Great content!

Very practical

Amazing course

best course..

very helpful!

Very helpful!

Excelente!!!

Nice course

Excelente

Excellent

Muy bien

Perfect

perfect

Great!!

useful!

Thanks

good

good

Good

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I'm in week 5, and I think that this course is interesting and you learn from it. However it is done in a somewhat sloppy manner, to my taste.

My biggest problem is the notations and equations are a bit of mess. Beta's in one equation are replaced with phi's in another (sometimes in the same "lecture" slides) or theta's - there's just no real coherent notation. The formulas are brushed through, and they contain mistakes (a product of this sloppy notations), e.g. pi(beta) is missing the beta (which is what it depends on! week5, ARMA properties and a little theory). The R code is also sloppy, for example you see them setting variables in the first cell, and then never using them in the next cell. Or calculating variance using a cumbersome call to an acf function telling it to bring back the autocovariance, and taking the first term. TL;DR - It's just sloppy.

There are no exercises, but the quizzes contain some code you can run. Not enough for really drilling the material into you, though.

In general, I think this course could really improve, and I would like to see it do so. As a general introduction to the topic it might be decent enough.

This course is difficult as it's not the typical hands on practice with R that you can find anywhere on the web but it goes deep into the math and statistics to let you see and understand what's behind the calls to the automatic routines that at the end we will use. 4 stars are motivated for two reasons: 1. there are some issues here and there with labs and R code 2. I would have preferred a direct-line with teachers and tutors because honestly there are some topics that raise questions and require clarifications but as far as I could see nobody answers questions in the community forum and therefore I found myself going outside of the course to find answers on the web or back to math books which is good if you are persistent but it slows down the overall speed of the learning and the course. So my proposal to coursera : why don't give the possibility (maybe paying for their time) to interact with teachers ?

Judging by some of the comments left in the Discussion Forums, the course name may be a bit of a misnomer. I think the term "practical" conveys more of a hands-on applied focus (using software tools to diagnose and estimate various time series), rather than a more theoretical approach. While there are numerous examples provided, there's also a sizeable theoretical component. While it's certain arguable that setting students loose with software tools and no understanding of the basis of their development is also dangerous, I think "Time Series Analysis" or "Time Series Analysis Fundamentals" might be a better title for the course.

That being said, the course met my objectives. There are a number of links to datasets that are obsolete; it would be good if these were updated, rather than having to spend time tracking them down on the Internet.

Professors obviously know their stuff and work to outline all the math fairly logical. The title, "Practical Time Series" is a little lost on the actual workload. I am finishing week 3 and I have yet to find anything 'practical' about the course. i'm very intrigued about the math, it is interesting and challenging, but i felt like the discussion in week 1 about all of the data sets we were going to use was a tease.

I would be better able to absorb (not just learn it long enough to ace the quizzes) the material if for each concept there was a practical application of the concept to one or more of the data sets that were made available to us. Because we don't, I often find myself in my own head, searching for applications, and thus not fully paying attention to the videos, which then I have to go back and watch multiple times.

This is a fantastic course, and I would recommend it to everyone that is interested in Time Series Analyses. After finishing the 5 week program, I can confidentially say that I feel comfortable to start tackling TS projects and build some forecasting models.

However, I deduct one star because the learning curve is very steep and could/should be supported more through graphs and examples, especially in the earlier part. This can be frustrating, especially for people without a very strong statistics background. My best advice for now is to keep going, many concepts become clearer in later lectures.

Overall, this course is highly enjoyable (for a statistics course on R) and I do recommend it to anyone that wants to explore the fascinating world of ARIMA models and time series.