How do we infer which genes orchestrate various processes in the cell? How did humans migrate out of Africa and spread around the world? In this class, we will see that these two seemingly different questions can be addressed using similar algorithmic and machine learning techniques arising from the general problem of dividing data points into distinct clusters.
In the first half of the course, we will introduce algorithms for clustering a group of objects into a collection of clusters based on their similarity, a classic problem in data science, and see how these algorithms can be applied to gene expression data. In the second half of the course, we will introduce another classic tool in data science called principal components analysis that can be used to preprocess multidimensional data before clustering in an effort to greatly reduce the number dimensions without losing much of the "signal" in the data. Finally, you will learn how to apply popular bioinformatics software tools to solve a real problem in clustering.
MK
Absolutely fantastic course. Kudos to the course creators.
DD
Truly awesome. What I liked best was that this course didn't have a peer reviewed final challenge, so I didn't have to wait months until my work was graded :)
ZX
In depth and comprehensive coverage of the topics in genetic data analysis.
HW
the part about EM is the best I know, and first time I understand the EM algorithm.
JM
Really enjoyed the clustering chapters and the practical exercises with the yeast dataset
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In depth and comprehensive coverage of the topics in genetic data analysis.
Absolutely fantastic course. Kudos to the course creators.
The first two weeks were very enjoyable and instructive. However, the third week was unrelated to everything else and did not contain lecture videos which made understanding it very complicated. I also found difficulties in solving quiz 3.
Please add more example to understand the formaula to apply. Being from biology backgroud it was difficult to an extent. but the explanation to the content was very well given.
This course is too Short and not up to date with deep learning
Material are poor... missing information about content asked in the quiz... the forum is not moderated... and Quiz 2 have a problem with inputs!
Lack of explanations of the contents.
A nice course to learn clustering algorithm in bioinformatics, it will be better if you take the previous class. Just jump into this class without some pre-knowledge will be quite difficult.
Truly awesome. What I liked best was that this course didn't have a peer reviewed final challenge, so I didn't have to wait months until my work was graded :)
as all the other courses of the specialization, this course is addictive, intelligent, and you learn a lot
Very interesting course! So inspiring :) Recommend for everyone who interested in bioinformatic
Really enjoyed the clustering chapters and the practical exercises with the yeast dataset
the part about EM is the best I know, and first time I understand the EM algorithm.
Good course to exercise you data science skills
very excellent course
Awesome course!
good course
The contents of the first two weeks are very well-organized and apprehendable. But the content of the third week was a bit difficult to follow due to the absence of an interactive text and lecture videos. I also wish that there were some code challenges for the last week. Now, the things I liked especially are the clustering algorithms and how the intuitions behind them were developed. I enjoyed implementing them and experimenting with the gene expression data of the diauxic shift in yeast. Another issue I must mention about the pdf provided as the last week's reading material that the embedded links do not work. Please fix them. I would love to play with the data and tools but could not.
The first two week was very good, but for week 3 I don't see the content has any connection with Genomic Data Science.
short course, but pretty informative.