This post is pretty bizarre, but it manages to hit on so many false beliefs that I've seen hurt junior data scientists that it deserves some explicit

(1) The notion that R is well-suited to "building web applications" seems totally out of left field. I don't feel like most R loyalists think this is a good idea, but it's worth calling out that no normal company will be glad you wrote your entire web app in R.
(2) It is true that Python had some issues historically with the 2-to-3 transition, but it's not such a big deal these days. On the flip side, I have found interesting R code that doesn't run in modern R interpreters because of changes in core operations (e.g. assignment syntax).
(3) "Most of the time we only need a latest, working interpreter with the latest packages to run the code" -- this is where things get real and reveal some things that hurt data scientists. If this sentence is true, it's likely because you don't share code with coworkers.
(3) Really is a broader issue in data science: people only think of what they need to do their work if no one else existed and code was never maintained. Junior data scientists almost always operate on projects they start from scratch and don't have to maintain for long.
(3) Especially astonishing is this claim, "The version incompatibility and package management issues would almost surely create technical, even political problems within large organizations." In reality, updating packages unnecessarily can itself be a source of problems.
(4) "To do this in R, we merely need to do b = a". The idea that assignment is intrinsically a copying operation seems to have just been made up. Making lots of copies is one of the things that slows R down and all R loyalists seem to admit this. Copying != purity.
(5) "as a functional programming language": Some folks keep claiming that R is a functional language, but they never define the term well. R is not pure by default. R code is riddled with mutations to the symbol table; library(foo) has to emit warnings for exactly that reason.
(6) "Eventually, such functional designs save human time — the more significant bottleneck in the long run." This belief is extremely common among R users and it really holds them back in situations in which performance does matter. Large projects often demand high performance.
(7) "In fact, the abstraction of vector, matrix, data frame, and list is brilliant." This belief really holds R users back when talking with engineers about implementations. At some point, everyone needs to learn what a hash table is, but its absence from base R confuses folks.
(8) "Beyond that, I also love the vector-oriented design and thinking in R. Everything is a vector:" This belief also seems common in the R community, even though the creator of R has said it's the biggest mistake they made. Scalars are always good and sometimes essential.
(9) If the most important of an IDE is an object inspector, maybe "No decent IDEs, ever" is true, but I think this is another case where the author has just never interacted with software engineers or understood their needs.
Putting it all together, there's a very troubling (and self-defeating) tendency in the data science world to embrace insularity and refuse to learn about the things software engineers know. Both communities have important forms of expertise; more sharing is the way forward.

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I have always emphasized on the importance of mathematics in machine learning.

Here is a compilation of resources (books, videos & papers) to get you going.

(Note: It's not an exhaustive list but I have carefully curated it based on my experience and observations)

📘 Mathematics for Machine Learning

by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong

Note: this is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it.

📘 Pattern Recognition and Machine Learning

by Christopher Bishop

Note: Prior to the book above, this is the book that I used to recommend to get familiar with math-related concepts used in machine learning. A very solid book in my view and it's heavily referenced in academia.

📘 The Elements of Statistical Learning

by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie

Mote: machine learning deals with data and in turn uncertainty which is what statistics teach. Get comfortable with topics like estimators, statistical significance,...

📘 Probability Theory: The Logic of Science

by E. T. Jaynes

Note: In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions.

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THREAD: 12 Things Everyone Should Know About IQ

1. IQ is one of the most heritable psychological traits – that is, individual differences in IQ are strongly associated with individual differences in genes (at least in fairly typical modern environments).

2. The heritability of IQ *increases* from childhood to adulthood. Meanwhile, the effect of the shared environment largely fades away. In other words, when it comes to IQ, nature becomes more important as we get older, nurture less.

3. IQ scores have been increasing for the last century or so, a phenomenon known as the Flynn effect. (N ≈ 4 million)

(Note that the Flynn effect shows that IQ isn't 100% genetic; it doesn't show that it's 100% environmental.)

4. IQ predicts many important real world outcomes.

For example, though far from perfect, IQ is the single-best predictor of job performance we have – much better than Emotional Intelligence, the Big Five, Grit, etc.

5. Higher IQ is associated with a lower risk of death from most causes, including cardiovascular disease, respiratory disease, most forms of cancer, homicide, suicide, and accident. (N = 728,160)
Funny, before the election I recall lefties muttering the caravan must have been a Trump setup because it made the open borders crowd look so bad. Why would the pro-migrant crowd engineer a crisis that played into Trump's hands? THIS is why. THESE are the "optics" they wanted.

This media manipulation effort was inspired by the success of the "kids in cages" freakout, a 100% Stalinist propaganda drive that required people to forget about Obama putting migrant children in cells. It worked, so now they want pics of Trump "gassing children on the border."

There's a heavy air of Pallywood around the whole thing as well. If the Palestinians can stage huge theatrical performances of victimhood with the willing cooperation of Western media, why shouldn't the migrant caravan organizers expect the same?

It's business as usual for Anarchy, Inc. - the worldwide shredding of national sovereignty to increase the power of transnational organizations and left-wing ideology. Many in the media are true believers. Others just cannot resist the narrative of "change" and "social justice."

The product sold by Anarchy, Inc. is victimhood. It always boils down to the same formula: once the existing order can be painted as oppressors and children as their victims, chaos wins and order loses. Look at the lefties shrieking in unison about "Trump gassing children" today.