Reddit reviews An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
We found 19 Reddit comments about An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics). Here are the top ones, ranked by their Reddit score.
I've posted a similar answer before, but can't find the comment anymore.
If you are interested in doing your own statistics and modeling (like regression modeling), learn R. It pays amazing dividends for anyone who does any sort of data analysis, even basic biostats. Excel is for accountants and is terrible for biological data. It screws up your datasets when you open them, has no version control/tracking, has only rudimentary visualization capabilities, and cannot do the kind of stats you need to use the most (like right-censored data for Cox proportional hazards models or Kaplan-Meier curves). I've used SAS, Stata, SPSS, Excel, and a whole bunch of other junk in various classes and various projects over the years, and now use only R, Python, and Unix/Shell with nearly all the statistical work being in R. I'm definitely a biased recommender, because what started off as just a way to make a quick survival curve that I couldn't do in Excel as a medical student led me down a rabbit hole and now my whole career is based on data analysis. That said, my entire fellowship cohort now at least dabbles in R for making figures and doing basic statistics, so it's not just me.
R is free, has an amazing online community, and is in heavy use by biostatisticians. The biggest downsides are
Unfortunately learning R won't teach you actual statistics.... for that I've had the best luck with brick-and-mortar classes throughout med school and later fellowship but many, many MOOCs, textbooks, and online workshops exist to teach you the basics.
If I were doing it all over again from the start, I would take a course or use a textbook that integrated R from the very beginning such as this.
Some other great statistical textbooks:
Online classes:
So many to choose from, but I am partial to DataCamp
Want to get started?
>prompt in the console:install.packages("swirl")library("swirl")swirl()And you'll be off an running in a built-in tutorial that starts with the basics (how do I add two numbers) and ends (last I checked) with linear regression models.
ALL OF THAT SAID ------
You don't need to do any of that to be a good doctor, or even a good researcher. All academic institutions have dedicated statisticians (I still work with them all the time -- I know enough to know I don't really know what I am doing). If you can do your own data analysis though, you can work much faster and do many more interesting things than if you have to pay by the hour for someone to make basic figures for you.
I've never heard of that book before, but I took a look at their samples and they all seem legitimate.
I would just buy the Ebook for $59 and work through some problems. I'd also maybe purchase some books (or find free PDFs online). Given that you don't have a deep understanding of ML techniques I would suggest these books:
There are others as well, but those are two introductory-level textbooks I am familiar with and often suggested by others.
This is a great book. The other book that is a bit less mathematical in nature, and covers similar topics, is Introduction to Statistical Learning. It is also a good one to have in your collection if you prefer a less mathematical treatment. https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370
100x though, that's a bit much :) If you read effectively and take notes effectively, you should only have to go through this book with any depth 1 time. And yes, I did spend time learning how read books like this, and it's worth learning!
It is like any other job, if not harder. You are entirely responsible for your decisions here. No boss to complain of, no sabotaging co-workers to blame. Just you and your decisions. And it will demand your devotion beyond the 9-5 job. You'll be on charts and reading analyses during weekends, trying to understand the political environment surrounding the instrument you are trading. And still, you may (or will) fail. Markets gonna do what markets gonna do. The only variable in your control is your reaction to it.
To get a feel of what kind of stuff you would be dealing with, check out some books that have a more rigorous foundation for trading:
The last one is not too important for Forex, but it is necessary to better understand other financial instruments and appreciate the deeper foundations of Finance.
I think books 1 & 2 are absolutely necessary. Consider these as "college textbooks" that one must read to "graduate" in trading. May be thrown in Technical Analysis of the Financial Markets, so you get the "high school" level knowledge of trading (which is outdated, vague, qualitative and doesn't work). We are dealing with radical uncertainty here (to borrow a phrase from The End of Alchemy), and there needs to be some way for us to at least grasp the magnitude of what few uncertain elements we can understand. Without this, trading will be a nightmare.
The best to start with Theoretical understanding would be: The Elements of Statistical Learning: Data Mining, Inference, and Prediction
If you prefer to understand along with computation implementation, go with this: An Introduction to Statistical Learning: with Applications in R
First 6 weeks of Andrew Ng's [basic ML course] (https://www.coursera.org/learn/machine-learning), while reading Intro to Statistical Learning, for starters (no need to implement exercises in R, but it is a phenomenal book).
From there you have choices (like taking the next 6 weeks of Ng's basic ML), but for Deep Learning Andrew Ng's [specialization] (https://www.coursera.org/specializations/deep-learning) is a great next step (to learn CNNs and RNNs). (First 3 out of 5 courses will repeat some stuff from basic ML course, you can just skip thru them).
To get into the math and research get the Deep Learning book.
For Reinforcement Learning (I recommend learning some DL first), go through this [lecture series] by David Silver (https://www.youtube.com/watch?v=2pWv7GOvuf0) for starters. The course draws heavily from this book by Sutton and Barto.
At any point you can try to read papers that interest you.
I recommend the shallower, (relatively) easier online courses and ISLR because even if you are very good at math, IMO you should quickly learn about various topics in ML, DL, RL, so you can hone in on the subfields you want to focus on first. Feel free to go through the courses as quickly as you want.
This one? linky
The Nature of Computation
(I don't care for people who say this is computer science, not real math. It's math. And it's the greatest textbook ever written at that.)
Concrete Mathematics
Understanding Analysis
An Introduction to Statistical Learning
Numerical Linear Algebra
Introduction to Probability
I read "Applied Predictive Modeling" by Kuhn. It has R-Labs at the end of each chapter with data. Very useful. I also read the R for Data Science and found this a good complement to it.
APM gives you enough of the theory behind the models to get a good understanding.
There's also the book "Intro To Statistical Learning" that is sort of like an abridged version of APM. Focuses just on the methods and R-Labs. Doesn't get too much into the theory and keeps it as a black box.
So if you want a deeper dive and have some time, I suggest APM, if you are on a time crunch, I'd suggest ISL
I think they're also available partially free on github. But I prefer having the book for quick reference.
Data Smart
Whole book uses excel; introduces R near the end; very little math.
But learn the theory (I like ISLR), you'll be better for it and will screw up much less.
Although I am not a statistician myself and given your background, some of my recommendations would be:
This should probably be enough for now but if you need more recommendations just say so :)
Regarding the time series question, it's not my area of expertise but since time series analysis ends up employing many statistical methods, I think it can be considered an area of statistics (Statisticians around here correct me if I am wrong :P)
If OP doesn't have the possibility of taking a statistical learning class, ISL is a good introduction.
If anyone's interested, this book here is a really good free introductory textbook on machine learning using R. It has really good reviews that you can see here
Also if you need answers to the exercises, they're here
The textbook covers pretty much everything in OP's article
This. The book that accompanies these videos link is one of my main go-to's. Very well put together. Great examples.
Another real good book is Practical Data Science with R.
I'm not sure what language the John's Hopkins Coursera Data Science courses is done in, but I'd imagine either R or Python.
Because, based on your initial comment and this one as well the learning curve in front of you is ... steeper than you might think.
I think you are jumping in to the real deep end, without starting with some fundamentals. The point these questions are at I would just recommend grabbing a book on Linear Regression. If you already have a strong math background them you could jump to something like https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370/ref=pd_sim_14_1?ie=UTF8&psc=1&refRID=086FTQPDGGERBQ7ZR2C5
But I often see people walk away from that book misunderstanding some of the assumptions behind the models they are building and trying to make very poor predictions. Inference is another story all to itself...
http://www.urbandictionary.com/define.php?term=laughing&defid=1568845 :))
Now, seriously, if you want to get started, I'd recommend this for R (http://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370/) and this for Python (http://www.amazon.com/Python-Machine-Learning-Sebastian-Raschka/dp/1783555130//).
Also, head out to /r/datascience and /r/MachineLearning!
EDIT: Wrong link.
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) https://www.amazon.com/dp/1461471370/ref=cm_sw_r_cp_apa_i_4BRVCb76G28M3
https://www.amazon.de/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370
Is this the right book: https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370
What about this one? https://www.amazon.com/gp/product/0387848576/ref=ox_sc_sfl_title_9?ie=UTF8&psc=1&smid=ATVPDKIKX0DER