hn.academy

Online courses recommended by Hacker News users. [about]

Mathematical Biostatistics Boot Camp 1

Coursera · Johns Hopkins University · 3 HN citations

This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior ...

View on Coursera
The vast majority of the courses listed here on HN.Academy are available from their providers for free. Many courses offer a completion certification for a fee. A few courses and specializations require an enrollment fee. HN.Academy receives a referral commission when you visit course pages through links on this site and then purchase courses and completion certificates. If you decide to purchase a certificate or course the commission does not increase the cost of the course and helps support the continued existence of HN.Academy which is much appreciated.

Hacker News Comments about Mathematical Biostatistics Boot Camp 1

All the comments and stories posted to Hacker News that reference this course.
May 31, 2015 ipsin on Coursera is phasing out free certificates
The shame for me is that I occasionally used the "with distinction" grading to push myself where I might otherwise think I've done a "good enough" job understanding the content. It really helped push me over the line when taking the "Mathematical Biostats Boot Camp" series, which I enjoyed. https://www.coursera.org/course/biostats

It's silly, I know, but free certificates were the gamification of learning, and I'll miss that particular aspect of the game.

May 25, 2015 rz2k on Software development skills for data scientists
I thought they got better compared to the first few classes, but they do really revolve around R. For a rigorous treatment of the subject matter, the MITx course on Probability is really good. [1] You could also take a look at the two JHU "Mathemtical Biostatistics Bootcamp"[2] courses. Those are also quick compared to the MITx course, but a little more careful about the math than the courses in the data science specialization are.

I haven't ever used Spark, and I like R, but I am going to take the Berkeley/EdX course.

[1] https://www.edx.org/course/introduction-probability-science-...

[2] https://www.coursera.org/course/biostats & https://www.coursera.org/course/biostats2

Dec 09, 2014 osoba on Ask HN: How or where to begin learning mathematics from first principles?
I will try to list resources in a linear fashion, in a way that one naturally adds onto the previous (in terms of knowledge)

[PREREQUISITES]

First things first, I assume you went to a highschool, so you don't have a need for a full pre-calculus course. This would assume you, at least intuitively, understand what a function is; you know what a polynomial is; what rational, imaginary, real and complex numbers are; you can solve any quadratic equation; you know the equation of a line (and of a circle) and you can find the point that intersects two lines; you know the perimiter, area and volume formulas for common geometrical shapes/bodies and you know trigonometry in a context of a triangle. Khan Academy website (or simple googling) is good to fill any gaps in this.

[BASICS]

You would obviously start with calculus. Jim Fowlers Calculus 1 is an excellent first start if you don't know anything about the topic. Calculus: Single Variable https://www.coursera.org/course/calcsing is the more advanced version which I would strongly suggest, as it requires very little prerequisites and goes into some deeper practical issues.

By far the best resource for Linear Algebra is the MIT course taught by Gilbert Strang http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebr... If you prefer to learn through programming, https://www.coursera.org/course/matrix might be better for you, though this is a somewhat lightweight course.

[SECOND STEP]

After this point you'd might want to review single variable calculus though a more analytical approach on MIT OCW http://ocw.mit.edu/courses/mathematics/18-01sc-single-variab... as well as take your venture into multivariable calculus http://ocw.mit.edu/courses/mathematics/18-02sc-multivariable...

Excellent book for single variable calculus (though in reality its a book in mathematical analysis) is Spivaks "Calculus" (depending on where you are, legally or illegally obtainable here http://libgen.org/ (as are the other books mentioned in this post)). A quick and dirty run through multivariable analysis is Spivaks "Calculus on Manifolds".

Another exellent book (that covers both single and multivar analysis) is Walter Rudins "Principles of Mathematical Analysis" (commonly referred to as "baby rudin" by mathematicians), though be warned, this is an advanced book. The author wont cradle you with superfluous explanations and you may encounter many examples of "magical math" (you are presented with a difficult problem and the solution is a clever idea that somebody magically pulled out of their ass in a strike of pure genius, making you feel like you would have never thought of it yourself and you should probably give up math forever. (Obviously don't, this is common in mathematics. Through time proofs get perfected until they reach a very elegant form, and are only presented that way, obscuring the decades/centuries of work that went into the making of that solution))

At this point you have all the necessery knowledge to start studying Differential Equations http://ocw.mit.edu/courses/mathematics/18-03sc-differential-...

Alternativelly you can go into Probability and Statistics https://www.coursera.org/course/biostats https://www.coursera.org/course/biostats2

[FURTHER MATH]

If you have gone through the above, you already have all the knowledge you need to study the areas you mentioned in your post. However, if you are interested in further mathematics you can go through the following:

The actual first principles of mathematics are prepositional and first order logic. It would, however, (imo) not be natural to start your study of maths with it. Good resource is https://www.coursera.org/course/intrologic and possibly https://class.stanford.edu/courses/Philosophy/LPL/2014/about

For Abstract algebra and Complex analysis (two separate subjects) you could go through Saylors courses http://www.saylor.org/majors/mathematics/ (sorry, I didn't study these in english).

You would also want to find some resource to study Galois theory which would be a nice bridge between algebra and number theory. For number theory I recommend the book by G. H. Hardy

At some point in life you'd also want to go through Partial Differential Equations, and perhaps Numerical Analysis. I guess check them out on Saylor http://www.saylor.org/majors/mathematics/

Topology by Munkres (its a book)

Rudin's Functional Analysis (this is the "big/adult rudin")

Hatcher's Algebraic Topology

[LIFE AFTER MATH]

It is, I guess, natural for mathematicians to branch out into:

[Computer/Data Science]

There are, literally, hundreds of courses on edX, Coursera and Udacity so take your pick. These are some of my favorites:

Artificial Intelligence https://www.edx.org/course/artificial-intelligence-uc-berkel...

Machine Learning https://www.coursera.org/course/ml

The 2+2 Princeton and Stanford Algorithms classes on Coursera

Discrete Optimization https://www.coursera.org/course/optimization

Convex Optimization https://itunes.apple.com/itunes-u/convex-optimization-ee364a... https://itunes.apple.com/us/course/convex-optimization-ii/id...

[Physics]

http://theoreticalminimum.com/courses

http://ocw.mit.edu/courses/physics/

Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
HN.Academy is an independent project and is not managed or owned by Y Combinator, Coursera, edX, or any of the universities and other institutions providing courses.
~ [email protected]
;laksdfhjdhksalkfj more things
hn.academy ~ Privacy Policy ~