IF all the points in previous slide didn't convince You, then think about Alan Turing - one of the most impressive man of 20th century, who was not only great at math, cryptography (thus saving millions of lives at WW2) and is father of computing science. also he made some important contributions to the theory of Bayesian statistics.
Basically he founded some of the things I am dealing with know. And that makes me feel closer to this course.
Positive aspects (I):
Positive aspects (II):
The subject of this course is rather complex, especially for person with no technical and mathematical background, I would suggest preparing extra information for total begginers, explaining so called “obvious” things (formulas, funcions of graphs, semantics of certain terms).
Even if at fisrt this course is hard to grasp, it doesn't mean it it not interesting, on the contrary, understanding took some time, but we gained knowledge about modern technologies and data configurations. For me, person with background in humanities, it was great excercises for the left side of the brains. Even if from time to time, questions going around in my head seemed a bit dumb, nevertheless, they were answered with no condescending tone.
I think, that environment and communication with learning staff is very important component, and in this course all lecturers were very competent and positive, and is the thing that I enjoyed most of all, light and pleasant atmosphere, that doesn't clug Your brains and the incoming infomation is easier to understand. And ofcourse the tirp to the lab was also a great experience. Most interesting lecture of all for me was about Artificial Neuron Networks, even though it is complex and a bit apocalyptical (Something from Terminator and Matrix combined), it was very exceptional.
Maybe a little bit parralyzing was my knowledge gap in this subject particularly. I would have prefered a little bit history of Big Data analytics, just to clarify the development of the scientifical aspects. Altough there was a lot of interesting facts in this course, just a hint - there's never enough.
library(cluster) library(fpc) data(iris) data_for_clustering <- iris[, -5] clusters_iris <- kmeans(data_for_clustering, centers = 3) plotcluster(data_for_clustering, clusters_iris$cluster)
Overall, outline of this course is logically arranged, course wasn't simple, but a little personal effort on our side could really do a lot of good in this course. Practical experience working with R made all the theoretical aspects easier to understand.
THANK YOU FOR YOUR TIME AND ATTENTION!