O curso apresenta uma proposta interessante, no entanto, a ausência de legendas em português nos vídeos dificulta o entendimento. Além disso, alguns textos, como os presentes nas avaliações, possuem erros de tradução, o que também prejudica a compreensão das perguntas e respostas. Por fim, embora o curso seja anunciado como focado em inteligência artificial e desenvolvimento, percebi que o conteúdo é mais voltado para ciências de dados.

The course did not provide what I expected from a Python course for Data Science. The parts of Pandas and Numpy which are very important in Data Science should be more focused with better explanation and exercises! Moreover, there are so many quizzes that are duplicated with the ones already shown in the video lectures, so as some practical assignments. Anyway, it delivered some valuable information that could be helpful for learners.

Although I learned a lot because I am new to Python, the learning approach used in this IBM course is quite poor. The videos are rather boring, the labs are often packed with too much content, some of the lab content is out of place, and the modules end up feeling rushed. In my opinion, this course needs a massive overhaul to make it more interesting, more user friendly, and to iron out some of the frequent errors and typos.

This course was very difficult. I did not understand what I was learning most of the time as it lacked the practical applications to data analytics like the other modules. I felt the formulas of python were just presented, and I couldn't pause the screen to maybe copy the formulas on screen as it just went black. I will have to supplement this with outside resources to ensure that I fully understand the basics of Python.

I think the information provided is good, but the level is not for someone with no prior experience in Python. A lot of the content felt way too much to handle. I'll have to fill the gaps with other sources and complement it with a lot more practice in order to feel confident with this technology. It is a great overview of Python for working with Data and if this was the intended goal of the course, it is excellent.

While I learned a lot in this course, it could have been a more enjoyable process. The videos and labs basically had the same content. However, the labs are a better learning experience (can run code and see results) but come later, making them less valuable and boring. Please consider having the lab content be more unique, as it was in the previous course. Also, please update the materials as Watson Studio changes.

The videos and labs were helpful and the course does help one learn some Python fundamentals. However, I believe that the labs and quizzes could have been improved. More exercises within the lab environment would be beneficial to promote learning. The weak point of the course is that the tutorials for using IBM Cloud were weak. More information and walkthroughs (including videos) should have been provided.

Good content overall but weird level requirements; the course covers too many basics for somebody who already programs but also assumes a lot and therefore is not adapted to someone who has never programmed.

It relies on external platforms that will not always work.

Instructional and useful nevertheless, recommend if you're already familiar with data analysis (in R for instance) and want further knowledge.

An adequate python crash course. The level is good for complete beginners. People with computer science background will find this slow. Also, the labs and the lectures are completely redundant, they almost match 1:1, except that the labs often fix a few mistakes in the slides. Don't expect a great numpy intro: you will basically get array creation, indexing and slicing and basic vector/matrix operations.

I am new to python I used it for the first time in this course. I found the course to be well organized. I liked the labs and how they are organized and well written. I found the course to be hands-on which I needed. I did have some background using MATLAB and found that to be useful, however, there are some differences in the syntax and the data structures.

There are not many self-learning assignments

The videos are quite good though the pace is a bit fast. There is a disconnect between the labs and videos are concepts are introduced in the labs which are not covered in the videos. While the students are probably not expected to understand these advanced concepts and just perform the exercises in the labs, it does affect the learning experience. Suggestion is to try and make the labs friendlier.

I would have given this course a 4 star rating if they had created the final assignment which would have make some better sense in terms of what this course content and course it about.

The assignment had no concept which was explained and in the course and it gets very difficult to understand the assignment. Never been through such a poor one in my entire 15 years career. One of the worst course.

VERY HARD for someone with 0 background in programming. The video felt rushed and didn't explain in concept but rather throwing the subjects right at your face and you need to figure out a way to memorize and apply each of them on your own. Some lab exercises aren't even mentioned in the prerequisited videos... Overall not a fun experience for a beginner. But still managed to learn the basics.

Overall, the course is ok. The first half of the course is manageable especially for someone who has experience in other languages. But the second half felt crammed - it is more syntax heavy and with few drills here and there. Reading the materials will make you say 'wait, where did this syntax came from?'. I believe this can be improved by adding more drills in the 2nd half of the curriculum.

Even it is a nice course to start at Python focus on Data analysis, I hink that the influance in IBM Cloud is too big and take te initial push of the first 3 chapters. Also, if they want to put the focus on that cloud, a more extense tutorials will be needed in that course (mostly, I had to search th "know how" of many elements, wasting time).

Overall, it is a good introductory course to Python.

I wanted to rate this course more highly. It starts well with a general introduction to Python, which should be helpful for anyone who needs to learn the syntax. Unfortunately, the last week or so of content (related to APIs) is rushed and disorganized. Would recommend spending more time on how to build web scrapers, load/interpret data, etc. Maybe add another week of content to the course?

This is a great course to get your feet wet with the concepts presented. But, the "hands on" labs are 95%+ done for you...not much coding on your part. Take it for what it is, take decent notes, and save the labs for future reference.

Advice to Creators:

Go thru the course, ESPECIALLY THE LABS, and update instructions, coding, and screenshots. Some things have changed since you put this up.

There is a lot of in depth information and resources shared about the Python programming language, but I do not feel confident in my skills after taking this course. It is a good start, but there is not enough practice or hands on application of writing code. The course compromises mainly of watching videos and reading code. I don't know how to code anything after completing this course.

Some labs exercises were no explained thoroughly, including more advanced coding and some commands that were not covered a 100% when completing some of the labs. In my opinion, in certain instances, this course assumes that I had previous knowledge in Python, and some of the lesson and topics were explained in a fast pace manner, so I had to pause it many times to understand the code.

Warning for those whose native language is NOT English: These IBM Data Science courses are in DESPERATE need of review by a native English speaker. If English wasn't my first language, I can only imagine how much I would have struggled. It is pretty unbelievable that they expect people to pay money for courses that have so many many grammar, syntax, and audio transcription errors.

The course was well planned however the pace of commentary in the videos was most of the time very fast which resulted in lack of understanding . In addition the Hands - On Lab was not available for a few days causing delay in completion of the course. I suggest that instead of making a commentary in the background while displaying videos the instructors should present the lecture.

I am taking this course as a part of IBM Data Analyst Certificate. The program became suddenly and irrelevantly challenging with this course. Why do i need all these stuff? And the things are passed so quick that it is impossible to catch up with the speed. Doesn't tell properly why sth is done in order to do what. It does not teach the logic but rather it enforces memorization.

This is course is a primer to Python and some libraries used for working with data. It covers some of basics of what certain tools can do for you, but doesn't really cover how to think or plan to do things in Python. If you're already a programmer maybe this will be sufficient, but if you're new to programming it may not give enough of a foundation about how to think in python.

There are some errors. Additionally, I couldnt get some of this code to work on PyCharm. It isnt much help to learn code in jupyter notebook, if I cant figure out how to install these libraries and use this code in PyCharm/Python. ALso, that Watson lab about language translation seemed to be more complicated than it should have been. I dont think I was prepared for that.

The course in general was quite good, but it is full of typos in the labs and to be so much time online and still have so many errors speaks very bad of the service that IBM/Coursera have contracted to moderate the course.

On the other hand, the fact that the videos and the labs say the same thing makes either the video unnecessary or the first part of the lab useless.