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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To my JVM friends looking to explore Machine Learning techniques - you don’t necessarily have to learn Python to do that. There are libraries you can use from the comfort of your JVM environment. 🧵👇 : Deep Learning framework in Java that supports the whole cycle: from data loading and preprocessing to building and tuning a variety deep learning networks. Framework for defining machine learning models, including feature generation and transformations, as directed acyclic graphs (DAGs). a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification. : TensorFlow Java API (experimental)
✨✨ BIG NEWS: We are hiring!! ✨✨
Amazing Research Software Engineer / Research Data Scientist positions within the @turinghut23 group at the @turinginst, at Standard (permanent) and Junior levels 🤩

👇 Here below a thread on who we are and what we

We are a highly diverse and interdisciplinary group of around 30 research software engineers and data scientists 😎💻 👉 #RSEng

We value expertise across many domains - members of our group have backgrounds in psychology, mathematics, digital humanities, biology, astrophysics and many other areas 🧬📖🧪📈🗺️⚕️🪐
/ @DavidBeavan @LivingwMachines

In our everyday job we turn cutting edge research into professionally usable software tools. Check out @evelgab's #LambdaDays 👩‍💻 presentation for some examples:

We create software packages to analyse data in a readable, reliable and reproducible fashion and contribute to the #opensource community, as @drsarahlgibson highlights in her contributions to @mybinderteam and @turingway: #ResearchSoftwareHour
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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Hello!! 👋

• I have curated some of the best tweets from the best traders we know of.

• Making one master thread and will keep posting all my threads under this.

• Go through this for super learning/value totally free of cost! 😃



Got these scanners from the following accounts:

1. @Pathik_Trader
2. @sanjufunda
3. @sanstocktrader
4. @SouravSenguptaI
5. @Rishikesh_ADX

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These setups I found from the following 4 accounts:

1. @Pathik_Trader
2. @sourabhsiso19
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4. Curated tweets on HOW TO SELL STRADDLES.

Everything covered in this thread.
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I'll begin with the ancient history ... and it goes way back. Because modern humans - and before that, the ancestors of humans - almost certainly originated in Ethiopia. 🇪🇹 (sub-thread):

The first likely historical reference to Ethiopia is ancient Egyptian records of trade expeditions to the "Land of Punt" in search of gold, ebony, ivory, incense, and wild animals, starting in c 2500 BC 🇪🇹

Ethiopians themselves believe that the Queen of Sheba, who visited Israel's King Solomon in the Bible (c 950 BC), came from Ethiopia (not Yemen, as others believe). Here she is meeting Solomon in a stain-glassed window in Addis Ababa's Holy Trinity Church. 🇪🇹

References to the Queen of Sheba are everywhere in Ethiopia. The national airline's frequent flier miles are even called "ShebaMiles". 🇪🇹