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282 Bewertungen

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!...

Jan 24, 2020

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.

Mar 21, 2019

This was a very good and detailed course. I liked this course for two reasons mainly:\n\nIt started from the basics of timeseries analysis, covering theory and secondly it took me gradually to r.

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von Koustav M

•May 02, 2020

Great help concept and R code wise

von Udit G

•Mar 02, 2019

Good, but can be more extensive.

von DropRooster

•Mar 08, 2020

Great for beginners

von Yash G

•Mar 25, 2019

Amazing course

von Prateek G

•Jan 01, 2019

Great content

von Kaumba C

•Oct 14, 2019

Very helpful

von Wim

•Jun 02, 2018

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von Eli R

•Feb 04, 2020

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.

von Daniel M

•May 30, 2020

The course is a good opportunity to learn and/or review basic concepts on time series modeling (e.g. ARMA, ARIMA). They are presented in a way that ideas are easily understood, but also covering enough detailed aspects. I really enjoyed lectures of Professor Sadigov as he usually derives all of the formulas presented.

However, there are many things in this course that I would urgently improve. Content is not properly sorted, so you can find concepts being mentioned before being introduced or concepts being introduced twice. Moreover, I have encountered errors in both slides and quizzes. Most of them had already been spotted by other users in the forums, but they have never been corrected. Indeed, course has not been reviewed for a while, so you will often struggle to get the data used in the lectures as links provided are no longer valid. Quizzes are also very simple, most of the time.

von Artem M

•May 18, 2018

Contents of the course are very good. It provides a compact introduction to the most important statistical methods for time series estimation. Nevertheless, there are many mistakes in slides/exercises, necessity to download datasets instead of using predefined ones, and a great number of time series libraries to download when we could just use 2 of those, which is ok, but I took 1 star for it. The other star taken was because I think the course could still include more information on the theory of time series (yes, I understand that the name is PRACTICAL time series, but still), and more meaningful exercises. Most of those were pretty trivial.

von Miguel L

•Sep 05, 2018

Overall, it is a pretty good course: in just a few weeks you are introduced to a satisfactory level of time series analysis with R even if your background in R and/or statistics is not that high. I have personally missed some more depth (even if optional: for example, providing additional optional readings, or creating an honours track with extra difficult assignments for motivated students). Instructors are pretty good but they should make an extra effort to be clearer and not to speed up too much: some more additional videos or just more time for each video covering the same content would be better.

von Sharon M

•Feb 28, 2020

While this course was pretty thorough, it was way too mathematical and not nearly as applied as I would have expected a 'practical' course to be. It was also extremely frustrating that a large proportion of the forum messages went unanswered - I've never done a Coursera course before where there hasn't at least been a research assistance answering questions. You really had to try to follow everything yourself, and if you had a question, you had to find the answer yourself. A bit disappointing.

von Richelle S A

•Jul 21, 2019

It was a nice course, very informative. But week 3 simply had way too much matter in it in comparison with the previous 2 weeks which makes it difficult if you've set a timetable. Also, week 6 had poorly maintained data availability, and missing code. Lastly, the professors are absent from the discussions. There are unanswered doubts from longer than a year ago.

But if you dedicate enough time and effort, this course is pretty good.

von Lyla F

•Jan 24, 2020

It's decent for what it is - a course trying to balance theory with practicality. However, it uses R for the target implementation environment. That's fine, but as a matter of taste, I prefer python. Therefore, for me, the practicality aspect of the course wasn't the greatest, and that was its main selling point.

von Arman A

•May 18, 2020

It is in R, making it rather limited for applications in industry, however using Python's statsmodel you should be able to keep up with the notes.

I was expecting a lot more theoretical explanation of the concepts rather than just demo'ing some R code and fitting some libraries to data.

von Pablo C T

•Oct 14, 2018

Overall, this is a good course for beginners in time series analysis.

However, I wish they had go beyond the basics. I missed more advanced content, discussions between different forecasting techniques, multivariate forecasting...It is pretty basic.

von Herman d V

•Nov 06, 2019

Very interesting course. For a 'practical' course, I did find it rather theoretical. Also, some links to downloadable data are missing and there are some small errors in the R scripts that are offered. Nonetheless a good course, I learned a lot!

von Emanuel N

•Mar 09, 2020

While professor Thistetlon gave the classes in a way that kept my attention,Prof Sadigov wasn't so good at it. Clearly he know a lot but not as a teacher.

von Mark H

•Jun 27, 2018

Would have loved more work with the ready made packages, I feel like I still can't master using them in R, although I did understand the material.

von Saket M

•Apr 18, 2020

Instructors are good but as per the title this is not more than 30% practical Time Series Analysis

von Maria B

•Oct 19, 2018

The content was great, but I'd have liked to see more practical exercises.

von Stéphane D

•Mar 21, 2020

I find the course a little too theoretical

von Joe G

•Jun 30, 2020

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.

von Panagiotis K

•May 05, 2020

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.

von SK A F

•Apr 24, 2020

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.

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