This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, Techniques and Models, and Mixture models.
Time series analysis is concerned with modeling the dependency among elements of a sequence of temporally related variables. To succeed in this course, you should be familiar with calculus-based probability, the principles of maximum likelihood estimation, and Bayesian inference. You will learn how to build models that can describe temporal dependencies and how to perform Bayesian inference and forecasting for the models. You will apply what you've learned with the open-source, freely available software R with sample databases. Your instructor Raquel Prado will take you from basic concepts for modeling temporally dependent data to implementation of specific classes of models
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It was a nice course, but it would be better if there were more supplementary materials for the proof and theoretical discussion.
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My suggestion would be to put most of the derivations that were written out by hand onto slides. The presenter could spend more time explaining the information and less time writing. That might make the course a bit more accessible.
The sessions taking the student through the R code and presenting demos and applications of the theory and concepts presented in the course, by contrast, were much better. In my case, I felt I learned as much from the code demos as I did from the presentation material.
Finally (and this is more of a general criticism of Coursera courses), it would be nice to provide references to books or websites where the reader can go for additional information. For instance, the instructor has a textbook on time series analysis and this isn't mentioned in the course. I realize the goal is not to sell textbooks, but I think this would add value overall.
An excellent series of videos and coding sections, but a serious bug in the final assessment peer review form (together with a lack of engagement from course instructors) left a sour taste and brought my review down from a 5-star to a 3-star.
This series of courses has too few students to support peer review assignments.
I liked this course. Nice balance between theory and practice. Prof Prado is a great instructor and explains everything very clearly. Lot's of useful coding examples in R. Articles on dynamic linear models are much clearer after taking this course.
riveting course! I have reviewed the lessons several time and I am still studying applying the concepts I learned to time-series data of my interest. Thank you to the teacher and the staff behind this course.
It was a nice course, but it would be better if there were more supplementary materials for the proof and theoretical discussion.