TSDoc is a way of writing TypeScript comments where they’re linked to a particular function, class or method (like Python docstrings).

ESLint does automatic code linting

Debugging is twice as hard as writing the code in the first place. Therefore, if you write the code as cleverly as possible, you are, by definition, not smart enough to debug it.

interactive notebooks fall short when you want to write bigger, maintainable code

Write a new test and the result. If you want to make it REPL-like, instead of writing console.log(x.toString()) use expect(x.toString()).toBe('') and you will directly get the result.

I recommend Airbnb style JavaScript style guide and Airbnb TypeScript)

Creating meticulous tests before exploring the data is a big mistake, and will result in a well-crafted garbage-in, garbage-out pipeline. We need an environment flexible enough to encourage experiments, especially in the initial place.

Being a software development team now involves all team members performing a mysterious ritual of standing up together for 15 minutes in the morning and drawing occult symbols with post-its.

Language tooling is richer today. A programming language was usually a compiler and perhaps a debugger. Today, they usually come with the linter, source code formatter, template creators, self-update ability and a list of arguments that you can use in a debate against the competing language.

There is StackOverflow which simply didn’t exist back then. Asking a programming question involved talking to your colleagues.

Since we have much faster CPUs now, numerical calculations are done in Python which is much slower than Fortran. So numerical calculations basically take the same amount of time as they did 20 years ago.

From examining the type hinting, you can immediately tell that the function expects the input name to be of a type str, or string. You can also tell that the expected output of the function will be of a type str, or string, as well.

def hello_name(name: str) -> str:
    return(f"Hello {name}")

Docstrings can be further broken up into three major categories: Class Docstrings: Class and class methods Package and Module Docstrings: Package, modules, and functions Script Docstrings: Script and functions

According to PEP 8, comments should have a maximum length of 72 characters.

use `multiple line comment`

Docstring conventions are described within PEP 257. Their purpose is to provide your users with a brief overview of the object.

All multi-lined docstrings have the following parts: A one-line summary line A blank line proceeding the summary Any further elaboration for the docstring Another blank line

"""This is the summary line

This is the further elaboration of the docstring. Within this section,
you can elaborate further on details as appropriate for the situation.
Notice that the summary and the elaboration is separated by a blank new
line.

# Notice the blank line above. Code should continue on this line.

say_hello.__doc__ = "A simple function that says hello... Richie style"

def say_hello(name):
    print(f"Hello {name}, is it me you're looking for?")

say_hello.__doc__ = "A simple function that says hello... Richie style"

def say_hello(name):
    """A simple function that says hello... Richie style"""
    print(f"Hello {name}, is it me you're looking for?")

>>> help(say_hello)

Help on function say_hello in module __main__:

say_hello(name)
    A simple function that says hello... Richie style

class constructor parameters should be documented within the __init__ class method docstring

Scripts are considered to be single file executables run from the console. Docstrings for scripts are placed at the top of the file and should be documented well enough for users to be able to have a sufficient understanding of how to use the script.

Documenting your code, especially large projects, can be daunting. Thankfully there are some tools out and references to get you started

Commenting your code serves multiple purposes

In general, commenting is describing your code to/for developers. The intended main audience is the maintainers and developers of the Python code. In conjunction with well-written code, comments help to guide the reader to better understand your code and its purpose and design

Along with these tools, there are some additional tutorials, videos, and articles that can be useful when you are documenting your project

If you use argparse, then you can omit parameter-specific documentation, assuming it’s correctly been documented within the help parameter of the argparser.parser.add_argument function. It is recommended to use the __doc__ for the description parameter within argparse.ArgumentParser’s constructor.

There are specific docstrings formats that can be used to help docstring parsers and users have a familiar and known format.

Daniele Procida gave a wonderful PyCon 2017 talk and subsequent blog post about documenting Python projects. He mentions that all projects should have the following four major sections to help you focus your work:

Since everything in Python is an object, you can examine the directory of the object using the dir() command

Documenting your Python code is all centered on docstrings. These are built-in strings that, when configured correctly, can help your users and yourself with your project’s documentation.

Along with docstrings, Python also has the built-in function help() that prints out the objects docstring to the console.

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.

In the ideal world, they should be 1 or 2 levels of indentation

In order to formalise the model training process in code, we used an open source tool called DVC (Data Science Version Control). It provides similar semantics to Git, but also solves a few ML-specific problems:

Machine Learning pipeline for our Sales Forecasting problem, and the 3 steps to automate it with DVC

Continuous Delivery for Machine Learning end-to-end process

common functional silos in large organizations can create barriers, stifling the ability to automate the end-to-end process of deploying ML applications to production

There are different types of testing that can be introduced in the ML workflow.

example of how to combine different test pyramids for data, model, and code in CD4ML

A deployment pipeline automates the process for getting software from version control into production, including all the stages, approvals, testing, and deployment to different environments

We chose to use GoCD as our Continuous Delivery tool, as it was built with the concept of pipelines as a first-class concern

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

We use a client/server architecture where Figma clients are web pages that talk with a cluster of servers over WebSockets. Our servers currently spin up a separate process for each multiplayer document which everyone editing that document connects to

CRDTs are designed for decentralized systems where there is no single central authority to decide what the final state should be. There is some unavoidable performance and memory overhead with doing this. Since Figma is centralized (our server is the central authority), we can simplify our system by removing this extra overhead and benefit from a faster and leaner implementation

the developer doesn’t need to worry about allocating memory, or the character set encoding of the string, or a host of other things.

“No Code” systems are extremely good for putting together proofs-of-concept which can demonstrate the value of moving forward with development.

With someone else’s platform, you often end up needing to construct elaborate work-arounds for missing functionality, or indeed cannot implement a required feature at all.

It is solved when you understand why it occurred and why it no longer does.

Development Pros Cons

This kind of “exploring” is easiest when you develop on the prompt (or REPL), or using a notebook-oriented development system like Jupyter Notebooks

notebook contains an actual running Python interpreter instance that you’re fully in control of. So Jupyter can provide auto-completions, parameter lists, and context-sensitive documentation based on the actual state of your code

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

Frontend Devs: What should I learn after JavaScript? Explore these frameworks and libraries

The best way to explain the difference between launch and attach is to think of a launch configuration as a recipe for how to start your app in debug mode before VS Code attaches to it, while an attach configuration is a recipe for how to connect VS Code's debugger to an app or process that's already running.

Logpoint is a variant of a breakpoint that does not "break" into the debugger but instead logs a message to the console. Logpoints are especially useful for injecting logging while debugging production servers that cannot be paused or stopped. A Logpoint is represented by a "diamond" shaped icon. Log messages are plain text but can include expressions to be evaluated within curly braces ('{}').

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

Version control is at the heart of any modern engineering org. The ability for multiple engineers to asynchronously contribute to a codebase is crucial—and with notebooks, it’s very hard.

The priorities in building a production machine learning pipeline—the series of steps that take you from raw data to product—are not fundamentally different from those of general software engineering.

Reproducibility is an issue with notebooks. Because of the hidden state and the potential for arbitrary execution order, generating a result in a notebook isn’t always as simple as clicking “Run All.”

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 camelCase when naming objects, functions, and instances.

const thisIsMyFuction() {}

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';

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.

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.