Chevron Left
Вернуться к Introduction to Data Science in Python

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

Оценки: 25,667
Рецензии: 5,716

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


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.


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

Фильтр по:

3601–3625 из 5,668 отзывов о курсе Introduction to Data Science in Python

автор: madan m

16 сент. 2018 г.

This is an amazing course, I got to work on the real life problems which was complex indeed. Completing every assignment was not easy, passed them after several attempts. I would say one downside is that I had to spend a lot of time googling and on stack overflow. just to give a feel, an assignment tagged 4 hrs takes nearly 20 hrs to complete. Its a great course, be ready to sweat out !

автор: Jay S

3 апр. 2017 г.

This is a good course if you have had some experience with the Pandas module in Python prior to taking the course. Pandas is a very powerful module but it has a fairly significant learning curve. There are all sorts of free Pandas tutorials available on the web. I highly recommend familiarizing yourself with the basics of Pandas prior to taking this course or you will probably struggle.

автор: Raman K

5 июня 2020 г.

The course is designed in a good manner. I would prefer slightly more material in the videos. The exercises are good, they definitely took more time than assigned. A few times question in the assignment is not very clear (that need a bit of work). I definitely learn many new techniques by listening to the videos, changing the class room notebook and last but not the least assignment.

автор: Tim S

13 июня 2017 г.

Nice introduction to using Python, but lecture vignettes moved a bit too quickly. Assignments required far more than what was presented in lecture. If not for a particularly heroic mentor, assignments might not have been doable for some like me. On the whole, though, I am grateful for the course and that, at the time I took it, all materials and assignments were available to auditors.

автор: Shilpa S

22 мая 2020 г.

The course contents are well structured but the video lectures are not enough to master the assignments. More detailed explanations could have been included, since its really difficult for a person who is new to PANDAS. The assignments were challenging. Feed back for the assignments submitted were not found so useful. Although, the course contents provide enough space for learning.

автор: Kai P H

22 апр. 2019 г.

For me, this was a difficult course in which I learned a lot. I did not find the materials (videos etc.) provided in the course so helpful, but the assignments you get for your own programming are very close to real world problems and will give you real experience. So you will need some other material to learn, I recommend the book "Python Data Science Handbook" by Jake VanderPlas.

автор: Kedar J

27 июня 2018 г.

Unlike other courses the lectures are packed with new concepts so much that you can't miss even a minute to understand it. The assignments are fairly challenging. The only part frustrating was working with the grader. Often it won't work and you are left wondering what went wrong without a great explanation. Overall great first course in the series. Looking forward to the next one.

автор: Aibek C

2 мар. 2019 г.

It was a challenging course as a lot of things in the assignments you have to learn yourself. But it was the right way to do as during the work often times one will get stuck on something without any step-by-step directions on how to solve certain issues. The only thing I didn't like about the course is a bit unclear questions in the assignments which took some time to figure out.

автор: Hrituraj S

19 июля 2017 г.

Overall, I liked the course but there were some flaws (not too big ones but still worth mentioning) - The way things were explained seemed more like just giving information about something rather than explaining it well (of course at times!). Exercises were really very good as they promoted individual learning more than just depending on what was taught. Recommended for beginners/

автор: Pieter C

31 мая 2020 г.

This course was tough but I learnt a lot. I really wish that they posted the answers for how the lecturers would approach the questions (even if it was only after you passed the quiz. I walk away from these quizzes not always sure if I made a bad plan that works or if my solutions is good/elegant. This would be very useful especially for us who are early in our coding careers.

автор: Sarah H H

11 дек. 2018 г.

I really enjoyed this course, but i am so glad I came prepared and had completed other Data Science tracks on other online sites (Codecademy, Dataquest, DataCamp, etc). I had to put all the skills learned elsewhere to good use in this course. The course was challenging--which is why I wanted to take it! I feel like I had to problem-solve, code, and work with data at a high level.

автор: Aditi V

4 июня 2020 г.

The material is great, but a tad quick-paced. A little more detail into some sections could be helpful. My biggest grievance with it is that the notebooks and autograder use an extremely outdated version of Pandas and NumPy, which leads to a lot of the official documentation being unhelpful. You can fix the problem within the notebooks but the issue persists with the autograder.

автор: Ahmad M

21 февр. 2021 г.

Great professor to listen to and pretty good videos but some assignment questions super challenging and very different from the examples. First three quizzes were weird as they were mostly simple but took 24 hours to output grade. Other than these two complaints, good course to understand how data manipulation works and to learn more about Python's data specific libraries.

автор: Andrew K

18 февр. 2017 г.

This course has greatly helped my understanding of not only some python, but pandas as well. My only suggestion to anyone that wants to take this course is to make sure to allocate as much time as possible to it. They give estimates as to how long it should take you to complete a week's worth of material, but this can vary highly from person to person. Overall, I enjoyed!

автор: Janina d W

28 мая 2018 г.

Despite having some basic python and pandas skills, I have learned a lot with course. The assignments are challenging and you need to do a lot of self-study, but I find that it is a good way to learn. The only reasons I am not giving a five-star rating is that some of the questions in the assignments (especially week 4) are very unclear, and can definitely be improved.

автор: Swati s

9 авг. 2020 г.

The knowledge offered in this course is very useful and to the point. However, I found it quite fast , as the students enrolled in this course has no idea of data science so it is quite difficult for us to understand the material at such a pace. Apart from this , the quality of the material was excellent and helpful.

Looking forward to enroll myself in other courses too.

автор: Raunak T

2 мая 2020 г.

I think Hypothesis testing was not given a lot of time and was barely skimmed through. Another problem was the auto grader itself which was quite painful to work with. I think Coursera should have the latest version of packages on their server as without that there were always inconsistencies with the way my offline python responded vs how the online server responded.

автор: James T

29 нояб. 2018 г.

Excellent Intro course. Videos are short and to the point. Does require you to have familiarity with Python and to research on your own to complete the assignments. In that regard, it may be different from other online courses, but I did find the process helpful in gaining a better handle on Pandas data handling. Additional readings are also relevant and helpful.

автор: Nav K

12 июня 2020 г.

For a beginner I found this course quite fast paced. One must have a good understanding of pandas and numpy functionality for doing the assignments. The difficulty level of assignments is relatively high to the amount of content being covered in the course itself. I feel more videos should be added to understand the concepts upto the depth of questions being asked.

автор: José F Q

7 окт. 2020 г.

Very nice introductory course, I think it would have been good to include a brief introduction to visualization. But the statistics concepts and theory were very well explained plus the assignments were designed to apply all of the learned concepts, I would have liked to see more practical exercises done by the instructor as I didn't have a programming background.

автор: Jean K J

13 мар. 2018 г.

Assignments are really tough which will make you do a lot of research and self learning to come up with the desired answers. The pace of the lectures is too quick and hence the depth is lost at times. Will have to do additional research to understand the concepts clearly . Overall, a good course to understand the introductory concepts to the world of Data Science

автор: Christian L

25 окт. 2018 г.

Value of the course is mostly about personal research and work to complete weekly assignments (whose allocated time is vastly under-estimated). There are some useful examples in the videos but the course would benefit from developing a more explanatory teaching framework to understand and navigate Python. TAs were awesome to support and coach during assignments.

автор: Hugo S

26 мая 2018 г.

The class material was very good and the assignment improved the understanding.

The only negative is the graders for some of the assignment do not always provide useful feedback. Thankfully posts from Stephanie Greene (a TA) in the discussion Forum provided helpful automated testers that often provided useful clues on the specific answers the grader was expecting

автор: Shrinidhi V

1 июля 2020 г.

I found the assignments a bit challenging since I was a newcomer to the concept of exploratory data analysis. I would like the background of the lectures to be taken off and get them replaced with a white one in order to avoid any possible distractions. This was one thing I observed with my focus span after having taken multiple courses on Coursera. Thank you.

автор: Mark M

8 июня 2017 г.

I really like this approach of the specialization. Data wrangling with pandas is essential. Also referencing some controversial discussions reagarding the limits and problems we are facing as data scientists is appreciated.

However one star less as as there is no feedback on the submissions. So I really don't know what was good and how can I improve my codings.