Are you interested in Data Science but lack the math background for it? Has math always been a tough subject that you tend to avoid? This course will provide an intuitive understanding of foundational integral calculus, including integration by parts, area under a curve, and integral computation. It will also cover root-finding methods, matrix decomposition, and partial derivatives.
This course is designed to prepare learners to successfully complete Statistical Modeling for Data Science Application, which is part of CU Boulder's Master of Science in Data Science (MS-DS) program. Logo courtesy of ThisisEngineering RAEng on Unsplash.com
SR
fast paced and minimal but very clear with good teaching
KC
Teaching is good, but should include more practice questions.
PJ
This was an excellent course. Covered some very technical aspects of both derivatives and linear algebra cleanly, and the quizzes made sense.
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One of the best lectures I have attended in the applied mathematics combining to numerical analysis in optimization and data science. Whole Specialization gives the essential tool set in Mathematics and Modeling in Data Science by case mathematical examples.
This was an excellent course. Covered some very technical aspects of both derivatives and linear algebra cleanly, and the quizzes made sense.
Excellent and thorough teaching
Finally something not basic
Thank you very much
very usefull
great course
Nice :)
IS GOOD
good
n
Good course
This course felt lacking in concepts and quizzes material. I think this course should be redone so that the teaching material can be made better.
I felt like the the linear algebra in this course was out of place for this course. Should've put it in the linear algebra course.
Too simple and you can finish this course in only a few hours. Definitely not for having a thorough understanding of the topics.
Quick last course of the specialization. Great way to freshen up on integrals and partial derivatives, with a short intro of SVD and gradients. Would have liked a deeper intuitive explanation of the uses of SVD but it's okay for a quick overview.
Teaching is good, but should include more practice questions.
fast paced and minimal but very clear with good teaching
A decent course. Lots of mistakes and the last module seems rushed. Got a taste of concepts such as gradient descent, directional vectors, eigenvectors, etc., but didn't feel like the intuition behind these techniques was fleshed out very well.
They present very basic but important materials