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學生對 纽约州立大学纽约州立大学 提供的 Practical Time Series Analysis 的評價和反饋

4.6
1,579 個評分

課程概述

Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future. Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn. You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself! Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!...

熱門審閱

SS

2021年4月6日

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.

SA

2020年1月23日

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.

篩選依據:

276 - Practical Time Series Analysis 的 300 個評論(共 440 個)

創建者 Marek D

2020年1月26日

Great content!

創建者 Yuen T C

2018年6月28日

Very practical

創建者 Deleted A

2018年6月5日

Amazing course

創建者 Ashutosh M

2021年1月5日

best course..

創建者 刘思航

2018年10月24日

very helpful!

創建者 Yu Q

2018年7月18日

Very helpful!

創建者 Gabriel A C N

2020年6月21日

Excelente!!!

創建者 Juan L R A

2020年5月27日

Nice course

創建者 Moises B J

2021年5月10日

Excelente

創建者 Roberto G A

2019年5月23日

Excellent

創建者 José E H M

2022年6月2日

Muy bien

創建者 Alireza P

2020年7月22日

Perfect

創建者 ِِِAli A A

2020年7月16日

perfect

創建者 Douglas B P

2018年9月2日

Great!!

創建者 Cathy D

2018年5月22日

useful!

創建者 Alla E G

2020年1月18日

Thanks

創建者 Mehrpouran, S (

2022年1月18日

good

創建者 GAUTAM T

2021年7月31日

good

創建者 Ganesh

2020年6月4日

Good

創建者 Aditi D

2022年8月5日

-

創建者 D. R

2019年11月9日

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.

創建者 Andrea G

2021年10月31日

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 ?

創建者 Murray S

2021年4月22日

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.

創建者 Ron M C

2019年9月8日

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.

創建者 Matteo B

2020年5月25日

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.