The general layout of the project and its documentation should be as follows:

project_root/
│
├── project/  # Project source code
├── docs/
├── README
├── HOW_TO_CONTRIBUTE
├── CODE_OF_CONDUCT
├── examples.py

In all cases, the docstrings should use the triple-double quote (""") string format.

Let's replicate our inefficientSquare example, but this time we'll use our memoizer to cache results.

The biggest problem with JSON.stringify is that it doesn't serialize certain inputs, like functions and Symbols (and anything you wouldn't find in JSON).

Memoization is an optimization technique used in many programming languages to reduce the number of redundant, expensive function calls. This is done by caching the return value of a function based on its inputs.

Continuous Delivery for Machine Learning (CD4ML) is the discipline of bringing Continuous Delivery principles and practices to Machine Learning applications.

Here are some optional attributes available to all launch configurations

The following attributes are mandatory for every launch configuration

Many debuggers support some of the following attributes

They switch to get features like good doc lookup, good syntax highlighting, integration with unit tests, and (critically!) the ability to produce final, distributable source code files, as opposed to notebooks or REPL histories

Exploratory programming is based on the observation that most of us spend most of our time as coders exploring and experimenting

A notebook, at a very basic level, is just a bunch of JSON that references blocks of code and the order in which they should be executed.But notebooks prioritize presentation and interactivity at the expense of reproducibility. YAML is the other side of that coin, ignoring presentation in favor of simplicity and reproducibility—making it much better for production.

Notebook files, however, are essentially giant JSON documents that contain the base-64 encoding of images and binary data. For a complex notebook, it would be extremely hard for anyone to read through a plaintext diff and draw meaningful conclusions—a lot of it would just be rearranged JSON and unintelligible blocks of base-64.

There is no hidden state or arbitrary execution order in a YAML file, and any changes you make to it can easily be tracked by Git

Python unit testing libraries, like unittest, can be used within a notebook, but standard CI/CD tooling has trouble dealing with notebooks for the same reasons that notebook diffs are hard to read.

Use PascalCase only when naming constructors or classes.

// good
class User {
  constructor(options) {
    this.name = options.name;
  }
}

const good = new User({
  name: 'yup',
});

Use uppercase only in constants.

export const API_KEY = 'SOMEKEY';

If you'd just like to see refactorings without Quick Fixes, you can use the Refactor command (Ctrl+Shift+R).

Stary, dobry Uncle Bob mówi, że poza etatem trzeba na programowanie poświęcić 20h tygodniowo.Gdy podzielimy to na 7 dni w tygodniu, to wychodzi prawie 3 godziny dziennie.Dla jednych mało, dla innych dużo.

Z gier można wyciągnąć też inną naukę. Jeśli Twoim celem jest przejście do następnej lokacji, to czy musisz wykonywać wszystkie zadania poboczne?No nie musisz. Dlatego wyżej, gdy podawałem wymagane umiejętności dla osoby, która prowadzi szkolenia z Reacta, albo pracuje dla startupów, to napisałem “dobra znajomość JSa”, bo “doskonała” nie pomoże Ci w osiągnięciu tego celu.

W miarę jak będziesz się rozwijać i zdobywać nowe informacje, Twój plan będzie trzeba dostosować do nowych warunków.

Arnie miał wielkie plany, ale nie realizował wszystkich na raz.Skupił się na jednej rzeczy - kulturystyce - bo wiedział, że to otworzy mu drogę do Ameryki i do aktorstwa.

Jeśli wybierzesz kilka rzeczy na raz to ryzykujesz, że znowu zaczniesz miotać się we wszystkich kierunkach.Nie polecam takiej opcji, bo właśnie przez takie myślenie kończymy potem wkurwieni, z siwymi włosami i podkrążonymi oczami.Doświadczyłem wszystkich tych trzech objawów i dopiero kiedy skupiłem się na jednej rzeczy, to odzyskałem balans i przestałem się denerwować.

Niestety, jeśli faktycznie chcesz być najlepszą osobą, to z moich obserwacji wynika, że trzeba zapierdzielać.

W czasach młodości Arniego, Reg Park był wielką gwiazdą kulturystyki, sławnym oraz bogatym aktorem i otaczały go piękne dziewczyny.Arnie chciał się wydostać z zadupia w Austrii, przeprowadzić do Ameryki i mieć dokładnie to samo co Reg.Wolność, sławę, kasę i dziewczyny.Wszystkie jego działania były podporządkowane dotarciu do tego celu.Arnie dokładnie zawęził czego chce.

👉 Wiele osób ma cele w stylu “chcę się nauczyć Reacta”. I jest to zbyt słabo sprecyzowany cel.👉 Celem musi być coś w stylu “chcę się nauczyć Reacta, żeby pracować w firmie X i robić projekty dla startupów z Doliny Krzemowej”.👉 Albo “chcę się nauczyć Reacta, żeby pracować w Facebooku z Danem Abramovem nad kolejnymi wersjami Reacta”.👉 Albo “chcę się nauczyć Reacta, żeby prowadzić szkolenia stacjonarne dla backendowców, którzy nie umieją we frontendy”.

Pytać wszystkich wszędzie i powiększaj swoją znajomość tematu. Poszerzaj worek możliwości.Przeszukaj Reddita, Stacka, grupy na Facebooku. Znajdź jakiegoś mądrego człowieka na social media i napisz mu DM.

Suppose you have only two rolls of dice. then your best strategy would be to take the first roll if its outcome is more than its expected value (ie 3.5) and to roll again if it is less.

Therefore, En=2n+1−2=2(2n−1)

It's responsible for allocating and scheduling containers, providing then with abstracted functionality like internal networking and file storage, and then monitoring the health of all of these elements and stepping in to repair or adjust them as necessary.In short, it's all about abstracting how, when and where containers are run.

You’ll see pressure to push towards “Cloud neutral” solutions using Kubernetes in various places

Heroku? App Services? App Engine?

Kubernetes (often irritatingly abbreviated to k8s, along with it’s wonderful ecosystem of esoterically named additions like helm, and flux) requires a full time ops team to operate, and even in “managed vendor mode” on EKS/AKS/GKS the learning curve is far steeper than the alternatives.

Azure App Services, Google App Engine and AWS Lambda will be several orders of magnitude more productive for you as a programmer. They’ll be easier to operate in production, and more explicable and supported.

With the popularisation of docker and containers, there’s a lot of hype gone into things that provide “almost platform like” abstractions over Infrastructure-as-a-Service. These are all very expensive and hard work.

By using events that are buffered in queues, your system can support outage, scaling up and scaling down, and rolling upgrades without any special consideration. It’s normal mode of operation is “read from a queue”, and this doesn’t change in exceptional circumstances.

Reserved resources, capacity, or physical hardware can be protected for pieces of your software, so that an outage in one part of your system doesn’t ripple down to another.

The complimentary design pattern for all your circuit breakers – you need to make sure that you wrap all outbound connections in a retry policy, and a back-off.

Circuit breaking is a useful distributed system pattern where you model out-going connections as if they’re an electrical circuit. By measuring the success of calls over any given circuit, if calls start failing, you “blow the fuse”, queuing outbound requests rather than sending requests you know will fail.

“scaling out is the only cost-effective thing”, but plenty of successful companies managed to scale up with a handful of large machines or VMs

Scaling is hard if you try do it yourself, so absolutely don’t try do it yourself. Use vendor provided, cloud abstractions like Google App Engine, Azure Web Apps or AWS Lambda with autoscaling support enabled if you can possibly avoid it.

Hexagonal architectures, also known as “the ports and adapters” pattern

Good microservice design follows a few simple rules

What Microservices are supposed to be: Small, independently useful, independently versionable, independently shippable services that execute a specific domain function or operation. What Microservices often are: Brittle, co-dependent, myopic services that act as data access objects over HTTP that often fail in a domino like fashion.

In the mid-90s, “COM+” (Component Services) and SOAP were popular because they reduced the risk of deploying things, by splitting them into small components

all a design pattern is, is the answer to a problem that people solve so often, there’s an accepted way to solve it

Let’s do a quick run through of some very common ones:

CDNs are web servers run by other people, all over the world. You upload your data to them, and they will replicate your data across all of their “edges” (a silly term that just means “to all the servers all over the world that they run”) so that when someone requests your content, the DNS response will return a server that’s close to them, and the time it takes them to fetch that content will be much quicker.

Understanding how a distributed cache works is remarkably simple – when an item is added, the key (the thing you use to retrieve that item) that is generated includes the address or name of the computer that’s storing that data in the cache. Generating keys on any of the computers that are part of the distributed cache cluster will result in the same key. This means that when the client libraries that interact with the cache are used, they understand which computer they must call to retrieve the data.

Memory caches are used to store the result of something that is “heavy” to calculate, takes time, or just needs to be consistent across all the different computers running your server software. In exchange for a little bit of network latency, it makes the total amount of memory available to your application the sum of all the memory available across all your servers.

All a load balancer does, is accept HTTP requests for your application (or from it), pick a server that isn’t very busy, and forward the request.

GraphQL gels especially well with modern JavaScript driven front ends, with excellent tools like Apollo and Apollo-Server that help optimise these calls by batching requests.

BFF is an API that serves one, and specifically only one application

GraphQL really is just a smart and effective way to schema your APIs, and provide a BFF – that’s backend for frontend

What sets GraphQL apart a little from previous approaches (notably Microsofts’ OData) is the idea that Types and Queries are implemented with Resolver code on the server side, rather than just mapping directly to some SQL storage.

GraphQL is confusingly, a Query Language, a standard for HTTP APIs and a Schema tool all at once.

Use a swagger or OpenAPI library to generate a schema and you’re pretty much doing what most people are doing.

JSON-RPC is “level 0” of the Richardson Maturity Model – a model that describes the qualities of a REST design.

REST is a good architectural style (and it is, a lot of the modern naysaying about REST is relatively uninformed and not too dissimilar to the treatment SOAP had before it)

there was a push for standardisation of “SOAP” (simple object access protocol) APIs

The basics of HTTP are easy to grasp – there’s a mandatory “request line”

GET http://www.davidwhitney.co.uk/ HTTP/1.1
Host: www.davidwhitney.co.uk
Connection: keep-alive
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64…
Accept: text/html,application/xhtml+xml,application/xml;q=0.9
Accept-Encoding: gzip, deflate
Accept-Language: en-GB,en-US;q=0.9,en;q=0.8

HTTP/1.1 200 OK
Cache-Control: public,max-age=1
Content-Type: text/html; charset=utf-8
Vary: Accept-Encoding
Server: Kestrel
X-Powered-By: ASP.NET
Date: Wed, 11 Dec 2019 21:52:23 GMT
Content-Length: 8479


<!DOCTYPE html>
<html lang="en">...

web is an implementation of the design pattern REST – which stands for “Representational State Transfer”. You’ll hear people talk about REST a lot – it was originally defined by Roy Fielding in his PhD dissertation, but more importantly was a description of the way HTTP/1.0 worked at the time

So the web is RESTful by default. REST describes the way HTTP works.

Most APIs you use, or build will be “REST-ish”.

Honestly for the most part it’s a matter of taste, and they’re all perfectly appropriate ways to build web applications.

static site generators became increasingly popular – normally allowing you to use your normal front-end web dev stack, but then generating all the files using a build tool to bundle and distribute to dumb web servers or CDNs

MVVM is equally common in single page apps where there’s two way bindings between something that provides data (the model) and the UI (which the view model serves).

SPAs are incredibly common, popularised by client-side web frameworks like Angular, React and Vue.js.

http://www.mycoolsite.com/Home/Index

when most people say “MVC” they’re really describing “Rails-style” MVC apps where your code is organised into a few different directories

most people stick to a handful of common patterns

There are tonne of general purpose web servers out there

“web servers”, implement the “HTTP protocol” – a series of commands you can send to remote computers – that let you say “hey, computer, send me that document”

He is intrigued by the unique qualities of each person, organizes for maximum productivity, has a great desire to learn, can sense other people’s feelings, and is introspective and appreciates intellectual discussions—strengths that have been confirmed both through the Clifton StrengthsFinder assessment and are reflected in his personal and professional life.

People pay for premium content for the content itself and connection to the creator, not for studio lighting and animations

Repeated measures involves measuring the same cases multiple times. So, if you measured the chips, then did something to them, then measured them again, etc it would be repeated measures. Replication involves running the same study on different subjects but identical conditions. So, if you did the study on n chips, then did it again on another n chips that would be replication.

I’m sharing a few insights I specifically found useful for developers who are not specialized in this domain.

So I wrote a GitHub Actions workflow that automatically updates the README page in the repo every time a new TIL markdown file is added or updated!

Think of mental laziness as a lack of mental exercise. Mental exercise, such as tough decisions, actually burn more calories and impact your overall physiology.

Laziness is stagnation without regard for enjoyment or perceptions. In other words it is the opposite of ambition or progression.

At the company I work at, one of our products is an embeddable commenting system. Unlike single-page applications, when we encounter bugs they’re usually on the client’s website. This raised the question, how can we embed a piece of code that will run on all our client’s websites, that will help us debug and improve our overall build experience.

“Hey, I have a good idea for a game,” I said. “It’s called the function machine game. I will think of a function machine. You tell me things to put into the function machine, and I will tell you what comes out. Then you have to guess what the function machine does.” He immediately liked this game and it has been a huge hit; he wants to play it all the time. We played it while driving to a party yesterday, and we played it this morning while I was in the shower.

Tau Day is an annual celebration of the circle constant τ=6.283185…\tau = 6.283185\ldots, which takes place every June 28 (6/28 in the American calendar system).

To customize settings for debugging tests, you can specify "request":"test" in the launch.json file in the .vscode folder from your workspace.

For example, the test_decrement functions given earlier are failing because the assertion itself is faulty.

Support for running tests in parallel with pytest is available through the pytest-xdist package.

With pytest, failed tests also appear in the Problems panel, where you can double-click on an issue to navigate directly to the test

VS Code also shows test results in the Python Test Log output panel (use the View > Output menu command to show the Output panel, then select Python Test Log

For Python, test discovery also activates the Test Explorer with an icon on the VS Code activity bar. The Test Explorer helps you visualize, navigate, and run tests

Once VS Code recognizes tests, it provides several ways to run those tests

You can trigger test discovery at any time using the Python: Discover Tests command.

Sometimes tests placed in subfolders aren't discovered because such test files cannot be imported. To make them importable, create an empty file named __init__.py in that folder.

Testing in Python is disabled by default. To enable testing, use the Python: Configure Tests command on the Command Palette.

python.testing.pytestArgs: Looks for any Python (.py) file whose name begins with "test_" or ends with "_test", located anywhere within the current folder and all subfolders.

python.testing.unittestArgs: Looks for any Python (.py) file with "test" in the name in the top-level project folder.

Create a file named test_unittest.py that contains a test class with two test methods

import inc_dec    # The code to test
import unittest   # The test framework

class Test_TestIncrementDecrement(unittest.TestCase):
    def test_increment(self):
        self.assertEqual(inc_dec.increment(3), 4) # checks if the results is 4 when x = 3

    def test_decrement(self):
        self.assertEqual(inc_dec.decrement(3), 4)

if __name__ == '__main__':
    unittest.main()

Each test framework has its own conventions for naming test files and structuring the tests within, as described in the following sections. Each case includes two test methods, one of which is intentionally set to fail for the purposes of demonstration.

Nose2, the successor to Nose, is just unittest with plugins

Python tests are Python classes that reside in separate files from the code being tested.

general background on unit testing, see Unit Testing on Wikipedia. For a variety of useful unit test examples, see https://github.com/gwtw/py-sorting

Running the unit test early and often means that you quickly catch regressions, which are unexpected changes in the behavior of code that previously passed all its unit tests.

Developers typically run unit tests even before committing code to a repository; gated check-in systems can also run unit tests before merging a commit.

in unit testing you avoid external dependencies and use mock data or otherwise simulated inputs

The practice of test-driven development is where you actually write the tests first, then write the code to pass more and more tests until all of them pass.

The combined results of all the tests is your test report

each test is very simple: invoke the function with an argument and assert the expected return value.

    def test_validator_valid_string():
        # The exact assertion call depends on the framework as well
        assert(validate_account_number_format("1234567890"), true)

Unit tests are concerned only with the unit's interface—its arguments and return values—not with its implementation

unit is a specific piece of code to be tested, such as a function or a class. Unit tests are then other pieces of code that specifically exercise the code unit with a full range of different inputs, including boundary and edge cases.

Open Command tool and search for coverage

Stroną, która jako jedyna wyświetla wszystkie dostępne do śledzenia statki powietrze jest ADSBexchange.com, którą zasilimy naszymi danymi. Jest to największa na świecie społeczność właścicieli odbiorników ADS-B / Mode S / MLAT i jednocześnie największe na świecie, w pełni publiczne i darmowe, źródło niefiltrowanych danych o lotach. Zapewniany przez to narzędzie dostęp do danych lotów na całym świecie jest wykorzystywany zarówno przez hobbystów, badaczy, jak i, co ciekawe, dziennikarzy.

Can G Suite Admin Read My Email?

AinD launches Android apps in Docker, by nesting Anbox containers inside Docker.

gh repo create hello-world -d "A react app for the web" --public

gh repo create hello-world -d "A react app for the web" --public

An emerging way to bypass the need for passwords is to use magic links. A magic link is a temporary URL that expires after use, or after a specific interval of time. Magic links can be sent to your email address, an app, or a security device. Clicking the link authorizes you to sign in.

Hashing passwords won’t save you or your users. Once a database of passwords has been stolen, hackers aim immense distributed computing power at those password databases. They use parallel GPUs or giant botnets with hundreds of thousands of nodes to try hundreds of billions of password combinations per second in hopes of recovering plaintext username/password pairs.

The solution will be to go for serverless functions. This means that instead of occupying a server completely, it will only use the server capacity when the function needs to run.

My favourites are Zeit and Netlify. They are quite similar in the features they provide: continuous deployment, around 100GB of bandwidth per month, and a built-in CDN. Another benefit is that they both provide the option of serverless functions, as we will see in the next section. This simplifies the number of services we need to integrate for our entire stack.

Guédelon Castle (Château de Guédelon) is a castle currently under construction near Treigny, France. The castle is the focus of an experimental archaeology project aimed at recreating a 13th-century castle and its environment using period technique, dress, and material.

To take full advantage of tabular augmentation for time-series you would perform the techniques in the following order: (1) transforming, (2) interacting, (3) mapping, (4) extracting, and (5) synthesising

process of modular feature engineering and observation engineering while emphasising the order of augmentation to achieve the best predicted outcome from a given information set

1) Redash and Falcon focus on people that want to do visualizations on top of SQL2) Superset, Tableau and PowerBI focus on people that want to do visualizations with a UI3) Metabase and SeekTable focus on people that want to do quick analysis (they are the closest to an Excel replacement)

Visual Studio Code supports working with Jupyter Notebooks natively, as well as through Python code files.

# %%
msg = "Hello World"
print(msg)

# %%
msg = "Hello again"
print(msg)

JupyterLab project, which enables a richer UI including a file browser, text editors, consoles, notebooks, and a rich layout system.

I could probably bootstrap my way up from this with the C compiler to write a terrible editor, then write a terrible TCP client, find my way out to ftp.gnu.org, get wget, and keep going from there. Assume that documentation is plentiful. You want a copy of the Stevens book so you can figure out how to do a DNS query by banging UDP over the network? Done.

B&W is compelling because by removing color detail it draws attention to figure, curves, facial expressions, emotions, movement, time of day, depending on subject

We find three important trends in the evolution of musical discourse: the restriction of pitch sequences (with metrics showing less variety in pitch progressions), the homogenization of the timbral palette (with frequent timbres becoming more frequent), and growing average loudness levels. The picture at right shows the timbral variety: Smaller values of β indicate less timbral variety: frequent codewords become more frequent, and infrequent ones become even less frequent. This evidences a growing homogenization of the global timbral palette. It also points towards a progressive tendency to follow more fashionable, mainstream sonorities.

Unfortunately no - it cannot be done without Trusted security devices. There are several reasons for this. All of the below is working on the assumption you have no TPM or other trusted security device in place and are working in a "password only" environment.

Most people are surprised to learn that of Plotly’s 50 engineers, the vast majority are React developers. This is because Dash is primarily a frontend library — there are far more lines of JavaScript (Typescript) than Python, R, or Julia code. Plotly only has 3 full-time Python developers, 2 full-time R developers, and 0 full-time Julia developers.

Behind the scenes, when a Python, R or Julia engineer creates a Dash app, they are actually creating a React Single Page Application (“SPA”).

With Dash, any open-source React UI component can be pulled from npm or GitHub, stirred with water, transmogrified into a Dash component, then imported into your Dash app as a Python, R, or Julia library. C’est magnifique! 👨‍🍳 Dash makes the richness and innovation of the React frontend ecosystem available to Python, R, and Julia engineers for the first time.

To use Gunicorn as your web server, it must be included in the requirements.txt file as an app dependency. It does not need to be installed in your virtual environment/host machine.

pip is a package manager. conda is both a package manager and an environment maanger.

To help you get started quickly, we created a special Installer of Visual Studio Code for Java developers. Download Visual Studio Code Java Pack Installer Note: The installer is currently only available for Windows. For other OS, please install those components (JDK, VS Code and Java extensions) individually. We're working on the macOS version, please stay tuned. The package can be used as a clean install or an update for an existing development environment to add Java or Visual Studio Code. Once downloaded and opened, it automatically detects if you have the fundamental components in your local development environment, including the JDK, Visual Studio Code, and essential Java extensions.

Note: When you create a new virtual environment, you should be prompted by VS Code to set it as the default for your workspace folder. If selected, the environment will automatically be activated when you open a new terminal.

Tip: Use Logpoints instead of print statements: Developers often litter source code with print statements to quickly inspect variables without necessarily stepping through each line of code in a debugger. In VS Code, you can instead use Logpoints. A Logpoint is like a breakpoint except that it logs a message to the console and doesn't stop the program. For more information, see Logpoints in the main VS Code debugging article.

Open an Anaconda command prompt and run conda create -n myenv python=3.7 pandas jupyter seaborn scikit-learn keras tensorflow

On the job, if you notice poor infrastructure, speak up to your manager early on. Clearly document the problem, and try to incorporate a data engineering, infrastructure, or devops team to help resolve the issue! I'd also encourage you to learn these skills too!

When I worked at Target HQ in 2012, employees would arrive to work early - often around 7am - in order to query the database at a time when few others were doing so. They hoped they’d get database results quicker. Yet, they’d still often wait several hours just to get results.

The Data Scientist, in many cases, should be called the Data Janitor. ¯_(ツ)_/¯

In regards to quality of data on the job, I’d often compare it to a garbage bag that ripped, had its content spewed all over the ground and your partner has asked you to find a beautiful earring that was accidentally inside.

On the job, I’d recommend you document your work well and calculate the monetary value of your analyses based on factors like employee salary, capital investments, opportunity cost, etc. These analyses will come in handy for a promotion/review packet later too.

you as a Data Scientist should try to find a situation to be incredibly valuable on the job! It’s tough to find that from the outskirts of applying to jobs, but internally, you can make inroads supporting stakeholders with evidence for their decisions!

As a Data Scientist in the org, are you essential to the business? Probably not. The business could go on for a while and survive without you. Sales will still be made, features will still get built, customer support will handle customer concerns, etc.

As the resident Data Scientist, you may become easily inundated with requests from multiple teams at once. Be prepared to ask these teams to qualify and defend their requests, and be prepared to say “no” if their needs fall outside the scope of your actual priority queue. I’d recommend utilizing the RICE prioritization technique for projects.

Common questions are: How many users click this button; what % of users that visit a screen click this button; how many users have signed up by region or account type? However, the data needed to answer those questions may not exist! If the data does exists, it’s likely “dirty” - undocumented, tough to find or could be factually inaccurate. It’ll be tough to work with! You could spend hours or days attempting to answer a single question only to discover that you can’t sufficiently answer it for a stakeholder. In machine learning, you may be asked to optimize some process or experience for consumers. However, there’s uncertainty with how much, if at all, the experience can be improved!

In one experience, a fellow researcher spent over a month researching a particular value among our customers through qualitative and quantitative data. She presented a well-written and evidence-backed report. Yet, a few days later, a key head of product outlined a vision for the team and supported it with a claim that was antithetical to the researcher’s findings! Even if a data science project you advocate for is greenlighted, you may be on your own as the rare knowledgeable person to plan and execute it. It’s unlikely leadership will be hands-on to help you research and plan out the project.

Because people don’t know what data science does, you may have to support yourself with work in devops, software engineering, data engineering, etc.

In 50+ interviews for data related jobs, I’ve been asked about AB testing, SQL analytics questions, optimizing SQL queries, how to code a game in Python, Logistic Regression, Gradient Boosted Trees, data structures and/or algorithms programming problems!

seven most common (and at times flagrant) ways that data science has failed to meet expectations in industry

In data science community the performance of the model on the test dataset is one of the most important things people look at. Just look at the competitions on kaggle.com. They are extremely focused on test dataset and the performance of these models is really good.

It's basically a look up table, interpolating between known data points. Except, unlike other interpolants like 'linear', 'nearest neighbour' or 'cubic', the underlying functional form is determined to best represent the kind of data you have.

there must be a space before any punctuation sign that is made of two parts ( ; : ! ? ) and no space before any other punctuation sign

git reflog is a very useful command in order to show a log of all the actions that have been taken! This includes merges, resets, reverts: basically any alteration to your branch.

git reflog

it pull is actually two commands in one: a git fetch, and a git merge. When we're pulling changes from the origin, we're first fetching all the data like we did with a git fetch, after which the latest changes are automatically merged into the local branch.

git pull origin master

It doesn't affect your local branch in any way: a fetch simply downloads new data.

git fetch origin master

When a certain branch contains a commit that introduced changes we need on our active branch, we can cherry-pick that command! By cherry-picking a commit, we create a new commit on our active branch that contains the changes that were introduced by the cherry-picked commit.

git cherry-pick 76d12

Another way of undoing changes is by performing a git revert. By reverting a certain commit, we create a new commit that contains the reverted changes!

git revert ec5be

Sometimes, we don't want to keep the changes that were introduced by certain commits. Unlike a soft reset, we shouldn't need to have access to them any more.

git reset --hard HEAD~2
git status

git rebase copies the commits from the current branch, and puts these copied commits on top of the specified branch.

git rebase master

This can happen when the two branches we're trying to merge have changes on the same line in the same file, or if one branch deleted a file that another branch modified, and so on.

git merge dev

git add updated_file.md
git commit -m "Merge..."

If we committed changes on the current branch that the branch we want to merge doesn't have, git will perform a no-fast-forward merge.

git merge dev

fast-forward merge can happen when the current branch has no extra commits compared to the branch we’re merging.

git merge dev

soft reset moves HEAD to the specified commit (or the index of the commit compared to HEAD), without getting rid of the changes that were introduced on the commits afterward!

git reset --soft HEAD~2
git status

git reset gets rid of all the current staged files and gives us control over where HEAD should point to.

There are 6 actions we can perform on the commits we're rebasing

git rebase -i HEAD~3

Pharma, which is one of the biggest, richest, most rewarding and promising industries in the world. Especially now, when the pharmaceutical industry, including the FDA, allows R to be used the domain occupied in 110% by SAS.

CR is one of the most controlled industries in this world. It's insanely conservative in both used statistical methods and programming. Once a program is written and validated, it may be used for decades. There are SAS macros written in 1980 working still by today without any change. That's because of brilliant backward compatibility of the SAS macro-language. New features DO NOT cause the old mechanisms to be REMOVED. It's here FOREVER+1 day.

wearing simple face masks which exert a barrier function that blocks those big projectile droplets that land in the nose or throat may substantially reduce the production rate R, to an extent that may be comparable to social distancing and washing hands.

Make sure to discard or launder after use without touching the outward surface

CDC suggests use of scarf by health care providers as last resort when no face masks are available

avoiding large droplets, which cannot enter the lung anyway but land in the upper respiratory tracts, could be the most effective means to prevent infection. Therefore, surgical masks, perhaps even your ski-mask, bandanas or scarf

The molecular analysis also show that the SARS-Cov2 virus is active and replicates already in the nasopharynx, unlike other respiratory viruses that dwell in deeper regions of the lung.

Surprisingly, ACE2 expression in the lung is very low: it is limited to a few molecules per cell in the alveolar cells (AT2 cells) deep in the lung. But a just published paper by the Human Cell Atlas (HCA) consortium reports that ACE2 is highly expressed in some type of (secretory) cells of the inner nose!

SARS-Cov-2 virus, like any virus, must dock onto human cells using a key-lock principle, in which the virus presents the key and the cell the lock that is complementary to the key to enter the cell and replicate. For the SARS-Cov-2 virus, the viral surface protein “Spike protein S” is the “key” and it must fit snugly into the “lock” protein that is expressed (=molecularly presented) on the surface of the host cells. The cellular lock protein that the SARS-Cov-2 virus uses is the ACE2 protein

Filtering effect for small droplets (aerosols) by various masks; home-made of tea cloth, surgical mask (3M “Tie-on”) and a FFP2 (N95) respirator mask. The numbers are scaled to the reference of 100 (source of droplets) for illustrative purposes, calculated from the PF (protection factor) values

The tacit notion at the CDC that the alveolae are the destination site for droplets to deliver the virus load (the alveolae are after all the anatomical site of life-threatening pneumonia), has elevated the apparent importance of N95 masks and led to the dismissal of surgical masks.

In the case of the SARS-Cov-2 virus it is not known what the minimal infectious load is (number of viral particles needed to start the pathogenesis cascade that causes a clinical disease).

Of course many aerosol droplets in the exhalation or cough spray may not contain the virus, but some will do.

droplets of a typical cough expulsion have a size distribution such that approximately half of the droplet are in the categories of aerosols, albeit they collectively represent only less than 1/100,000 of the expelled volume

For airborne particles to be inspired and reach deep into the lung, through all the air ducts down to the alveolar cells where gas-exchange takes place, it has to be small

Droplets can (for this discussion) be crudely divided in two large categories based on size

Droplet larger than aerosols, when exhaled (at velocity of <1m/s), evaporate or fall to the ground less than 1.5 m away. When expelled at high velocity through coughing or sneezing, especially larger droplets (> 0.1 micrometers), can be carried by the jet more than 2m or 6m, respectively, away.

The official recommendation by CDC, FDA and others that masks worn by the non-health-care professionals are ineffective is incorrect at three levels: In the logic, in the mechanics of transmission, and in the biology of viral entry.

Flattening the curve”. Effect of mitigating interventions that would decrease the initial reproduction rate R0 by 50% when implemented at day 25. Red curve is the course of numbers of infected individuals (”case”) without intervention. Green curve reflects the changed (”flattened”) curve after intervention. Day 0 (March 3, 2020) is the time at which 100 cases of infections were confirmed (d100 = 0).

Ancestor of all animals identified in Australian fossils

Połączona sieć komputerów działających w ramach inicjatywy Folding@Home przewyższyła swoją mocą obliczeniową najwydajniejsze siedem superkomputerów na świecie. Dobrze przeczytaliście: połączone w ramach F@H urządzenia dysponują mocą obliczeniową na poziomie 470 PetaFLOPS - wyższą od siedmiu najwydajniejszych superkomputerów na świecie razem wziętych! To trzy razy większa wydajność od tej, którą dysponuje najwydajniejszy obecnie superkomputer SUMMIT (149 PFLOPS).

This denotes the factorial of a number. It is the product of numbers starting from 1 to that number.

x = 5
fact = 1
for i in range(x, 0, -1):
    fact = fact * i
print(fact)

import math
math.factorial(x)

# 5*4*3*2*1
120

The hat gives the unit vector. This means dividing each component in a vector by it’s length(norm).

x = [1, 2, 3]
length = math.sqrt(sum([e**2 for e in x]))
x_hat = [e/length for e in x]

math.sqrt(sum([e**2 for e in x_hat]))
# 1.0

It gives the sum of the products of the corresponding entries of the two sequences of numbers.

X = [1, 2, 3]
Y = [4, 5, 6]
dot = sum([i*j for i, j in zip(X, Y)])
# 1*4 + 2*5 + 3*6
# 32

It means multiplying the corresponding elements in two tensors. In Python, this would be equivalent to multiplying the corresponding elements in two lists.

x = [[1, 2], 
    [3, 4]]
y = [[2, 2], 
    [2, 2]]
z = np.multiply(x, y)

[[2, 4]],
[[6, 8]]

This is basically exchanging the rows and columns.

X = [[1, 2, 3], 
    [4, 5, 6]]
np.transpose(X)

[[1, 4], 
 [2, 5],
 [3, 6]]

This denotes a function which takes a domain X and maps it to range Y. In Python, it’s equivalent to taking a pool of values X, doing some operation on it to calculate pool of values Y.

def f(X):
    Y = ...
    return Y

This symbol checks if an element is part of a set.

X = {1,2,3}
3 in X

The norm is used to calculate the magnitude of a vector. In Python, this means squaring each element of an array, summing them and then taking the square root.

x = [1, 2, 3]
math.sqrt(x[0]**2 + x[1]**2 + x[2]**2)

This symbol denotes the absolute value of a number i.e. without a sign.

x = 10
y = -20
abs(x)  #10
abs(y) #20

In Python, it is equivalent to looping over a vector from index 0 to index N-1 and multiplying them.

we reuse the sigma notation and divide by the number of elements to get an average.

x = [1, 2, 3, 4, 5]
result = 0
N = len(x)
for i in range(n):
 result = result + x[i]
average = result / N
print(average)

x = [1, 2, 3, 4, 5]
result = sum(x) / len(x)

In Python, it is equivalent to looping over a vector from index 0 to index N-1

x = [1, 2, 3, 4, 5]
result = 0
N = len(x)
for i in range(N):
 result = result + x[i]
print(result)

x = [1, 2, 3, 4, 5]
result = sum(x)

2D vectors

x = [ [10, 20, 30], [40, 50, 60] ]
i = 0
j = 1
print(x[i][j]) # 20

1–9–90 rule (sometimes 90–9–1 principle or the 89:10:1 ratio),[1] which states that in a collaborative website such as a wiki, 90% of the participants of a community only consume content, 9% of the participants change or update content, and 1% of the participants add content.