Apr 20, 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.
May 06, 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.
创建者 Shernice J•
Mar 30, 2019
Instead of having a lab after each topic, this course one lab per week encompassing all of the topics. Some might find that better than having smaller labs but to me the information was assimilated better when i did a lab right after the topic. That being said, you can open the lab first and follow along with/after each video. You just need to be mindful of what works best for you. Taking time to understand the code is a must and some more documentation would be helpful. I wasn't a beginner with Python and it took some time and work out what was happening at times.
创建者 Peter A•
Oct 16, 2019
Too many mistakes in the lectures and the main lab. Confusing for new learners when the math is wrong or the python syntax is wrong. Anyone who rates this above 3 stars you are simply not paying attention to the myriad of mistakes.
创建者 Franco J•
Oct 17, 2019
Really intensive module. Be prepared to learn a lot here. You'll be diving into real stuffs that wil ask you to listen carefully and understand the matter. My advise is to take note of any points that you are not comfortable with and make additional research on google/youtube to become friendly with it.
One of the top module i've complete so far on Coursera. Full of usefull, meaningfull information and knowledge.
创建者 Oritseweyinmi H A•
Apr 23, 2020
Great course, that builds up and ties together some earlier parts of the DS certification. It helped me to understand the process behind developing a model and then later evaluating it. Solid introduction to analysing data with Python and I look forward to applying the skills I learn here in the applied capstone project!
创建者 Nora I•
Mar 12, 2019
Really great! Tons of information complemented with exercises. To deal with the amount of material, I suggest following the labs very closely and doing a bit of research in the Python documentation avalaible online. I have to say you really hit the core of the matter in this course!
创建者 Luis H•
Jun 04, 2019
I liked so much that I solve more short test because it helps me to remember information easily and guess it allowed me to perform better. It's the first time I get 100% three times in final tests of each week.
创建者 Cecil K•
Feb 07, 2020
The courses from IBM on Data Analysis, Visualization, Machine Learning are great. I am in an online M.S. program, and the material from IBM is lightyears ahead of my university material.
创建者 Opetunde A•
Jul 13, 2018
I have been looking for a very non-complicated course on data analysis and I hit the Jackport with this course! Very simplified and explanatory. You should definitely take the course
创建者 Prakash C•
Jun 06, 2019
Great course. I had fun having a kick start in the field of data in machine learning. I understood the concepts related to how to improve the model.
创建者 Siddhartha S•
Jun 04, 2019
Great Course. Amazing Work By the team. Concepts explained clearly, followed by the week end quiz to revise. The Labs Do a great work in helping out
创建者 Aldy P S•
May 07, 2020
helps me a lot! FYI I was new to data science and programming language but this course helps me to understand business analysis with Python!
创建者 Ted H•
Jun 06, 2019
Covers a lot of ground but the Python Labs are great at bringing everything together.
创建者 Paulo B M d S•
Jun 04, 2019
A very complete course of Data Analysis.
创建者 Mahmood H•
Mar 16, 2019
Tough but useful.
创建者 Vincent Z•
Mar 11, 2019
The course content is definitely interesting, but the approach is superficial. You will get a broad overview of the keyword to search for, and what is available in popular Python packages. However, the quizzes are way, way too easy. The course needs a final "open" assignment, where you have to use the tools without being guided along the way. This is the only way to truly learn.
创建者 Mahvash N•
Mar 05, 2019
Course was great but it had number of errors and typos, that per my experience and other attendees caused some confusion.
I am sharing so it could be improved as it is a dream come true for myself to gain this valuable knowledge as conveniently as possible.
Dec 19, 2018
I think few more practical exercises or at least references of the same would help better understand the overall fundamentals.
创建者 Rebecca V•
Mar 05, 2019
Material covered is useful, but there are a lot of typos and mistakes in the lecture slides and labs.
创建者 Rene P•
Mar 24, 2019
There could be links to functiones libraries in the lab for a fast check of a function if needed.
创建者 Devansh N•
May 05, 2020
Was a bit tough to keep up at the week 4 and week 5 but overall a very good course
创建者 Charles C•
Feb 05, 2019
Some mistakes/ typos in the exercises and slides, but great overall
创建者 Nigel A R H•
Mar 14, 2019
Quizzes are too easy. No evaluation of actual code.
创建者 Yogish T G•
Mar 30, 2019
An assignment should have been included
创建者 Niko J•
Apr 29, 2020
The course included a lot of very useful information. Thank you for that! Unfortunately it is also full of mistakes/misinformation. Every time I was about to report those errors, I found out that they have been already reported in the forum. And usually reporting had happened several months ago so that left me wondering how it can be that the mistakes are still there. So far I've been participating two other Python/AI courses by IBM and they were 5 starts. For this one, unfortunately 3 stars is best I can give with all the unfixed mistakes..
创建者 Miguel E M•
Apr 15, 2020
There where some typos in the labs that could confuse most learners. I didn't feel like the course prepared people for real applications. The final project was quite hard because of this .
But it does give you a wide vision on hoy pandas work and some basic but apparently often used tools.
I see this course as a complement to a more detailed data analysis resource or perhaps as simply as an introductory view.