This course covers the core techniques used in data mining, including frequent pattern analysis, classification, clustering, outlier analysis, as well as mining complex data and research frontiers in the data mining field. This course can be taken for academic credit as part of CU Boulder’s MS in Data Science or MS in Computer Science degrees offered on the Coursera platform. These fully accredited graduate degrees offer targeted courses, short 8-week sessions, and pay-as-you-go tuition. Admission is based on performance in three preliminary courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals. Learn more: MS in Data Science: https://www.coursera.org/degrees/master-of-science-data-science-boulder MS in Computer Science: https://coursera.org/degrees/ms-computer-science-boulder Course logo image courtesy of Lachlan Cormie, available here on Unsplash: https://unsplash.com/photos/jbJp18srifE
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Assignments are well designed guiding students to learn not only the concepts of data mining methods but also the necessary Python coding techniques.
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Overall this is a very practical course. One complain is that for the programming assignment, the python version in the Jupyter notebook is not the latest one. That would cause a waste of time in debugging since it does not support some codes and you could not figure out the cause of the failure from the auto grading.
Assignments are well designed guiding students to learn not only the concepts of data mining methods but also the necessary Python coding techniques.
extensive overview of data mining methods.
Informative course
good im satisfied
very helpful
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good
good
good
good
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Nice
good
Good
very useful course
GOOD
ok
The exercises are locked into a rigid evaluation pattern that is built like the teaching assistant expected the user to understand what they were doing and why. But the pattern diverges from a direct application of the knowledge reviewed in the lectures. The lectures are good summaries, but I highly advise supplementing the lectures with other reputable sources, buy a book, or research each topic. If you want to do well on the Exam, YOU MUST STUDY OTHER SOURCES. About half the exam is represented well in the lectures, the rest requires more in-depth independent study.