Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.
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I think the amount of course work to lectures was more appropriate than the first segment. I enjoyed the exercises and felt that they mixed the correct amount of theory and applicaiton.
Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.
Excellent Lectures. Since the course is several years old the organization of some of the assignments needs updating. That's the only reason I gave it 4 instead of 5 stars.
I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .
The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course
Nive that the course covered a broad range of topics.\n\nAnd good to get pushed to do some kaggle competition and peer review.
A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.
Need some background in R or Python and the lectures are from around 2013. Most of the material is still relevant.
關於 大规模数据科学 專項課程