Great

good

good

good

good

Good

Good

good

good

good

good

Nice

Nice

cool

good

good

yes

ok

0

C

E

g

The last course in this specialization had me second guessing myself on whether or not I could do this (based on how difficult the quizzes/course project was). I spent tons of time researching, looking at the forums, and programming documentation. Going into this course, I was surprised about how much I knew when it came to the quizzes and how my time looking at all the resources above decreased and I actually knew where to begin! The course project was still VERY difficult for this beginning specialization compared to what information is given in the lectures (spent over 16 hours on it and still didn't meet ALL criteria). However, it does challenge you to think and reflect on your knowledge. You probably won't understand 100% of what is in the course (especially the project) but you will LEARN especially if you are totally NEW to programming like me! My knowledge has increased and that is what I was hoping for. If you have those same hopes and do not mind not getting 100%'s on all quizzes/projects this course is for you. Do be mindful that I personally feel that this course requires more than 4-6 hours per week especially for beginners. I spent more than 24 hours on week 4 alone.

Aunque el curso es muy bueno y lo recomiendo a cualquiera que quiera hacer una carrera como Data Scientist, tiene algunos problemas completamente solucionables, por ejemplo en algunas de las clases que muestran el uso de paquetes problemáticos y que son completamente prescindibles para el 99% de los casos. Además no hay orientación para los sistemas Linux, y siendo un curso sobre una herramienta open-source me parece una triste omisión. Dicho esto, el curso es muy bueno, y te va a enseñar a lidiar con datos desordenados y con distintos formatos de datos. No lo recomendaría para alguien que no tiene experiencia previa en R.

Although the course is very good and I recommend it to anyone who wants to pursue a career as a Data Scientist, it has some completely solvable problems, i.e. some of the classes that show the use of problematic packages and that are completely expendable for 99% of cases. Also there is no guidance for Linux systems, and being a course on an open-source tool seems to me a sad omission. That said, the course is very good, and it will teach you how to deal with messy data and different data formats. I would not recommend it for someone who has no previous experience in R.

I was pleased to be selected as a beta tester, but it turns out to be a little bit boring for a course I did already pass and succeed. Slides are unchanged, so my concentration drops after a while, even with some good will. It seems there are new swirl lessons, but they don't seem to be available yet, at beta test time, bad luck.I like the new overall presentation (provided by Coursera?) where we can pick transcripts without leaving the normal course flow. I tend to click the "finger up" icon for each lecture, because I just think the general organization of this specific course is quite good, and the contents consistent and not uselessly overlapping (unlike some other courses of the track such as "Reproducible research"). Also, the theme seems to perfectly fit the 4-weeks standard slot of the track. I have been busy those days, so I didn't fully review the course yet: I'll try to review as much lectures as possible till the end of the countdown. I'll try to do it better for my next beta test, if any. Anyway, thanks a lot for that very interesting track (I plan to go up to the capstone project, certified).