学生对 IBM 提供的 使用 Python 进行数据分析 的评价和反馈

I liked the course. Highly recommended for someone who is looking for coding experience for data Analysis using python. Please practice the lab that will make you confident. Only thing which bothered me is the final assignment review. It was not correctly reviewed. I lost 4 marks. Hope this helps!

a few discrepancies here and there, please see the comments in the discussions. Other than that, very good! This course was more difficult than the others, and so i guess this is why employers prefer potential employees hold a PhD, or at least maintain a high algebraic/calculus/statistical aptitude

Actually one of the better courses in the Data Science program. Information is useful, test/quizzes are related to the material, and the final exam is appropriate difficulty to the material and information being applied. This course was challenging yet the tools needed to succeed were available.

The underlying basics of Data Analysis with Python were deeply conveyed. Simple examples and easy to operate commands were greatly described. I would suggest everyone take this course whether or not they know to code. It is always great fun to learn new concepts and Coursera makes it possible.

Some of the Regression-model and Plotting topics that were tested on the Peer-Graded Assignment should have gotten a little more time in the videos. The best solution to this would be to put the "Data Visualization with Python" course BEFORE this one, as it devotes more time to these topics.

Contained some simple grammatical errors, as well as some syntax typos in some of the modules. The most relevant thing I would criticize is the lack of depth with describing certain topics ion the modules as they can be very complex. I recommend studying the section notebooks thoroughly.

There is a lot to unpack in this course. If you have a statistics background, this may seem kind of trivial, but for the rest of us it is loaded with ways to view data. My only criticism would be that it sometimes skims across an advanced topic without really giving a general overview.

This course gives a very clear view of the tools used to find the best way to analyze data when looking for the best model to predict target values. The use of Jupyter Notebooks to run code for the data analysis is very useful and enables the student to experiment on his own for options.

I have start this course without knowing any Python code. I made it through but with a lot of rock with all the code. like a For loop or simple Python code. I suggest to study basic Python code then start this course but this course did push me a lot on Python code learning with Youtube

Only 1 thing i miss in this course : some extra reading material because especially the last subjects here are a bit tricky to comprehend in full. Presentation overall is great, the labs are really helpfull as they are packed with excercises AND extra info. So yeah : take this course!

This was a good course, but didn't have as much labwork as I would have liked. There were a lot of labs, but they were mostly already completed by the instructors - more of a read-along than actually doing work yourself. That said, it was a valuable course and don't regret taking it.

This was a very challenging course. i don’t think I had any business choosing an intermediate level course because I have no experience in Data Analytics so I am a beginner. It was very interesting to see how statistics and math concepts were applied though and I did learn a lot.

Great course to help us understand more about Python libraries. Just marked as 4 stars because I wish we had a better conclusion, showing us how to explain the charts and values to a meaningful insight for decision making. There could also be more dataset examples for training.

The course teaches an incredible amount of information in a relatively short time. The downside to this is that users don't get enough practice within the course on the data analysis methods and functions taught. Additionally, there are a lot of typos that need to be fixed.

I think the breadth of content in the course was a bit too wide. More modules, and Python content, focused on exploratory data analysis could've been expounded upon, instead of so quickly moving into predictive analytics. Nonetheless, I did gain value from the course.

This module is loaded with concepts. Even though they are introduced in a logical sequence, it gets a little overbearing and tend to lose the relevance in the context of car price prediction. At least, now I am aware of the techniques, methods and python's capability.

Good introductory course. Even though it is an introduction, the course would benefit a lot from including a bit more of theory, even as optional material. For example, including theory about ridge regression, instead of just mentioning how to implement it in Python.

It is a great course. The one thing I believe could be better, is to deepen the scope of the mathematical concepts. Indeed, it is a course that assumes knowledge in that area, but it would be great to include links to papers or articles that explain those concepts.

Content was decent. Do ungraded labs provided as practice exercises if you want much exposure and and free flow of code while using the data analysis libraries. Overall, the course is helpful for an intro and intermediate level. Will definitely work as a refresher.

I think that you missed more detailed explanations on how to analyze the results, especially for those of us who are not mathematicians or with advanced knowledge of statistics. But, is a fact that In the end it was the course i've enjoyed the most. This is awesome

Great notebooks and clear content except two points :

1 - polynomial regression and pipelines have not been enough thorough and detailed. Quite complicated to aprehend

2 - final assignment question 8 - nothing to do. answers were already in the downloaded notebook

The course is quite challenging for me as a beginner of using python to perform linear/non-linear model development. It is good in terms of the plenty of content for people to learn but it is quite hard also as it would be better to have more practice / examples.

This course teaches you important python liabraries like pandas, scikit-learn. It also provides information about regression and helps us to build a model for a given case. Overall its very nice course for getting idea about how to do Data Analysis using Python

I think this is a decent course that introduces data analysis on a basic level. The first 3 weeks were really well written, the last 2 weeks have some faults in them though, like values referred in the text which does not match with values written in the code

Very good introductory course on data analysis using Python! It is best for people who already had some level of analytics experiences before as it sometimes goes a little bit fast. But very good in general, covers a wide range of topic, with good exercises.