Path: Data Analysis in R [closed] - r

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I am building a path for beginners (like me) to guide them through the learning of data analysis in R (Only in R please).
Would you suggest me any new sections and/or new courses that i should add?
Heres is what i have been adding till now: http://studiy.co/path/data-analysis/
Thanks for the help!

well you need to distinguish... If you want just to learn R programming you may check Coursera course on R
https://www.coursera.org/learn/r-programming
that would give you some basics. If you need to learn how to analyse data then you may need a bit more. What are your background in mathematics? What kind of statistics know-how do you have? A GREAT resource in my opinion is the book
An Introduction to Statistical Learning with applications in R
That you can even find for free in pdf (http://www-bcf.usc.edu/~gareth/ISL/). Beware that you may need some mathematics background to be able to understand it fully. In case you have a more profound background in science I may have other suggestions. Could you tell us at what level are you?
Hope that helps.

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Data science project example [closed]

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Do anybody know about a github repo with a full well organized data science project? Preferable in Python. My hobby project often get mezzy with a mix of Python code and notesbook. A worked out project is the best way to learn some new tricks.
Data Science is regarded a bit differently by different people, so you might consider focusing on what exactly you wish to learn.
But, take a look at those:
https://github.com/bulutyazilim/awesome-datascience
https://www.kaggle.com/
The first one contains lots of relevant sources of information. The second is originally a competition site with varied different problems in ML, but also contains past competitions (and datasets). They added a cool feature called "kernels" which are just code files people publish and you could learn from those.

How necessary are mathematics in programming? [closed]

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I read on many forums for "Do I need mathematic-skills to code?" (and such), but my question is slightly different.
What I want to know is: How much mathematics do you really need to graduate (with high marks) in 'Computer Engineering' or 'Information Technology' and is it hard?
By the way, I love mathematics, eventhough I'm not good enough at it (I won 3 math olympiads but I struggle from self-esteem).
To graduate - You'll need it for sure. An easy example are integrals, derivatives and stuff like that. A lot of subjects, at least in my university requires a basic understanding of maths.
Another thing is what kind of a programmer you wanna be. Sure I guess you do not always have to use math's while writing HTML code or so.
On the other hand - for algorithmic stuff you couldn't get away without it :)
all in all, To graduate in IT you have to know math, but to code, not always.

Making a random tone-sequence in R [closed]

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I would like to generate random tone sequences in R (likewise https://en.wikipedia.org/wiki/Twelve-tone_technique) for creating rule-based items. I'm interested, if someone could suggest R packages, which could sonify data for making melodies based on number sequences (for pitch and duration of a tone).
Here is a package to analyze music, that might help.
Here is an example you can try out.
Replicating something similar like the answer to this questions is maybe rather what you want. There the OP requests code to play the happy-birthday song in R. They code in the answer has, notes, pitch and duration, that you can easily define by yourself and sample randomly from to generate a song.
I think that it might be simpler to do most of these things in python.
Hope this helps!

mathematical-optimization tutorials [closed]

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Can someone help me with some tutorials on how I can learn to formulate optimization problems in R?
I am mainly looking for 2d optimization problems. Until now I have found a lot of info on optimization algorithms, however almost nothing on how to implement them with 1 or 2 examples.
Well, the R task guides are always a good place to start.
In addition, there was a recent article on the optimx package in R
Also, if you load up some packages listed in the task guide, then you will probably find some useful vignettes (I've learned an awful lot of stats from those vignettes over the years).

What are some good resources for GNU Make? [closed]

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I have read about 1/3 of the GNU Make manual, do you guys know of any resources to really learn the Art of using Make.
Ideally, there might be some examples where two ways of doing something are possible, and the author explains which method he chose and why.
I am primarily using Make not to build C/C++ programs but to operate a processing pipeline for data analysis.
Managing Projects with GNU Make, by Robert Mecklenburg, is the best I've come across. Plus, it's an O'Reilly Open Book, so O'Reilly gives away free PDFs of it on their website. Read it linearly, and start from the beginning (even if you think you already know the basics of GNU Make).
John Graham-Cumming has written a book called GNU Make Unleashed which looks promising, although I have not read it yet. http://www.lulu.com/product/paperback/gnu-make-unleashed/2937580 and http://jgc.org/

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