The course is overall nice, but at some points in hand-on lab the exercises jumps to new functions which has not been discussed previously. Furthermore, there is a bit of repetition in between videos, reading material, and hands-on lab. In between the repetitions there are some new points, which is good, but at the same point you might skip them thinking you have already went through it. I feel it should have more exercises with gradual difficulty curve. These are my concerns, but the course is really nice to go through the basic concepts

I really enjoyed the subject matter in this course. Sometimes the information presented in the videos is presented in a rapid fire manner, which makes it challenging to follow the line of thought (I'm a complete beginner to computer science/data science). Also, I (along with other students) experienced some frustration with labs not working/loading. However, when trying again a day or 2 later, it seemed to work without any glitches. The final project was enjoyable, but I wish there had been a lab similar to the dashboard project.

The course does a good job with teaching you the general basics of Python. Unfortunately, it doesn't go into details as I had hoped. Moreover, I was disappointed that the final project relies heavily on the lessons learned near the end of the course. Nevertheless, it is a good course since it can be quite challenging for beginner programmers. For instance, you need to retain all the previous knowledge you obtained each lessons. Also, there isn't only one way to find a solution as you will learn in the final project.

Functions and Classes lab questions were slightly harder than the rest of the programming labs. It would have been so much better if every time I was running the test code to see if I had built the appropriate function/class, what was the output. Not being able to see the output I didn't know what I had to change on my code to make it right so it took me a while to go through these labs.

Also I had some issues with the two IBM's APIs. I couldn't make them run, I'm not sure why.

However, overall it is a great course!

This course is pretty much an overview/an introduction to Python. I feel overwhelmed with new concepts and content in week 3 & 4. For assignments and tests, you just need to know that instructors will write complex codes/functions for you and you are tested only on simpler codes at this stage. I found the homework submission on week 5 very interesting and helpful for me to know the implication of using python and libraries in real life. Also, you will discover new solutions to the problems from the peer rating.

Excellent for those who doesn't know even basic of python, Thanks to the instructor who make it very easy using a beautiful presentation. Nice and very interactive labs consisting more Gifs that make coding really enjoyable.I never feel like I studying something but I feel I just playing with the codes. However I deduced one star from it due to there is lack of information available in-order to create the dashboard in IBM cloud.Overall course is really good with an awesome case study as assignment at the end.

The first 4 weeks are fine.. But last week, no proper explanation is given for the "Hands on lab".. Few things are explained in the videos but while doing Hands On lab, we see that such concepts are not explained in the video.. So, the final week is really not up to the mark.. Instead of APIs, they could have covered some other topic or if APIs were so important, then they should have clearly explained each and every line in the "Hands on lab".

Other wise, the first four weeks are somewhat ok ok..

4 stars, because in general the course is well structured(as well as specialisation), packs you with all the necessary skills to strat Data Science journey...BUT the labs! They are in Jupyter Notebooks, which is good, but the labs have a lot of code snippets, which a beginner-level student doesn't need to see. Not to mention certain coding moments are outdated. My suggestion-provide a student with simpler, but with such tasks, which are possible to recreate in any Jupyter Notebook from scratch.

Some modules seemed disjointed and this shows in the Lab sessions where the difficulty levels varied (some were for beginners - as the course intended- whereas others were more intermediate). For the intermediate level Lab sessions, they were sometimes discouraging as it could make programming novices such as myself find the exercises too difficult. Otherwise, a great overview of Python for data science. Students are encouraged to already have some prior Python knowledge beforehand

Although I already knew basics of Python, the tasks related to data analysis were cool and engaging. The only thing that I really did not like were time estimates for labs. I mean, have anyone verified that? For example, it was stated that the lab should take around 15 minutes. Maybe 15 minutes it is when you DON'T READ anything and just blindly hit shift+enter. For me, to analyse those labs, make notes, check the code, it took a couple of hours. And it was a good thing, though.

The final task using IBM Watson Cloud was very confusing to me so that it take me almost 5 hours to solve the problem. The dealing with Python in the task was not a big deal: the real problem was IBM Cloud setup for hosting my Dashboard as .html and this was not the most important part of a Python course. All the rest of the course was quite useful and helpful. You team could make more intuitive this part of hosting to make the course the great course of Python it can be.

Overall, the content is well-illustrated with full of hands-on labs. It's easy to be understood by students who are new to Python programming. But the coding format in lab notebooks is not good. For example, there are many things like "df =pd.read_csv()", where there should be spaces at both left and right. Also, I expect in the function part, there should be some contents regarding lambda function. And there should be list comprehensive which I think is very useful.

This course provided a very helpful summary of basic Python concepts. For a beginner, I would imagine that the lecturer went too fast. I had to pause and rewind multiple times per section, just to process what was being said.

The labs (in Jupyter notebooks) were by far the highlight of this course, with lots of practical examples. As many people have commented, the final project is a bit tricky and has less to do with Python than with understanding IBM data storage.

As a 5 week course it lacked content and detailing, specially introducing python and pandas. Most people including me already have a working knowledge of python so it doesn't matter but the course should have been designed to include all learners in mind.

The course also forces the IBM Watson platform to the learners, I enjoyed it but felt that the gradable assignments should be allowed to be submitted in an alternate format like uploading a .IPYNB notebook

This is one of the better courses of the set. The videos actually go into a lot of detail on Python that even a person with absolutely no programming experience should be able to get. I took away a star as I feel as the Pandas portion needs a lot more coverage. Pandas was only briefly skimmed through, and it did not get into merging, which is one reason why you want to use Pandas over a Dictionary. Furthermore, the Pandas portion did not have any labs.

Explanation and guidance are very detailed - not much chance to go wrong.

I completed the 5 week course in 3 days partly because it was easy to follow, partly because I could work on it most of my day and partly because the course is pitched at someone who has no knowledge of data structures and programming functions. I know those but are new to Python. I would have liked an additional module explaining how to set up my own Python environment.

Everything about this course is great, and there were some minor issues within labs. What I mean is that you'll still need to go to forums to find the issue you're facing like "wget command not found," somewhere along those lines, the solutions offered from the forums should've been featured in the either the videos or a part of the reading in the Reading files with Open Lab. Other than that, I can't think of any other flaws from this course.

Overall, the course is very good. Improvements could include- A primer on what Python is, what it is used for, etc. It assumes far too much of the beginner. The videos were unhelpful since they increase the total length of the course and are redundant to the labs containing the full script that is being read by the AI. I'd rather have a proper lecture, followed by a lab, where the information is explained in ways that are more approachable.

Course material in video is basic but some labs are surprisingly difficult, I have gave up on some labs in week3 and 4. I had no idea about the 'moving average' but to google it, this makes understanding the code of computing 'moving average' very difficult. So as the lab of NBA api, much love of NBA though, I had to drop it and move on with the course. I think I will come back for these labs when I have in depth knowledge of the subject.

Solid introductory course. I liked the compartmentalized approach to each of the course segments. They were manageable and easy to digest.

I found that there were some minor quirks (i.e. spelling mistakes and inconsistencies) in the short videos. I had some issues with some of the labs... it seems like some parts were editted/updated incompletely; ultimately, I was able to figure things out for myself as the labs were intuitive.

Gives a general introduction to Python. Including how functions, classes and different types of objects interact. Easy to follow if you have a elementary coding experience in other languages. Would be nice if it explains how to use and read python documentation to facilitate learning and self-suficiency. Practice exercises could also be a bit more challenging to push the student beyond replicating code they have previously seen.

Really good for what it is. But this course assumes the learner is already familiar with coding. I was. But others in my study group for the Data Analyst certifikate were not, and jumping from course 1-3 that starts from assuming the learner has never touched a computer or Excel into python programming that skips any "hello world" or basic intro is a giant leap, that I'm not sure all learners are comfortable to take.

The course allows beginners like me gain basic concept for coding in python. The discussion forums is really useful for me to interact with other students, even get help from teaching staff. However, I think the guideline for the final project can be better so that students are able to understand quickly. Anyway, I really thank for all course staff, peers to help me in this python road! Thank you very much!

Bom para aprender os básicos de python, mas um dos mdóulos, onde pedia em parte uma ferramenta da IBM cloud não funcionava, o site não deixava criar uma conta gratuíta, fazendo log out se cancelasse a compra.

Dependendo do tempo que despende, pode fazer em bem menos que as 5 semanas. Exame final é bem mais dificil que os testes de cada semana, por isso vale apena decorar e compreender muitas das funções

I am completely satisfied with the course provided by IBM in coordination with Coursera. I love to do the assignments and to answer the quizzes that will quickly assess ourselves just finishing a topic.I highly recommend this course which will cover all the essential python for the one who intends to start hos career in Data Science and in AI.Looking forward to do such great courses offered by coursera.