Despite this being the course with the lamest name (sorry), I really enjoyed it! I learned a lot of new stuff and also got to apply the things that I learned in the previous courses (especially Exploratory Data Analysis), so I feel that this was time well spent.

The course was fantastic. I realized the power that a Data Science Analyze can create. In this module in particular, I was even more interested in completing the specialization. Thank you Professor Roger Penn and the entire team of teachers for their teachings.

A great course which might not draw the right attention while moving towards a data scientist role. But without a great deal of focus on the communication of analysis is even more important to gain buy-ins within or outside the org. Will keep them in mind.

Wonderful course. Maybe you could enlarge the part related on litarate programming, and you could change the position in Data Science Specialization. I also think, it would be better to arrange it after Statistical Inference and Regression Models courses.

This course makes us re-think things that we take for granted. I was shocked in the beginnig on how we ignore practices that should be the basics of any research. As the course progress, I learned new concepts what is essential to our self development.

I guess from the case studies and research on the web whats I learned from this course is the importance of reproducible research is. This course explains the importance of it and the ways to achieve it easily and concisely. Thanks for the authors.

This turned out to be one of the more fun courses, especially listening to Rogers lecture "live in class room" and also the case presented by M.D. Anderson was great. I really enjoyed this course, even more than I initially expected to.

This is an excellent course which teaches you fundamental best practices in research. After completion you will look back at early scripts in horror! It's unfortunate that the practices you learn aren't more widely practiced.

My favorite course in the degree, so far! I started applying what I learned immediately. This course should be mandatory for any data analyst, as the concepts are applicable no matter which language or tools are used.

There are important things you usually dismiss.

The lecture is great and it really deserve professional course.

This lecture is potentially hard, so you should spend a lot of time for the lessons and course projects.

Great course that provides a structure for analysis and how to challenge the analysis. I found the assignments hard. The lack of information about the data for the assignment represented a real-life situation.

I have found this course very useful in order to learn the keys of reproducible research. Moreover, both course projects are useful for putting other skills of this specialisation into practice. I recommend it!

This course had a profound impact on the procedures I use for data analysis. It provides best practices for documenting the steps of the analysis to ensure accuracy and quality. I highly recommend taking this.

This has been a great introductory course into reproducible research. The topics were clearly carefully chosen and wisely integrated to create a smooth flow. Money well spent and invaluable knowledge gained!

It is a nice introduction to some important issues in scientific research, may not be so intresting to non academic data scientist hopefuls, but it is very important, I think to those that are within academia.

A very important course that greatly improved my ability to communicate the findings of any sort of data analysis that I perform. This is a critical skill to acquire to "deliver the means."

A very useful course. It helped me to improve the way I structure the analysis at my current job, especially by keeping track of every transformation I apply to the data I’m working with.

Of course, I liked this course. There was even an extra non-graded assignment. Plus two graded assignments. Quality instruction videos and lots of practice. Everything a learner needs.

I took this course as part of the Data Science specialization without any real expectation and realized that this subject is probably one of the most important in data analysis.

This course is very helpful in terms of not only doing the analysis but also getting to know the finer nuances of making a structured markdown document for future reproducible.

Great topic which is discussed well with a good case study. I'd like to see more up-to-date content and more detailed analytical techniques. However, it's a nice introduction!

I personally got a lot out of this, both from a philosophical perspective and a nuts-and-bolts perspective. And I got to practice a lot of stuff learned on earlier courses.

This course provides the necessary resources to learn how to do good science, what is reproducibility and few guidelines about how to write a good report using RMarkdown.

Very informative and enjoyable class. The importance of reproducible research is stressed clear and concisely, Roger D. Peng does a great job of explaining the material.

Highly recommended for beginners to learn the basics of Data Science, Re-producibility and how to write a good report around the analysis done by you as a data analyst.