Google needs to either change the course from ' Getting Started in Python' or lower the steep difficulty level of course that starts with when you finish the module of dictionary, tuples, and so on. The reason is that the lectures emphasize on the basics of data structure but the questions of the same lectures of much higher difficulty and doesn't even explain the relevant functions / techniques and such strategy leaves students like me to be frustrated & disappointed. So, please if the questions (especially the dictionary & tuples) ones bound to be difficult then share the relevant techniques in the lectures before prompting us to do the exercises.

Great courses for a beginner, but the labs are so difficult, you cannot finishi them based on those video and readings without any previous knowldge about Python

The materials and presentations are excellent, but I am not a fan of the busy work involved in the end of course PACE portfolio work. Professional presentation is certainly important, but you end up diluting the course by including so much of this material. Furthermore, having worked on many projects, I can tell you that the workflow process is so iterative that trying to "shoe horn" the process into a four letter acronym -- which is better than the six letter acronym in the previous specialization -- is both reductionistic and frankly artificial. My suggestions are as follows: remove this excess material and "re-factor" the PACE/portfolio material into one course, perhaps the capstone course. Otherwise, I learned quite a bit.

Too basic to "Advanced"

This course covers the essentials for data analytics well, introduction(short) to python basics and pandas.

Good amount of labs practice with data frames and the functions and methods that come with it, however, this course is not sufficient if you have little to none experience or knowledge of the python language.

I would highly recommend a full basic course on python for data science before proceeding any further in the certification because every other course in this program uses python extensively and you do not want to limit your understanding due to the technical aspect. Take your time, review the course material, take regular breaks to realign your mindset and most importantly - practice, practice, practice.

This was a very good course for people just starting out with Python. The instructor was well spoken and did a nice job explaining the topics. If you have never been exposed to any sort of computer programming language than this course may take you a little bit longer to get up to speed, but the resources provided should be enough to get you started.

I'm exited with this course, i learn python from scratch and pyhton libraries such as numpy and pandas are easy to learn. tutorial video are effictive, lab are so effictive, thanks a lot.

I really enjoyed this course. There was a wide range of things covered and even though I had no experience with Python at the start I now feel confident enough to do the basics.

Exceptionally well-designed course! I just loved it. I knew Python before but from this course, I learned the ways to communicate results. Lastly, The instructor was amazing.

Overall the course is pretty good. The python tutorials could be more interactive, include more tutorials and then gradually increasing the difficulty level.

videos where not that effective compared to the readings and there was too much to learn with less examples, it would be better if they provide more readings than links for further studies, because not everyone is going to make an effort to open the links and study.

Although I have not completed this course by now and am halfway done, I feel extremely disappointed with this advanced course. In this advanced course, course 1 is the intro, and courses 2 and 3 discuss fundamental Python analysis and visualization using numpy, pandas, matplotlib, and seaborn. By viewing content, courses 4 - 6 are advanced Python analysis, such as Introduction to Machine Learning and Regression Analysis. Let's start talking about courses 2 and 3. They throw you everything, and you must figure it out alone. It's just like you haven't learned Allegra yet, and they throw you calculus staff. How can you learn how to run if you don't know how to walk? It feels like impossible. If you have no previous Python experience, don't take this course! It will keep and keep confusing you. Let's have an example by looking at course content. In course 3, they taught you data analysis, visualization, and data cleaning afterward. This does not make any sense. If you aren't familiar with data analysis yet, data analysis should follow the steps in order(the steps might differ, but the structure is the same): Ask questions, data collect, data clean, analyze, and visualize. They don't teach in order, which is ridiculous, in my opinion. I learned content similar to this course about two years ago and recently re-picked it up for review. But for sure, if you have yet to gain experience with Python, don't take this course; find something better, I promise. At least, it confused me more.

In the Laboratory, there is some content you need to seek in the web to solve the exercices :-( In case you do the course in different days, don't forget to import pandas and Numpy again.

"I recently completed the 'Python Programming,' and it's undoubtedly a 5-star course! The content is expertly structured, starting with basics and progressing to advanced topics. The instructor's explanations are clear, even for coding newcomers. Hands-on exercises and great community support boosted my confidence. This course equipped me with a solid Python foundation for real-world projects. I highly recommend it to beginners and those wanting to deepen their skills. I'm thrilled with the experience, and it exceeded my expectations. A definite 5-star rating!" 🌟🌟🌟🌟🌟

This was a very good, no-frills turbo-mode boot camp for python. I already knew at least 80% of it, and it was good review and practice, plus I learned a couple new things along the way that I'm excited about, mostly list comprehension, plus good practice with numpy and pandas proficiency.

About the most interesting learning experience i have had. I love the course structure , its clarity and especially the content of its labs which took the concepts to another level of understanding.

I really enjoyed learning python. The for loops and list comprehensions were a little tricky, so I took a side course to help me understand those, but overall, it was fun and fantastic.

I get more detail information about python programming language and its hands on labs are perfect to know the concepts .

A best approach for data analysis and better foundational skills to learn new techniques with python

perfect starting point

Pretty good scope and nice exercises for entrenching what you learn. But you need to do a lot of self-learning through Python documentation and own research, which you could do without having to enroll for a Coursera course.

The title should have been: "Python: A Refresher".

theorical

Very unimpressed. This course could have been much more useful if they had focused on using python to address real world problems, rather than spending so much time teaching individual functions. It's like trying to learn Excel one function at a time rather than working on a problem and solving it. They did provide real world examples, but the way they did it was way too structured. This is not how real world problems are addressed. They should have had the instructor take us through several examples of how he used python to address real world problems. Watching him go through the learning would have been very instructional and very helpful. What we got was a bunch of lectures on the individual components of python - not helpful for beginners. These lectures would have been more helpful after we knew how to use python.

Too easy, assignments and test do not test. Everyone will pass this. Sometimes just outright wrong information. Even in an assignment the answer is outright wrong. E.g. it is wrong to claim that parantheses are used to create a tuple. Tuples in python are made by commas not by parantheses. You only use parantheses to border a tuple when needed. The course is totally basic. If this is all you need to be advanced in data analysis in python everyone is advanced in data analysis