5 de mai de 2020
I started this course without any knowledge on Data Analysis with Python, and by the end of the course I was able to understand the basics of Data Analysis, usage of different libraries and functions.
19 de abr de 2019
perfect for beginner level. all the concepts with code and parameter wise have been explained excellently. overall best course in making anyone eager to learn from basics to handle advances with ease.
por Brett W•
17 de set de 2019
While the lecture material is well presented and certainly can be followed, the slides are littered with spelling mistakes, and many in important places (code that couldn't run as displayed.) Even the final assignment had formatting issues, and without the discussion forums suggesting removing the confidence interval, it was taking an excessively long time to run. These are generally minor issues that can be ignored, but as a mass, they are embarrassing at best.
por Samantha R•
7 de mar de 2019
The course content was relevant and quite useful. Its the structure of the course that I didnt like. These are the things that could be improved:
QA before sections are finished does not work - one should first go through the section then the mini QA should start
If one is paying for the course, the slides should be made available for download. Its nice to have reference material for afterward because one forgets things. Even more so if you pay to do a course
por Daniel Z•
14 de jul de 2020
Many typos, some code does not match text (e.g. text says test sample of 10% but code has test sample of 15%). Where there are questions embedded in the video they often interrupt a sentence which breaks up the flow of the material. Complicated concepts or uses of code are often mentioned very quickly and the related slide disappears from view too quickly.
My peer reviewed assignment was reviewed twice and both times scored incorrectly but in different ways!
por Lucas T H D•
2 de jun de 2020
Some of the instructions were not clear enough, with a couple of typos here and there. Alot of explanations can be given to the code, e.g. what is for what. Also, before the video quizzes, needs to let learners look at the screen, pause before flashing out the quiz. Overall, good experience. Aside from having some difficulties trying to understand some parts of the module, but able to pick up Data analysis thanks to the course.
por Liam M•
17 de jan de 2019
So far the other courses in the Data science specialisation contained a final graded assignment. I found them really useful. This course didnt. Also, instead of telling us about all the tools available in the libraries, maybe explaining why we would use them would be better. I could code these functions myself if I understood them, but just using a library seems like it could lead to laziness and a lack of understanding.
por Josep R C•
20 de mai de 2020
+Useful course for beginners. You get to learn basic concepts although these are not enough to get to work on real projects. Another good point is the set of useful libraries and methods presented in the course.
-Downsides of the course are the amount of mistakes found in the labs which are supposed to help understand the theory seen in the videos, but in some occasions can even mislead and mess the students up.
por Vimal O•
9 de nov de 2021
On overall IBM data science professional certificate track: Pros: Content is just good enough, instructors are good. Cons: IBM watson and the platform given to practise on is awful and has terrible performance and reliability issues, most often doesnt work and had an impact on my test deliverables. I personally overcame those issues to some extent with kaggle's and google colab jupyter notebook environments.
por Carsten K•
11 de mar de 2020
Great coverage of topic, but unfortunately comes with several imprecise (or even planely wrong) explanations in the videos. Video quality (style of presentation) is ok, but sometimes missing things are slightly missaligned or questions show up before the topic/sentence is finished - could use some polishing. The hands-on labs are great though - if the notebooks open or the servers are reachable.
por Felix S•
1 de jul de 2019
Material to learn data analysis was very good but had quite a few bugs. It was very annoying to review the assignment of a peer because it is not possible to zoom into the screenshot. Furthermore did I need to flag a person because he had copied screenshots and his notebook was empty or only with screenshots but I was still required to review a second person to complete the course.
por Jackson V•
5 de jun de 2019
Not as impressed with this course as the previous courses. My main complaints were:
-Seemed to be some gaps between the lectures and labs
-Some lectures seemed rushed through w/ simple questions, and did not prepare well for the lab
-Pre-written code in labs would produce errors
-Spelling mistakes (i.e. the week 5 "Quizz")
-No final project to conclude and summarize up our learning
por Chioma J E•
9 de abr de 2019
The course was not detailed enough. I think the instructor assumed that people taking the course would know a lot about Regression, Correlation and some other statistical functions, that it was hard to understand or follow at times. Maybe consider 'dumbing' down down the statistical functions so that newbies can also follow.
Overall interesting course. Thank you.
por Kam S H•
23 de jan de 2021
First 2 weeks were fine for beginners, but after week 3 where all new different syntax and concepts like seaborn, visualization, Regression models etc etc were thrown in, it got way too advanced for beginners especially when there insufficient and effective practices available to hone the knowledge. Have to spent most of the time self-learning on other websites.
por Nikhil B•
25 de fev de 2019
This is an excellent course for beginners in the data analysis and data science fields as it explains deep technical concepts in layman terms along with the Python code for the same. However, not a perfect course for someone wanting to go into conceptual depth or wanting to expand their knowledge of analysis in Python beyond use of standard packages.
por Fares A G•
18 de mar de 2020
Needs to rely less on the cognitive class platform, just host the ipynb files externally as the labs are inaccessible alot of the time. Course only covers regression models, I would've liked to see SVM, KNN and other algorithms. However the course excels in explaining the relevant maths related to regression and regression evaluation
por Mbongeni N M•
9 de set de 2018
It was educational, but when you pass a quiz, there should be an option to get answers to the questions you got wrong. And the practice exercises were filled with mistakes, particularly week 5. And the instructor was not responding to students' questions for week 5, which was one of the most challenging weeks. That was annoying.
por Yariv Z•
23 de mai de 2020
A lot of un addresses subjects. Many mistakes both in the videos and in the labs.
Overall after viewing all the videos again and summarizing for my self everything, I felt a lot better with the material but I think the course is not organized. I also think that it should get into some mathematical subjects more thoroughly.
por Brisa A•
28 de jun de 2019
A lot of errors make the course confusing. Also, the assigments and labs are "too easy"... it is clearly shown in the videos that there is much more to be done, but the course only demands you do about 50% of what is taught. How are we supposed to really learn without practice?? Give us real and demanding projects!
por Antonio P•
5 de mar de 2019
The content was good, but there were numerous mistakes and inconsistencies (i.e. a chart would show a red line as a training set but the write-up would say the red line was a testing set). Also, I would have preferred to have shorter and more lab activities. The lab activities were too few and each was too long.
por Vyacheslav I•
15 de nov de 2019
Grammatical mistakes, low quality videos, low quality slides and videos. Labs are okay, though no in-depth clarifications and explanations are given. Like "to do this you write this". Options? Explanations? What for? It's too much. Just remember how we wrote these lines and copy-paste them in you code later.
por Hemanth S•
4 de mai de 2020
Course is a bit too short and way too fast paced for what it is trying to convey! Of course people will be able to complete the course without problems but, have to re-visit and brush knowledge on these a lot more. Anyways, it is a bit of confidence booster. You feel like you learnt a new course.
por Rakshita S•
26 de jul de 2020
The reason I am giving a three to this course because compared to rest it was a bit fast-paced. Also, I feel we need a prerequisite of statistics before starting this course which was not mentioned anywhere.
Guess it is time for a lot of practice. Wish there were more assignments as well.
por Fernando M M E•
23 de out de 2021
I am doing this course as part of the IBM Data Analyst Certificate and even it was the 7th course I take I don't feel it was well explained. The videos pass very fast and the explanations are insufficient to understand what happen in the labs. I think there is place for improvement.
por Sisir K•
15 de fev de 2019
Highly technical and complex in nature. Difficult for people just starting out with data science. The hands-on labs are more useful than the videos themselves. The quizzes in between videos felt a bit too easy and mostly comprised of examples (as questions) in the videos themselves.
por Jingyi Y•
16 de abr de 2022
The final assignment is terrible. I've spent a long time setting up the environment because the online notebook is not available. And some questions are hard to find what they are really aimming for. And instruction is actually bad, at least compared to the course.
por Raghav N•
14 de set de 2018
This course is definitely very helpful to people who are passionate about Data science and have basic to intermediate understanding of Python but this course can be much better if it includes coding assignments rather than quiz submission. It was a great experience.