional videos should be included by the TAs for the assignments.

Thanks,

Sourav

автор: Jens L

12 авг. 2018 г.

Excellent learning materials. Clear concise explanations, but with the focus and majority of time devoted to activity-based learning: exploring the docs, practicing skills, and developing solution code. Even better is how subsequent lessons not only build on previous skills, they actually help guide and refine approaches even further. Well orchestrated progression of zone of proximal development. Thanks for a great learning experience!

автор: Hamdy M E T

16 мар. 2020 г.

Great Course and Awesome Instructor. The course is very practical and hands-on. All assignments starts with messy data and leave it up to you to start cleaning and manipulating the data with some modeling objective in mind which is what a real data scientist typically do. Thanks for the course , it was a really cool experience ! I really enjoyed the course and it was a bit challenging sometimes!

автор: Oluwapelumi S

5 авг. 2020 г.

This course is really wonderful and tasking. You'll get to know the core foundations of Data Science and useful libraries Data Scientists use to manipulate data. The assignments are very thorough and deep. Many thanks also to all the teaching assistants who were available to help, especially to Sophie Greene and also to Yusuf Ertas. I look forward to completing the specialization!

автор: YIJUN F

8 мар. 2021 г.

Overall the course is great for people who want to begin with data science. The skills it incorporate are very useful. The only thing to improve is that we could be given more hints when doing assignments. Sometimes we are not familiar with what can be done with Pandas, so it took a lot of googling to complete the assignment.

автор: Sean C

29 июля 2019 г.

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

автор: Francis J A

24 окт. 2020 г.

Great introduction to applied data science. The weekly assignments are challenging and varied, and students are required some independent studying outside of the lessons. The forums are also quite helpful in approaching the assignments.