The course moves really fast. You get a bunch of videos that are less than a minute long, with a few 2 minute ones in there. You can blow through all of the video content in a module in a few minutes. Then you get a quiz which is only difficult because you've only had ~4-5 minutes of time spent with the material (unless you watched the videos several times). But you'll pass the quiz without too much difficulty because you can either rewatch videos to get the answers or just retake it until you pass. I don't find that experience to be that useful.

The programming assignments are frustrating because they are harder than any of the examples given and require you to know the material well enough to apply it to something else. That's quite difficult because you have to use information that got less than a minute of attention in the course. If you ever had a horrible experience in a math classroom because a teacher did one easy example on the board and then expected you to do far harder problems on the exam without really explaining how or why any of it works, you may find this course to feel like that. I don't personally like this format for learning. I also found the directions to be ambiguous in a few cases.

The assignments also seem to be a bit buggy. Putting aside the usual issues with Jupyter Notebooks that people run into (not running cells in the correct order, etc.), I find that I had tremendous difficulty with a few of the assignments and those issues were not with my code. I would run the cells and have my answers get rejected. After an hour of pulling out hair wondering how I was wrong, I'd walk away. A few hours later, I'd open up the notebook again, run the EXACT same code, and the answers would be accepted. There is nothing more annoying than having a correct answer get rejected and wasting hours wondering why it was wrong. This doesn't seem to be an isolated experience--there are tons of people in the discussion forums posting about how their answers weren't accepted and they don't understand why, with the course staff replying that the answers were accepted.

I don't want to be unfairly negative. The production quality is good and the staff are reponsive in the forums. I just think the course is weirdly hard for an introduction. It's one of those introductory courses that sort of only works well if you already know the material already. But then, why take the introductory course? The course would be a lot better if they spaced it out and went slower. I think the videos need to be longer with more examples. That would allow people who don't "just get it" to not feel overwhelmed with the large amount of information while also allowing experienced people to just skip over that stuff if they don't need it.

The course is very short. I expected more . Out of all the courses I have taken in coursera, this looked weak. The questions and instructions are ambiguous, could be more professional.

The lecture delivery is very good.

Course was very good to learn pre-processing and visualization and also gives good practice. The questions in the homework exercises could be more clear as in what they are expecting as the output. The way it has been put out makes few exercises confusing for us to understand in order to solve them. Response by the teaching staff on the forum can be quicker, sometimes it's frustrating for the coder if they are stuck and we don't get response atleast in 12 hours. Right now it goes beyond 24 hours.

This course is way to skinny on the lecture-side of things and way too heavy on the homework-side.

I would not recommend it for people older than 18

Good course, it gives you the basic info to pandas, numpy and matplotlib. It teaches you how to obtain dataframes, join, filter, group, summarize and visualize data. Short course but really worth it.

Excellent course. Assignments /home work explaination need to be rethought. Special thanks to Jahnavi for helping through out the course.

Units Tests in the assignements are a bit buggy but they do give scope to research. Would be nice if the questions then are to the point.

It was really a great learning experience. I joined this course thinking of will get an overview of Python. But unfortunately it was Data Analysis Using Python. So first of all you need to have basic idea of Python before getting into this course.

Initially I thought that It is not for me I have enrolled in a wrong course. But later I decided to go through this course, and started learning Python basics in parallelly.

Thanks

this course was perfect for me, I learned alot from it.

I want to thank Jahnavi Chowdary quck reply and you helping me through out, you also deserve five stars

Excellent course and wonderful support from TAs. Appreciate the support.

Comprehensive review of how to uploade csv and xls files in jupyter notebook and processing with python and pandas. Then why 4 out of 5 stars? Because the questions in the assignments are not always really clear. But here again the upside: the supporting tutors are very responsive !

Great course for the beginners, would like to thank faculty and staff for making a great course, and the discussion forms were really helpful, Thanks to TAs for reverting back to all the questions.

This is a good course! I hope University of Pennsylvania will offer the more advanced course so I can enroll, too! Kudos to the instruction and to the staff.

Video lectures were split() into short fragments which I enjoyed very much. Brandon, the professor of the class is very much involved with the audience and he is not monotonous. This is a huge plus! The only point of improvement that I would like to mention is; in some assignments the ask could be more clear. However, this comes with a hidden advantage, it gets you researching the Python libraries/documents, and learn about more advanced topics with trial and error. This is not a class to just watch the lectures, if you like to do self-research and looking for a foundational class to build upon, don't miss it. Make sure to install pyCharm and Jupyter in your local machine to test your code more efficiently (he walks your through how to do that during the class). All in all, this is a high-quality class that deserves five stars, especially recommended for finance professionals.

This course covers many areas of data analysis using the Python language. It is an excellent introduction to the world of data analysis, and a great furthering of development from the Intro to Python course that precedes it. I found it to be quite interesting and engaging, and it sparked a great deal of interest in data analysis in me, which previously was something that I didn't think I'd have much interest in. I am excited about the new level of knowledge and understanding of python I have attained, and also looking forward to learning more about python and data analysis.

The material is perfect, easy to read and understand.

Remember to get the slide from resourse page. It's helpful when you do your assignments and as a reference after you accomplish this course.

I would say I prefer last lesson by Brandon Krakowsky: Introduction to Python Programming.

The video in this course is less than former one. Maybe because data analysis is a practical one and need more project-oriented course to get familiar with the skills learned from this course.

One of the best courses I've ever taken! This course is an introduction to Data Analysis and Data Visualization using popular python libraries such as Numpy and Matplotlib. The course content is wisely crafted with sufficient in-depth knowledge and a lot of practice exercises on offer. I highly recommend this to everyone seeking a basic understanding of the aforementioned libraries and python programming in general.

Overall, very well-crafted. Content is presented accessibly, with special attention put toward aggregating and plotting data. This class requires a bit more outside exploration in terms of understanding functions than Penn's Introductory Python course. The videos and discussion forums continue to be very helpful in developing solutions for some of the more challenging problems in the homework sets.

Excellent course,

This course is intended for an online master's degree so it's not your most friendly beginner course as it goes by really fast in the explanation and the homeworks are challenging but not extremely hard for you to get discouraged. This is a serious course if you're really trying to get into the field and already studied a little of the basic syntax and variables.

The instructor, Brandon Krakowsky, is excellent. His instructions are clear and descriptive. He also seems to know when to repeat explanations or specific details. The technical support using Jupyter Notebook is very good. My only complaint is that the auto-grading sometimes doesn't consider that the order of completion may vary while the final results are effectively the same.

This is a pretty comprehensive and basic course for Data Analysis by using Python. I love the way that Brandon made in the video, I could always pause and follow the steps with him. But,,, like a 30 secs always have loads of things to follow and note, my advice would be that you should always prepare an external screen for this course when you learning :: Love this one!

This was a great course for focusing on pandas, plus some numpy and matplotlib. I took this after completing the Python For Everyone by Dr. Severance at University of Michigan, which is an excellent py 101 course.

All the hands-on problem solving really deepened by knowledge of these python libraries. It was time well spent.

Thank you for organizing this. I had best experience but I think i need further more exercises before the assignment begin, because i actually cant tell what the Qs wants. In Notes is too general.... i really there should be more scenarios and examples. but overall, great course. thanks

All the topics in this course are explained extremely well by the teacher. A very helpful pdf containing all the slides and coding examples is available for download. Highly recommended for those who are new to Pandas, NumPy and other tools for data analysis with Python.

This course is a little bit short (3 weeks) but the programming assignment is fun and challenging. I love that this course offers full set of presentation slides which are very useful reference for the course assignments and also for future use.