C

Solid process overview of managing a data analysis project.

Overall a straightforward course and the length/depth is appropriate for the course objectives.

Some of the material is entry level management and experienced managers can judge how best to consume the course

The course does not directly cover supervised / unsupervised learning but refers to association and prediction. There is no mention of cross-validation data sets, F1, precision, or recall as "measures" for evaluating the formal models.

The EDA section could be bolstered by mentioning feature scaling as part of the exploratory data analysis. There is no direct mention of cluster analysis, k-means, PCA, or similar tools that may be applicable to EDA.

Good course if you have some knowledge of data analysis and an interest in the area. On completing I felt more confident about my abilities, in my ability to work with data scientists, as well as critiquing some past projects and realising how I might have improved them. (I also now realise and can explain why recommendation algorithms are so annoying, which is perhaps more important.)

Pretty good, but I would have liked more math. I understand that others would not, but many times one equation can cut through 3-4 paragraphs and be more clear than the text. It can be frustrating knowing that if you just had the equation things would be 100% clear, but with just a bunch of text, you just get a vague idea, for more work, ie reading time.

The course was good but as a suggestion, walkthrough of an example for the modelling would have helped. I was little confused when the equation was used during the course to explain the confounder, predictor and outcome. Instead of using X, Y, Z - may be use an actual example and show they all relate would have made the course

Very interesting insights and ideas about how to manage Data Analysis, especially the part with the communication. I think there could be some more emphasis on the troubleshooting side, as it overall appeared to be a finite, engineering process which can always end successfully if the instructions are followed closely.

It was very informative. The instructor needs to slow down just a little though, I could tell he's a little nervous speaking to "large groups". Otherwise, it was great. I'm from BAWA and I am familiar with and love JHU, so I support any great course coming from there.

Excellent focus on what makes managing Data Analysis teams different from managing managing other teams. This course has the most impact if combined with general management background/classes. Speech fillers, like "um," "ah," "like," etc. prove distracting at times.

Great course and overview of the processes involved. But it would be great if the readings were also put into videos (I'm doing an online course - if I wanted a book, I would buy it). And please (!!) get rid of the music in some of the videos, as it just distracts.

Brilliant course - fantastic overview. What's lacking for 5* is the EDA exercise in R should've been within an R IDE to enable total beginners to get more hands on. After all EDA is mostly about DOING. Hope course designers fix this for the next iteration.

The topics in the course shows that there is a set of steps to counduct a data science project since the definition of the question to solve to the apropiate way to communicate the results. The content of some videos could be considered technical.

Very valuable, however, in particular the section on inference vs prediction included material not explained before and hard to follow. Also examples with t-values and interpretation of values when adding confounders was difficult to grasp.

Liked how a lot of content was covered in a small amount of time. Thought the instructor was effective in presenting the important elements of the lectures. Only issue is some typos in the quizzes that were a bit distracting.

Great course with short digestible lessons, great lecturer with an ability to communicate technical details in a very engaging manner, and my appreciation of data analysis is better. Am glad I took this course.

It was a great experience a big amount of knowledge in a short presentation. Overall the summary papers are great, however I missed a more interactive videos with more bullets points and etc.

Thank you!

Like other reviewers said, this course is larger than the two previous courses. The content is excellent but I am giving 4/5 stars because I found many misspelled words throughout the course lectures.

This course is not for a person without any idea of Data analysis or Statistics. This course isn't for beginners. The content could have been presented even better with lucid examples.

Good overview of the process. Helped me in bridging data analysis processes with things that I already do as part of project management or business analytics/decision support projects.

Critical thinking is essential at the moment of working over data. This was a nice course with a very good theory about all the process,which a data analyst has to perform.

Well defined strategies for getting a handle on the data analysis process. Short and concise class that hit on relevant points required to be successful in this area.

sometime it was not easy to understand the lecturer. also, it would be good to try some things out versus reading the expamples. other than that - a great course!

A great course with a lot of information. However, the course might be more concise sometimes due to the complexity of the information brought by the instructor.

Good course for the fundamentals of making sure data analysis is done correctly. These are good things to keep in mind when you are managing a data science team.

Course is very good and concise from a business owner point of view. Other technical courses will definitely add to the nuances of full fledged insights.

Comprehensive overview to understand activities involved in the iterations and epicycles of Data Analysis and to manage it with correct expectation.