A very good and concise course that helps to understand the basics of the Data Science and its applications. The examples are very relevant and helps to understand the topic easily.
Highly educational course on the realities of data analysis. Many good tips for your own analyses as well as for managing others responsible for coherent and accurate analyses.
par SATISH R•
par Luis A S E•
par David T•
Some good tips, nothing terribly new for those who have had a course in statistics. Materials made easy to digest. The variety from the 3 instructors was nice. Missed opportunity: to combine the best aspects from each. The course notes were either excerpts from R.Peng's books /blogs (good) or automated transcripts (complete with typical AI typos... "wait" instead of "weight"). Some lectures were repetitive from one course to another. Slides with examples were useful, slides with clip-art and comic stips less so. Tries to be something for everyone. Would be better to aim either at former DS analysts aspiring to be managers or seasoned managers trying to better understand DS.
par Ruben S•
Brian tries to achieve too much in too little time. It addresses important issues and it gives a good overview, including some hidden gems (Machine Learning vs Stats, for example), but it feels mostly too rushed and superficial for my taste/expectations, and it fails to connect to my previous knowledge (and I have a PhD in Maths, although no strong Stats background), hence little added value for me when I cannot relate to what is being discussed.
par Rajeev R•
Lectures themselves were OK, but presentation needs work. Intro session was very repetitive. Lot of jargon introduced without explanation. Pop-ups w text showed up but disappeared before I was able to finish reading them. Best part of course was actually the text notes at the beginning of each sesssion. A minor nitpick: course description suggests that there are 3 instructors presenting, but I only saw one.
par Gonzalo G A•
It's sometimes difficult to follow professors beacuse they take for granted information about the examples they use that is not evident for the learners. They should take a minute to explain a little bit more what the examples consist of and what are the charts they show. As it happens when Brian Caffo explains the blocking adjustments part.
par Cauri J•
I found this course used a lot of jargon without explanation. It seems like the instructor understands the content so well that he assumes a level of knowledge from students that do not match the expectations of the rest of the content in this track. At the same time I found the content well presented.
par Michail C•
This course is an excellent effort to document the issues faced in real-life data science. However, the flow of the videos seems to be a bit confusing and some of the content is explained in a weird manner.
par Daniel C d F•
I missed several concepts to better understand some of the discussions and explanations. It was valid, but I think the statistics background should be better explored.
par Peter L•
The course is valuable but highly focussed on scientific applications (inference) and less on business application (i.e. prediction). I hoped for a more even mix.
It was good, but the content is harder to understand in this course.
I would prefer a similar format and emphasis as the other two last courses.
par Sean H•
The video quality and content were good. Unfortunately, there were a lot of spelling errors and grammatical mistakes in the written portions.
par Chong K M•
Very difficult and time consuming course which contains a lot of technical words and jargon. Not recommended for the average beginner.
par Jean-Michel M•
I would drop some of the cartoons. They are funny but they seem to distract Bryan and overall it's distracting for us students too.
First of all it's too tough to understand but day by day I understood something I got it ..tq.it is very helpful for my studies
par Rong-Rong C•
There is a lot of technical jargon covered which made the course more challenging than the other courses in the series.
par Alberto M B•
It wasn't as focus on Managing Data Scientists as I was expecting, but rather focus on tips for Data Scientist.
par Marco A P•
Much theorical with few examples. Could incorporate examples outside the health world as well.
par Giovany G•
I would prefer that the examples be expressed with statistical and mathematical calculations
par Gilson F•
Não gostei muito da didatica do instrutor e os slides não ajudam no entendimento
par emilio z•
Explanations in videos qere not very clear nor very well connecetd with the Quiz
par Christopher L•
Would have liked a bit more examples and math in some cases. Others were fine.
par Ioannis L•
A bit less engaging than the other parts of the Executive Data Science course.
par Patricia S•
good content but could be simplified and presented in a more focused man