This was a good course, but didn't have as much labwork as I would have liked. There were a lot of labs, but they were mostly already completed by the instructors - more of a read-along than actually doing work yourself. That said, it was a valuable course and don't regret taking it.

This entire course is elucidated through projects and their respective tasks and assignments, allowing for a comprehensive understanding. Skills: Data Analysis, Visualizations, & Predictive Modelling Model Selection Python: Pandas, NumPy, Seaborn, Matplotlib, SciPy, Scikit-Learn

This was a very challenging course. i don’t think I had any business choosing an intermediate level course because I have no experience in Data Analytics so I am a beginner. It was very interesting to see how statistics and math concepts were applied though and I did learn a lot.

Great course to help us understand more about Python libraries. Just marked as 4 stars because I wish we had a better conclusion, showing us how to explain the charts and values to a meaningful insight for decision making. There could also be more dataset examples for training.

This module required a lot of effort and dedication in studies. It was very gratifying to complete it. Particularly, I will have to study the topic of this module a lot. I can say that this module deserves a separate course. Thank you to everyone who helped me along this path.

The course teaches an incredible amount of information in a relatively short time. The downside to this is that users don't get enough practice within the course on the data analysis methods and functions taught. Additionally, there are a lot of typos that need to be fixed.

Course was very useful and helpful. However, there are so much new and complex information being introduced in just one 4-6 minute videos, which can be difficult to understand, and may require to watch the same videos over and over, as well as alot of practice to be done.

Pretty good class. Ridge Regression and GridSearch were new topics for me. Felt the explanation of hyperparameters could have had a little more detail and the Week 5 lab has some details to correct. Lesson on polynomial regression and under/over fitting was well-done.

I think the breadth of content in the course was a bit too wide. More modules, and Python content, focused on exploratory data analysis could've been expounded upon, instead of so quickly moving into predictive analytics. Nonetheless, I did gain value from the course.

This module is loaded with concepts. Even though they are introduced in a logical sequence, it gets a little overbearing and tend to lose the relevance in the context of car price prediction. At least, now I am aware of the techniques, methods and python's capability.

Good introductory course. Even though it is an introduction, the course would benefit a lot from including a bit more of theory, even as optional material. For example, including theory about ridge regression, instead of just mentioning how to implement it in Python.

It is a great course. The one thing I believe could be better, is to deepen the scope of the mathematical concepts. Indeed, it is a course that assumes knowledge in that area, but it would be great to include links to papers or articles that explain those concepts.

Content was decent. Do ungraded labs provided as practice exercises if you want much exposure and and free flow of code while using the data analysis libraries. Overall, the course is helpful for an intro and intermediate level. Will definitely work as a refresher.

I think that you missed more detailed explanations on how to analyze the results, especially for those of us who are not mathematicians or with advanced knowledge of statistics. But, is a fact that In the end it was the course i've enjoyed the most. This is awesome

Great notebooks and clear content except two points :

1 - polynomial regression and pipelines have not been enough thorough and detailed. Quite complicated to aprehend

2 - final assignment question 8 - nothing to do. answers were already in the downloaded notebook

The course is quite challenging for me as a beginner of using python to perform linear/non-linear model development. It is good in terms of the plenty of content for people to learn but it is quite hard also as it would be better to have more practice / examples.

This course teaches you important python liabraries like pandas, scikit-learn. It also provides information about regression and helps us to build a model for a given case. Overall its very nice course for getting idea about how to do Data Analysis using Python

I think this is a decent course that introduces data analysis on a basic level. The first 3 weeks were really well written, the last 2 weeks have some faults in them though, like values referred in the text which does not match with values written in the code

Very good introductory course on data analysis using Python! It is best for people who already had some level of analytics experiences before as it sometimes goes a little bit fast. But very good in general, covers a wide range of topic, with good exercises.

Good to start with, this course provide you with the step involved in Data analytics but no logic behind these steps are provided. If you are new to python library this course will be helpful for you as it involve use of pandas, Scikit, Scipy and matplotlib.

Really enjoyed the Labs, specially the last ones that were long and covered a lot of material in depth. I think the course would have a better user experience if it wasn't for the many spelling mistakes and small bugs, specially in the Jupyter notebook Labs.

Toughest course so far. I liked being able to visually see the statistics behind data analysis, which was much more helpful than the textbooks I had to use to earn my math degree! However, the final week was still a challenge to get through and understand.

I liked the structure and pace of this course. The videos and exercises were helpful and the final project was a very good measure of what we had done in the course. I took off a star because there were more typos in this course than some of the others.

There should be an Handicap assistance in the course as some of the visually impaired people are finding it difficult to read the assignment codes with their screen reader nvda.

The assignment notebooks code settings need to be modified to support this.

Covers lots of materials. Lab is at end of each week, but I did better following along with coding during each lesson, A good framework, but with a lot of jumps and not much depth. With additional studying from other sources, I got a lot of knowledge.