The best
Mathematical Physics books:
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Top Reddit reviews mentioning Mathematical Physics books:
I'm not sure how advanced your statistical background is yet, but the best purchase I ever made was <strong>An Introduction to Statistical Learning: with Applications in R</strong> by Hastie et. al.
It gives you a basic, intuitive background on various machine learning methods without getting into nitty gritty probability or statistical theory. And it has really helpful problem sets at the end of each chapter that shows you how to apply each of them in R, and which packages you'll need.
Seriously, that thing is like my bible. The authors have made a pdf available on the internet as well, but I'd highly suggest springing for a hard copy. It's pretty cheap as far as textbooks go.
Other than that, I've never been one to learn through online courses or books. I'd second /u/veeeerain and just do a bunch of projects using datasets from sources like Kaggle. Maybe start a blog to keep a portfolio of all the cool things you do. ;)
I'd look at applying to S2DS if you can get in. C++ and ROOT have very little relevance nowadays in data science (or, ever, really). Knowing the fundamentals of some of the ML algorithms in ROOT wont hurt you, but you need to learn scikit-learn, numpy, scipy, etc in Python as a bare minimum.
Plenty of (free) courses on Coursera, too.
It's an extremely competitive market, and whilst some of the stuff she's done will be useful, she'll be (in many employers' eyes, at least) behind the people graduating with Masters in Data Science/Comp Sci.
Recommend this, too:
https://www.amazon.co.uk/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370/ref=asc\_df\_1461471370/?tag=googshopuk-21&linkCode=df0&hvadid=310848077451&hvpos=&hvnetw=g&hvrand=3789835037830509153&hvpone=...
“Flatland” is a very thin book and worth the read. Don’t even need to check it out of a library, you can finish it in a few hours.
https://www.amazon.com/Flatland-Romance-Dimensions-Thrift-Editions/dp/048627263X
The Elegant Universe did a pretty decent job of explaining it in terms I could understand. I think the author Brian Greene, has a documentary on YouTube too.
Check this book out: Chapter 18 - Special Functions
You can also find it for free on the Genesis Library.
Will do, and thanks for the recommendation. One in return, just because I think you may find it interesting (And also because I don;t like to leave recommendations unreciprocated), "The Holographic Universe", by Michael Talbot. :)
I think that many of your problems arise because of a flawed understanding of what probability is. Can I recommend this excellent book by ET Jaynes? It is written from the perspective of a Bayesian, and as a someone working in quantum information, there are multiple issues on which I have some disagreements with Jaynes, but it is an excellent and life-changing book nevertheless.
>Pls to point out conclusive proof doesn't say that Kejriwal is dishonest.
Well the burden of proof is on the one making the claim, or in this case you. It is like you have picked one interpretation of OP's statements, you can show that that interpretation is fallacious and therefore you just want to stick to that interpretation. I have just shown you that if you are a little generous, his statements make sense.
Again, all of this discussion is kind of pointless if you agree that given all that we know about Kejriwal, it is fair to say that it is pretty likely that he is not a very honest person.
More elegant but not necessarily better
I found it. It's from the book, The Drunkard's Walk. It's a 269-game series. Here's a link to the book. It's one of the best books I've ever read. Definitely a big recommend, especially to fellow stats nerds.
There's a cool little book called "Flatland - A Romance of Many Dimensions" which describes a group of creatures living in a 2D just as you describe.
https://www.amazon.com/Flatland-Romance-Dimensions-Thrift-Editions/dp/048627263X
Bro if you want a math book I recommend https://www.amazon.in/Mathematical-Methods-Physics-Engineering-Comprehensive/dp/0521679710 I did that to strengthen my linear algebra and vector calculus after doing the MIT ocw courses, will be useful for college too
You know, I’ve thought something similar for a while…
It’s really amazing to hear others think it too. Something I’m reading right now that really supports the idea is the book:
Okay thats fair.; Exact category is often debated.
The less debated fields don't have a replication crisis per se: They have a falsifiability crisis.
https://blogs.scientificamerican.com/cross-check/how-physics-lost-its-fizz/
If you read my comment I gave you two books I recommend:
https://www.amazon.com/Introduction-Statistical-Learning-Applications-Statistics/dp/1461471370
https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646
It's a bit advanced and doesn't hold your hand, so don't beat yourself up if it takes a long time or is difficult. But if you work through Mathematical Methods for Physics and Engineering by K.F. Riley et. al. step by step and really follow their reasoning you will have an extremely strong basis to attack any problem and learn more if you need it. Most bachelors degrees won't give you as good a basis as this book. I credit my success in math to this book, can't recommend highly enough https://www.amazon.co.uk/Mathematical-Methods-Physics-Engineering-Comprehensive/dp/0521679710
The Road to Reality: A Complete Guide to the Laws of the Universe by Roger Penrose. Talks about if/how math can describe the physical world, each chapter building off the last.
Data Science seems to have the more robust career path. I recommend supplementing your coursework with as much of both as you can. Strongly recommend the following: Statistical applications, applied math, programming in R and/or Python, PowerBI, and this book.
I went the Biostatistics route for grad school and am now working as a data scientist/biostatistician. Very happy with where I am professionally.
Since you’re an engineer(?), I found this on Amazon as commonly bought with the Handbook of Math seen above. As a physicist I kinda want it.
I'm a statistician (well epidemiologist) and I approve this message. This book is great , but probably more advanced that you need. Even just any intro to stats book should really help teach you probability and statistical fundamentals to analyze your data
Roger Penrose, The Road to Reality.
It explained what it means to exist. The first stuff isn't even mathematical, but it gets more so very much, and exceeded my math ability about 2/3 through.
I just remembered on more thing. If you start getting into the higher stuff and are confused on the math, there's a number of books that are geared towards teaching math techniques that are commonly used in physics. I used Mathematical Methods in the Physical Sciences in school, but there are a number of other ones that should do the trick. They often have misleading titles like "Theoretical Physics" even though they're basically math books.
For me, the best way to get started is to find a question that you want answered. A lot of people on this subreddit say things that they feel are true; investigate them and find a way to prove or disprove it.
Once you have a question to answer, you need to find the data for it. One of the better resources for this if you don't have some sort of web scraper is pbpstats. Tons of data, easy to sort, and there's a button to download a csv file of the data you're looking at. For smaller projects, basketball reference is fine too.
Then, if you know Python, the rest is easy. If you're new to statistics and statistical ideas, I'd recommend intro to statistical learning with R. Easy to read, not too technical, and they give examples in R that are easy enough to follow. They give good rules of thumb, so you don't need to dig deep to understand an idea if you're just trying to do a fun project and need some guidance.
The rest is practice! Fail spectacularly, post your results, get feedback, and do it all over again :)
The exam is kinda its own thing, just because you need lots of practice problems. For that, I think everyone will agree CA is worth it. That being said, learning probability is a great thing, and I recommend this textbook, which my actuary-turned-prob phd professor said was the best textbook. Plus you learn R which is relevant for the job :)
Anyone interested in getting a fund going in the subreddit to help pay for people's first CA subscription? Christmas Coaching Actuaries?
Not sure if this is what you're looking for, but I thought The Holographic Universe by Michael Talbot was a great read.
Fritjof Capra (Tao of Physics) and Gary Zukav could be worth checking out as well. And of course Terence McKenna (The Archaic Revival) :) and Ram Dass (Be Here Now)
Just want to comment, that physics dont have to be beautiful minimalistic which is also discussed in the science community and also found its way into popular books like Lost in Math: How Beauty Leads Physics Astray.
Imo it is quite unscientific to rule out solution which are not "mathematically beautiful" or "simple and elegant". Or not searching for solutions which do not meet this criterias.
"Hologram plates have nothing to do with the principle of holographics universe."
Actually, they do - very much. This is a seminal work on the topic. Give it a read! It's not too complicated to digest and will give you a good explanation of the physics behind holograms and how they are used as a model for the universe.
Safe travels!
There is so much overlap. That's why some authors use the term Statistical Learning (highly recommend that book BTW). In practice, undergraduates studying "machine learning" are probably expected to be more fluent at programming than people studying statistics (although this may be changing, and it might depend on your university).
For instance, for my senior machine learning class I needed access to a powerful GPU. So, I had to create and SSH into an AWS instance, clone git repositories, run the programs from the cloned repositories (pretrained GANs), and upload files and download results from my PC using SCP. I think this would've been beyond a statistics undergrad.
A major in statistics, with a minor in computer science is an awesome combination.
r/Psychonaut
This is also used as an example of the concept of implicate order in "holographic universe" theory. Great book for anyone interested:
https://www.amazon.com/Holographic-Universe-Revolutionary-Theory-Reality/dp/0062014102
Technical literature, even when it includes exposition in some formalism such as natural deduction, will include some vernacular. This vernacular will employ some amount of domain-relevant, specialized terminology, often pejoratively called “jargon.” To be put off by “proposition X characterizes mathematical object Y” because you interpret “characterize” to imply some sort of moral judgment is, first of all, not a reasonable interpretation or reaction, and secondly means you haven’t read much formal mathematics. It’s also unreasonable to expect a formal text to recapitulate standard terminology in the field. Formal texts necessarily make demands of their readers. If Probability Theory: The Logic of Science recapitulated its applied mathematics prerequisites, it would be a graduate-level applied mathematics text and probably not get past its first two actual chapters, because it’s already expecting you to solve functional equations by then. TAPL is written in a similar style, making similar demands of its readers. But even for a complete newcomer to the field, complaining about Pierce’s “thoughts and narrative,” particularly based on such an absurd interpretation of very normal usage in the field, is bizarre.