Mathematical Biostatistics Boot Camp 1
Below are the top discussions from Reddit that mention this online Coursera course from Johns Hopkins University.
This class presents the fundamental probability and statistical concepts used in elementary data analysis.
Statistics Confidence Interval Statistical Hypothesis Testing Biostatistics
Next cohort starts July 27. Accessible for free. Completion certificates are offered.
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Taught by
Brian Caffo, PhD
Professor, Biostatistics
Offered by
Johns Hopkins University
Reddit Posts and Comments
0 posts • 17 mentions • top 13 shown below
21 points • geebr
Cover the basic distributions  Gaussian, binomial, Poisson, chisquared, exponential (these latter two are both just special cases of gamma). Develop a good understanding of confidence intervals, summary statistics, p values, and moments of distributions. The central limit theorem is enormously useful so make sure you have a good handle on that. Understand hypothesis testing using both parametric and nonparametric approaches. That means also understanding bootstrapping, permutation tests, and Monte Carlo simulations. In my experience, you need to have enough background to find a satisfying answer to the question "what does chance look like?". In my head at least, those concepts form the core of a data science stats curriculum. I heartily recommend Mathematical Biostatistics Bootcamp for getting a better understanding of distributions and hypothesis testing. The course doesn't really cover nonparametric methods much though, as far as I can remember.
12 points • adventuringraw
I found value in this course, but to be honest... I think a MOOC is likely a poor way to learn math. After all, whatever you're learning (math, coding, whatever) your main learning time is when you're in the trenches solving problems, ideally with access to help when you need it. For CS stuff MOOCS seem great. A class based around what amounts to 10 problems (code up the feed forward and back propagate part of this neural network. Use this technique to predict this target variable with this data set, etc) all take a fair bit of time, and get you thinking about a lot of different sides of your craft.
Math on the other hand, it seems like most problems (until you're pretty high level at least) are going to be more run and gun. Your linear algebra will be solid when you've cranked through a few hundred problems covering different techniques, you know? So... what I've done, I picked out some textbooks with accessible solution manuals, a lot of useful practice problems (ideally more geared towards probing deep understanding instead of math busy work) and just... you know. Cranked through. I feel decent about my stats knowledge now, I'm currently working hard to shore up my linear algebra, heading towards matrix calculus. I got a ways into this and realized I need a little more background, haha.
Which brings me to my next thought... math is far easier for me to learn at least, when I have a concrete goal. I'm not actually all that interested in math as a thing in and of itself, but I'm extremely interested in anything that'll give me new insight when solving complex problems. You might find that you have narrow pockets of math you need to pick up now, and don't actually need to go through whole courses or anything.
If you need low level stuff though (basic stats, intro to linear algebra, basic calc, etc.) then Kahn's Academy's probably your best bet, but obviously you'll run way outside the course has to offer pretty quick if you're interested in getting into white papers and such.
8 points • MaiLittlePwny
Coursera do a course https://www.coursera.org/learn/biostatistics/home/welcome bio statistics that uses R pretty heavily. Haven't completed it so don't know how in depth it goes though.
10 points • adventuringraw
I've been working my way through for the last six months, starting with a great foundation in linear algebra and calc, and basically zero statistics.
I learned basic stats from Kahn's Academy. It's not perfect, I prefer a more problem centric approach instead of lecture centric (lectures are uninteresting unless I actively know I need the information to solve what's in front of me) but... it'll get you through. I suggest doing the unit test at the beginning of each section, and skipping through, only slowing down when you encounter problems you don't know how to solve.
You'll likely encounter other areas you're weak on. Make notes. You'll end up with an insane rabbit trail, you need to stay organized and not get distracted. (i.e, don't unintentionally set down stats for a month to bone up on infinite series convergence, just to understand the poisson distribution derivation).
If you're weak on linear algebra or calc, hit those from Kahn's Academy as well.
As an alternative, I really have been enjoying brilliant. It's a problem centric approach, providing only as much instruction as you need to tackle the next problem. The elegance is in problem selection... they've done a great job guiding you through to more complex areas, without getting bogged down.
When you're ready, the coursera John Hopkins biostats course is the only free mook I've ever seen with serious math. It's no joke though, if you aren't solid on your calc and series algebra, save this for a year from now. It took me six months between starting it the first time, and finding I finally had enough foundation to actually complete the course. Some of those problems are fucking rough.
In my view, math comes down to two things: deep understanding, and practice. That means practice problems to drill down things you already mostly understand (Hogg's intro to mathematical statistics is great if you want a million problems to pound through. Make sure you get an edition with solutions easily available). 3blue1brown is great for getting an awesome intuitive sense of what's going on with some complex areas (his backprop videos were especially helpful I thought, and his primer on linear algebra is great).
One last, interesting one... I'm a huge fan of having an intuitive understanding of how things fit together. There's not much support for that, it's expected that you'll work through problems until eventually you start to have pieces click together. In practice, that doesn't always work out... so an incredibly helpful resource, is the infinite napkin project. It's basically an attempt to make higher level math accessable to gifted high school students (going up to complex analysis, differential geometry, and all kinds of other shit you don't necessarily need) and his section on linear algebra is fucking gold.
Last piece... and this one's a strange one: check out Anki. It's a flash card program for language learning, but I've found it to be invaluable for learning and retaining the massive amount of math I've crammed into my head in the last six months. I especially like noting down proofs to rederive later for review. It's an art to know what cards to bother making (intuitive understanding is the goal far more so than rigour in my view, so you don't want to waste time with anything that won't contribute... but you also don't want to risk forgetting a key piece that you'll need six months later to understanding something new) and Anki's been an incredible way to automate review so I can quickly plow ahead without forgetting as I go. The only downside... you'll need to figure out LaTex, and it's nontrivial to get it set up, and even then you'll need to learn the syntax (facilitated with Anki, haha). It's optional, but I wouldn't have been able to achieve what I've achieved without it. Anki's also brilliant for leaning new APIs and languages quickly.
I haven't gone through them yet, but I'm also excited about Linear Algebra Done Right, and the elements of statistics learning, and obviously the deep learning book... if only I had access to a DBZ style hyperbolic time chamber. Ah well. For what it's worth, linear algebra seems like the most important area by far. If you get far enough to have a deep, intuitive understanding of, say, PCA, you'll probably be mostly far enough to function I imagine, though I'm convinced a deep understanding of statistical analysis is valuable as well, if just to build up an intuition for how to think about data.
6 points • ruslankl
I enjoyed these two resources:
 Mathematical Biostatistics Boot Camp pt.1 and pt.2 (Coursera)
 Biostatistics and Epidemiology Lecture Series (YouTube)
1 points • sagar_r_parmar
This course from coursera may feel a bit familiar to you based on your background https://www.coursera.org/learn/biostatistics
5 points • AstroZombie138
I am not into biostats, but I thought the biostats bootcamp from Johns Hopkins was interesting (it is a video lecture series, not a book)
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https://www.coursera.org/learn/biostatistics
and some on youtube here: https://www.youtube.com/watch?v=jkUqDVtpKs4&list=PLplgQkQivXhk6qSyiNj51qamjAtZISJ
​
15 points • NicolasGuacamole
[Undergrad General] Trying to plan out a selfstudy degree
Hi. I have nearly completed my degree in a different subject and would like to essentially study for a second degree, but this time in Mathematics. The trouble is that I can't afford to study it formally. As such I'm looking to put together a personal course of study. So far, with a great deal of advice I've come out with this:
1st year 

Stanford  Mathematical thinking https://www.coursera.org/course/maththink

MIT  Algebra 1 http://ocw.mit.edu/courses/mathematics/18701algebraifall2010/index.htm

Ohio State  Calculus 1 https://www.coursera.org/learn/calculus1

MIT  Linear Algebra http://ocw.mit.edu/courses/mathematics/1806sclinearalgebrafall2011/

MIT  Intro to Probability and Statistics http://ocw.mit.edu/courses/mathematics/1805introductiontoprobabilityandstatisticsspring2014/index.htm

MIT  Algebra 2 http://ocw.mit.edu/courses/mathematics/18702algebraiispring2011/index.htm

Ohio State  Calculus 2 (Sequences and Series) https://www.coursera.org/learn/advancedcalculus

John Hopkins  Mathematical Biostatistics 1 https://www.coursera.org/learn/biostatistics
2nd year 

MIT  Calculus with Applications http://ocw.mit.edu/courses/mathematics/18013acalculuswithapplicationsspring2005/

MIT  Differential Equations http://ocw.mit.edu/courses/mathematics/1803scdifferentialequationsfall2011/

MIT  Calculus of Several Variables http://ocw.mit.edu/courses/mathematics/18022calculusofseveralvariablesfall2010/

MIT  Partial Differential equations http://ocw.mit.edu/courses/mathematics/18152introductiontopartialdifferentialequationsfall2011/

UoM  Complex Analysis http://www.maths.manchester.ac.uk/~cwalkden/complexanalysis/complexanalysis.html

MIT  Fourier Analysis http://ocw.mit.edu/courses/mathematics/18103fourieranalysisfall2013/
If there is any input/advice anyone could give me, as to what I could add to bring this closer to a 'real' degree I would be very grateful.
2 points • Sarcuss
Yes, these are more applied statistics courses. If you want to delve deeply in the math, I think you would be well served with Mathematical Biostatistics part 1 and Part 2 or a textbook such as Mathematical Statistics with Applications by Wackerly et al.
1 points • gma617
I've considered it and I do think it would be fulfilling and lucrative potentially. But I don't have the baseline coursework from my undergrad (no biology, just standard stats). I did take a MOOC biostatistics course (https://www.coursera.org/learn/biostatistics) and I'm currently taking a python machine learning course (https://emeritus.org/universitycoursesonline/appliedmachinelearning/) so maybe with this helps my chances of being accepted into a masters program. Still feels like a long shot. What do you think the highestpaying career paths in bioinformatics would be? Wall Street can always just hire BME or Quantitative Finance PHDs, and it's not the time to enter equity research. Salary ranges I'm seeing for bioinformatics scientist are quite low, and that job would not utilize my peoplefacing skills developed in consulting. I must be overlooking something...
2 points • alejo_sc
I did a lot of online review to prepare me for my Masters program, mostly through Coursera. They have a lot of great Biostats and Data Science courses:
Basic Statistics  University of Amsterdam
Epidemiology: The Basic Science of Public Health  University of North Carolina  Chapel Hill
Statistical Reasoning for Public Health  Johns Hopkins University
Mathematical Biostatistics Bootcamp Johns Hopkins University
I don't know of any resources for developing your SPSS skills, but Datacamp helped me a ton with learning R.
1 points • NicolasGuacamole
Undergrad level selfstudy degree advice
Hi. I have nearly completed my degree in a different subject and would like to essentially study for a second degree, but this time in Mathematics. The trouble is that I can't afford to study it formally. As such I'm looking to put together a personal course of study. So far, with a great deal of advice I've come out with this:
1st year 

Stanford  Mathematical thinking https://www.coursera.org/course/maththink

MIT  Algebra 1 http://ocw.mit.edu/courses/mathematics/18701algebraifall2010/index.htm

Ohio State  Calculus 1 https://www.coursera.org/learn/calculus1

MIT  Linear Algebra http://ocw.mit.edu/courses/mathematics/1806sclinearalgebrafall2011/

MIT  Intro to Probability and Statistics http://ocw.mit.edu/courses/mathematics/1805introductiontoprobabilityandstatisticsspring2014/index.htm

MIT  Algebra 2 http://ocw.mit.edu/courses/mathematics/18702algebraiispring2011/index.htm

Ohio State  Calculus 2 (Sequences and Series) https://www.coursera.org/learn/advancedcalculus

John Hopkins  Mathematical Biostatistics 1 https://www.coursera.org/learn/biostatistics
2nd year 

MIT  Calculus with Applications http://ocw.mit.edu/courses/mathematics/18013acalculuswithapplicationsspring2005/

MIT  Differential Equations http://ocw.mit.edu/courses/mathematics/1803scdifferentialequationsfall2011/

MIT  Calculus of Several Variables http://ocw.mit.edu/courses/mathematics/18022calculusofseveralvariablesfall2010/

MIT  Partial Differential equations http://ocw.mit.edu/courses/mathematics/18152introductiontopartialdifferentialequationsfall2011/

UoM  Complex Analysis http://www.maths.manchester.ac.uk/~cwalkden/complexanalysis/complexanalysis.html

MIT  Fourier Analysis http://ocw.mit.edu/courses/mathematics/18103fourieranalysisfall2013/
If there is any input/advice anyone could give me, as to what I could add to bring this closer to a 'real' degree I would be very grateful.
1 points • manohar_v9
Bioinformatics Resources
I have been trying to collect as many resources as I can since yesterday to get started with bioinformatics. Searched in Reddit and Google and got these links. I stopped when I felt I have more than what I need for present, thus this list is by no means exhaustive. I haven't given much thought into organising and there might be duplicates. Most of them are from reddit articles written on getting into bioinformatics, they are the ones who deserve the credit
Anyone reading please comment resources you know that deserve to be here. Happy learning :)
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Bioinformatics resources
Major online course platforms: coursera.org edx.org
Coursera:
https://www.coursera.org/learn/bioinformatics
https://www.coursera.org/learn/bioinformaticspku
https://www.coursera.org/learn/genomicdata
https://www.coursera.org/learn/genomesequencing
https://www.coursera.org/learn/comparinggenomes
https://www.coursera.org/learn/dnamutations
https://www.coursera.org/learn/dnaanalysis
https://www.coursera.org/learn/bioinformaticsmethods1
https://www.coursera.org/learn/networkbiology
https://www.coursera.org/learn/algorithmspart1
https://www.coursera.org/specializations/datascience
https://www.coursera.org/specializations/dataanalysis
https://www.coursera.org/learn/datascientiststools
https://www.coursera.org/learn/biostatistics
https://www.coursera.org/learn/machinelearning
https://www.coursera.org/learn/interactivepython1
https://www.coursera.org/learn/rprogramming
Youtube:
https://www.youtube.com/watch?v=lhlBWlhS7Vg&list=PLhQjrBD2T383Q2VtqEaQn8nZh681av20
https://www.youtube.com/watch?v=ZbYiYuwRFM&feature=youtu.be
https://www.youtube.com/playlist?list=PLpPXw4zFa0uKKhaSz87IowJnOTzh9tiBk
https://www.youtube.com/channel/UCK1yol7I6ZQXZxZOnjo2zzw (Biopandit)
Datacamp:
https://www.datacamp.com/courses/biomedicalimageanalysisinpython
https://www.datacamp.com/courses/singlecellrnaseqwithbioconductorinr
https://www.datacamp.com/courses/introductiontobioconductorinr
https://www.datacamp.com/courses/rnaseqwithbioconductorinr
https://www.datacamp.com/courses/chipseqwithbioconductorinr
Reddit:
https://www.reddit.com/r/learnpython/wiki/index
https://www.reddit.com/r/learnbioinformatics/wiki/index
https://www.reddit.com/r/bioinformatics/comments/d7ot3w/my_longterm_learning_plan/
Amazon.com:
https://www.amazon.com/MolecularBiologyGeneJamesWatson/dp/0321762436
https://www.amazon.com/IntroductionAlgorithms3rdThomasCormen/dp/0262033844
https://www.amazon.com/BioinformaticsProgrammingUsingPythonBiological/dp/059615450X
https://www.amazon.com/LinuxCommandLineCompleteIntroduction/dp/1593273894
https://www.amazon.com/ArtProgrammingStatisticalSoftwareDesign/dp/1593273843
Documentations:
https://docs.oracle.com/javase/tutorial/java/concepts/
https://www.perl.org/books/beginningperl/
Articles:
http://www.bioinformaticscareerguide.com/2018/01/getbioinformaticseducationonlinefor.html
Web:
https://github.com/ossu/bioinformatics
https://github.com/ossu/datascience#curriculum
http://rosalind.info/problems/astable/
https://www.ncbi.nlm.nih.gov/home/tutorials/
http://korflab.ucdavis.edu/Unix_and_Perl/index.html
https://code.snipcademy.com/tutorials/linuxcommandline
http://rcourse.iop.kcl.ac.uk/2011/
http://www.rtutor.com/rintroduction
https://code.snipcademy.com/tutorials/linuxcommandline
https://code.snipcademy.com/tutorials/shellscripting
https://softwarecarpentry.org/
University websites:
https://advanced.jhu.edu/academics/graduatedegreeprograms/bioinformatics/degreerequirements/
https://ucsd.edu/catalog/curric/BIOIgr.html
https://ocw.mit.edu/courses/biology/701scfundamentalsofbiologyfall2011/
http://faculty.marshall.usc.edu/garethjames/
https://web.stanford.edu/\~hastie/ElemStatLearn//
Googling:
https://nptel.ac.in/courses/102106065/
http://bozeman.mbt.washington.edu/compbio/mbt599/
http://people.csail.mit.edu/bab/class/0118.417home.html
https://courses.cs.washington.edu/courses/cse527/01au/
https://edwards.sdsu.edu/research/teachingbioinformatics/
https://stepik.org/course/55789/promo
https://stepik.org/course/2/promo
https://edu.tbio.info/course/introductionbioinformatics/
https://chagall.med.cornell.edu/BioinfoCourse/SuggestedReading.html