Terrible explanations... they do not make any sense....

Basically the instructors are reading mathematical expressions. No intuition, no significance, you are taught mechanically, sorry to say it

I wont buy anything from this provider ever.

Nothing practical, real example are used to enhance theoretical stuff.

Not a single example of practical use.

Too many mistakes in course - quizes are based on following week materials, materials are titeled with mistakes, there are mistakes in narratives in addition to the mistakes in what is talked.

One of the lecturer is talking like he has to read slides as quickly as possible, because he is to buisy with other stuff.

Too much math. you have to know algebra really well to understand what he is talking about.

you should learn some R by yourself, because it is not

explained how to do a lot of things. i think R should be in cluded in course title, to make people know in advance...

my score is 1.4(9) stars....

Time Series Analysis is done for one two reasons: [1] build a model quantifying patterns and noise and [2] forecasting. The two goals are very different. You can execute forecasts in R without understanding the underlying mechanism. To get a good grasp of time series forecasting you MUST understand how to model the time series and that is done with mathematics.

COURSE GOAL and CONTENT: This course focuses on the mathematics of building time series MODELS. Forecasting is addressed lightly towards the end. The course should be named “Time Series Mathematical Primer” or something like that, IMO.

INSTRUCTORS: Sadigov is an expert but he can’t teach. Add to this heavy accent and inability to speak clearly, and you have someone who would benefit from accent reduction training and adult learning training. The other lecturer, Thistleton, is easy to understand and conveys explanations rather than reading formulae out loud. Both are inferior to say, Galit Shmueli (https://bit.ly/2qM9eHL) whose free Time Series course online is clear and practical indeed, IMO.

MATHEMATICS and THEORY: Although I had reasonable mathematical training in the past, I found parts of this course hard to follow and difficult to comprehend. I have been frustrated with the materials and having no ability to interact with the instructors or a teaching assistant. Most of the discussion topics / questions have no answers. Don’t expect any help from anyone, unless you formed your own study group with people you already know.

BOTTOM LINE: While I do not regret taking this course, I feel I would have benefited more from a course whose material is more practical and whose instructors know how to teach.

Very monotonous lectures. They feel like a recitation of formulae.

Great introductory course on time series. Focus on ARIMA model most of the time while the last lecture capture a little bit of exponential smoothing. Would be great if there if summary lecture regarding to when to use which modeling technique. would be even better if there is a optional lecture to cover some of the more advanced time series models.

I found one of the instructors (Thistleton) much clearer and didactic than the other. I would have liked a deeper formal insight into the models that were discussed: limitations, assumptions, what kind of physical models they can represent? what to do with systems that don't behave "nicely"?, etc.

There was not a good intuitive and more visual explanations of the principles behind the techniques.

Given the proposed 'practical' nature of the course, it would be better to explain any concept by using concrete every-day examples than preceding them with a elaborated mathematical reasoning of the equations used.

It is an interesting course, however both instructors should rebuild their material and make it more interactive and viewer-engaging. Practicality is not always pursued throughout the lectures, leading to unnecessary and tiresome long talks. Focus on real data as well as more programming exercises would really benefit this course.

Avoid if you're looking for an applied or practical course.

Too much detail and outdated course

It is a very good course which builds on the basics of time series and also covers more advanced topics like SARIMA. The course contains ample examples which helped me better understand the material.

a good course on univariate time series modelling by ARMA type models, with additional details on Yule Walker equations, seasonal models, and forecasting.

This is one of the best courses I've taken so far on Coursera. The exercises and delivery are so practical. I had taken a college course in Time Series Analysis, but didn't pretty much understand the concepts. This course has given me far better theoretical and practical understanding of TMS. A big thanks to the professors.

This was a very good and detailed course. I liked this course for two reasons mainly:

It started from the basics of timeseries analysis, covering theory and secondly it took me gradually to r.

A nice course which is practical as the name said, it balanced the portion of theories and practices. I used to not familiar with this topic, but now I consider myself much more familiar.

Overall, this was a really great class. I have a good grasp now for the basics of modelling time-series and producing forecasts (using ARIMA, SARIMA, Smoothing with Holt-Winters). I also have an understanding of the tools used for analysing time-series (autocorrelation, partial-autocorrelation).

The course is a good mix of theory and practice. I haven't taken a stats class for a long time; however, the first week was more than enough to refresh my memory. Really, only some basic stats knowledge is needed (mean, covariance, correlation). There is also quite a lot of usage of the geometric series. However, a lot of time is spent reviewing this topic later (week 3, I think). The course is done in R, but the usage is so small that anyone with any experience in programming will have no trouble. I actually had no experience with R before this course, and I finished the course with only a little additional experience.

My only complaint is that the course could use some minor maintenance. One of the weeks (maybe 3?) has a section of duplicated notes, but I'm pretty sure that the section with duplicated notes could be deleted. Most of the pdfs have the wrong week in the header (likely a new week was added after the course was released). There are also several quiz questions with incorrect values (before taking a quiz, I recommend scanning through the forum to find known problems). The instructors do occasionally post in the forums, so that gives me hope that the course will not be abandoned.

On other courses I received feedback on the forums in a prompt manner from the instructors, here none of my questions have been answered.

This course title needs to change, This is more about statistics but its title is about to Practical analysis. Practical part with default R database with packages. And those are very old datasets. Labs need to upgrade with and some lectures also.

Very unhappy with the course. Just completed it because I started it. Waste of time.

Excelente, uno de los mejores cursos que he tomado. Lo más importante es que se practica muy seguido y hay examenes durante los vídeos. Si hay un nivel más avanzado de este tema, seguro que lo tomo.

I have not completed the course yet, working on week 5. If you have some Math background, this course gives a good practical introduction to Time Series Analysis. I recommend it.

Lots of theory, but very well-founded, and very practice-oriented at the end.

It is a good course if you want to learn about the basic concepts of time series

This got a little too technical a little too quickly. It doesn't tie any of the abstract operations to real-world interpretations, so I got a little lost in the rapid series of transformations. The practical examples were interesting and helpful, but they came a little too late in the course for me to be able to juggle all of the concepts successfully. It's almost like having someone read a textbook to you.

Could've been more technical, with introduction to new machine learning techniques like RNN.