This course is good if you already have some experience in Python and its structures, or if you have some knowledge in programming. You will learn some basic data manipulation and exploration techniques and also start with some of the model evaluation metrics in order to assess the (regression) models created. Overall good experience. If you already have some knowledge of Python SciKit Learn and Pandas, you could easily cram this course in 2 days (all-in) without too much sweat.

It has been a fantastic experience to have gone through this course materials. Although I found the lecture videos quite quick to the extent that we fail to understand the concept well. But while going through the labs carefully, I was able to get the concepts right. So only because the lab part was well organized, the course was helpful to me. But had it been the lectures alone, then it would have been difficult to grasp all the concepts clearly.

Great course! More hands on and practice, a bit lack of theories, compared with Andrew Ng's ML course. And there are a few typos or mismatch in the course materials that need more attention. However, I especially like the fact the example, i.e. predicting car price, has been revisit and further developed through the 5-week course. Just finished round 1, guess I need to go over it again (maybe again) to grasp more details. Recommend the course!

Overall the course is well written. There are a few typos including in the instructions for final assignment. I feel that a summary is missing for the overall data analysis process and methods. This course is the longest in the series so it takes a lot of effort to get through. I did not have much Python background so it was a bit challenging at the beginning but the material was very helpful in bringing me up to speed.

The course is good but sometimes the exercise texts are not very clear and some of the lessons are very straightforward, leaving many doubts. The course should have a larger series of exercises and an automatic correction system that facilitates the review of the exercises. In addition, it would be interesting to have a module on how to use IBMDB2 without the online platform, but through Jupyter on the computer.

This course was challenging. I will probably want to come back to it after learning a bit more statistics. But it was cool stuff, and at the right level of depth. (The only criticism I have is that there are some problems with the final assignment, a small discrepancy between the question in the notebook and the question on the assignment submission, and some other formatting issues on the submission form.)

It's a great course for beginners. A lot of topics are squeezed under this course. But, I wish the topics were a bit more elaborated and the number of videos increased. To back up the topics related to any calculations, actual algebra and statistics implementation should have been shown. Because of the confusions, tons of questions were arising during lab activity. Quizzes and lab activities were good.

Although this course comprises the most common techniques used for Data wrangling and basic modeling, it does not go any deeper into understanding the logic behind many of the subjects.

Perhaps, giving out some aditional lectures for every week lessons could be of good help to better understand this topics, so the learning process would not be just a "follow through" that just works for ideal scenarios.

The final project left out some higher cross-validation methods like Grid search and model comparison. Nevertheless, the course tried to cover a lot of useful and relevant examples of the whole process, as well as providing good practice opportunities. Personally, I would love to have more practice on each module so that I can turn the knowledge into my own. Overall, a well-designed course!

Week 1 until Week 4 the syllabus are okay & understandable, but when it reached week 5 it's another level, like suddenly jump from beginner level to advance level without detailed explanation. I have to ask people or search online to look for answer & further details to understand the whole concept. This reminds me of the movie series GOT... exciting beginning but terrible ending

Very good lectures, but the final project takes way longer to set up than to complete: finding the link to the final assignment and making it work in Watson took me too much time. There should be an option to do it outside Watson environment without loosing points because Watson is very slow. Moreover, the assignment and the link to the dataset should be posted more clearly.

The material are structured very well. The explanation in the video and lab tutorial really help to understand. The discussion forum is active and the teachers are responsive. You will also get a free certificate and IBM badge. Though there are some typos and errors and some things left unexplained, but overall it's good. Hope you guys can increase the course's performance.

This Course is extremely useful for quick learning of skills. This course takes you into world of data analytics at the same time giving you practical experience, unlike many other courses. All the topics in this course are up to the point and tell you its application rather boring you with details. If you are a beginner then this is a perfect course to begin with.

Well design for beginners with a scientific profile. The course starts moderately and covers a large amount of concepts. I advise to take notes and often to deepen certain concepts in dedicated tutorials on google or YouTube and other appropriate platforms. Cleaning mistakes on the slides and the notebooks will be great and make the learning experience more fluent.

Covers a lot of content very quickly with not enough opportunities to practice using and applying the code. Having lots of quizzes is good for testing passive knowledge, but more active hands-on application in labs would be most welcome. Useful content, but I am going to go take an intro to Python course so that I can actually follow and use what is presented here.

The course videos were excellent! The final project did a good job in covering the course material. However, the support to the course was unacceptable. I never got a response to any of my questions after posting them twice and waiting for a day. I then just decided to submit my project without waiting for a response since I felt my wait will be in vain.

This is a well organized class and consistent with the rest of the course series so far. One improvement could be to reinforce the concepts more such that we can create our own projects and decide what we need to do. At this point we're just performing methods for the class, but I don't yet feel comfortable starting my own project using these methods.

The overall modules were great. But a few comments, I think I am supposed to get more score for my final assignment. The reviewer did not grade me fairly, even though my answers were correct and matched the rubric. I do not seriously mind the issue. But to be honest, this is kinda annoying and your team should care about how to handle it.

Es un curso introductorio, realmente no es complejo, solo se trata de entender las bases del análisis de datos. Sí, es cierto que los videos y los laboratorios tiene algunos errores (que si has pagado por el curso no serían aceptables en ningún momento). Es básicamente una introducción para saber como se trabajan en el análisis de datos.

Some of the course skates over pretty difficult information really quick and then gives you challenges that haven't really been that well explained, so some self-research is required. The assignments are also pretty copy/paste + modify a couple of variable names so you have to put in the effort to really get good value out of the course.

The course is very interesting and concise, it has a very logical flow. The best parts about the course are quiz embedded in the lectures and detailed lab assignments. However, there are few errors in the lab and assignments, which need to be rectified. Otherwise, it would have been 5star from me. Thank You for desiging this course.

Easy to understand and grasp for a beginner. Good refresher for those who have some basics of programming down. Typos in the reference codes here and there but no major problems. Other than that the Watson interface is alright to work with however there will be some lagging some times. I enjoyed the process of learning this course.

Clear structure and message delivering. I have learnt a lot from this course within a short time. Teaching assistants answer questions in each weeke's forum also with good clarity and patience. Although some mistakes, cannnot obscure the splendor of the jade. :) Looking forward to a better version after the improvement on typos.

The course covers a lot of Python libraries and functions, but in practical exercises, all tasks are mainly aimed at copying author’s examples. I would like to better remember the syntax of the language. As an overview tour of Python, it turned out great, but I only remembered a small part of the names of methods and functions.

You can consider some more explanations on how the training and testing codes are linked together and what explicitly the Python codes does. This was the elements I struggled to understand. But this was the only part that also was new to me. All in all, the material was well explained and the course was very interesting.