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Articles related to "use"


Functional programming in Javascript

  • In computer science, functional programming is a programming paradigm—a style of building the structure and elements of computer programs—that treats computation as the evaluation of mathematical functions and avoids changing-state and mutable data.
  • This is in contrast to imperative programming where, in addition to a function's arguments, global program state can affect a function's resulting value.
  • A function is a process which takes some input, called arguments, and produces some output called a return value.
  • A side effect is any application state change that is observable outside the called function other than its return value.
  • This is really important for a programming language to support first-class functions which basically means passing functions as arguments to other functions or assigning them to variables.
  • That is, the function is passed as an argument to another function, returned as a value from a function, or assigned to variables or data structures.

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Guide for the daily JavaScripter

  • This document is a summary of good programming practices in js in general.
  • The trend today is to focus on functional programming, both to develop a frontend or a backend.
  • These are some functional programming principles that are useful to know.
  • Use camelCase when naming objects, functions, and instances.
  • Use PascalCase only when naming constructors or classes.
  • Use JSDOC tag to describe a function.
  • Different ways of handle errors.
  • How to handle functions in a modern way.
  • Use named arrow function expressions instead of function declarations.
  • Use single quotes '' for strings, don't mix with "".
  • Use destructuring when accessing and using multiple properties of an object.
  • Use array destructuring.
  • Use dot notation when accessing properties.
  • Some points of how to handle and declare variables in javascript.
  • MINOR: version when you add functionality in a backwards-compatible manner.

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How I Created an Interactive, Scrolling Visualisation with D3.js, and how you can too

  • With the dataset decided, I started exploring the data using Python and Pandas in Jupyter notebooks, looking for any interesting trends or points I would want to emphasise in a visualisation.
  • The bubbles would then change form and colour based on the type of data I was trying to highlight (ie gender discrepancies, median salary, category grouping etc.).
  • I would strongly suggest that you take a deep dive into your data before starting any of the visualisation process, because you want to have some semblance of the interactions you have in mind before actually sitting down to code the final product.
  • In this article, I will focus on the elements which I found confusing, and put a greater emphasis on the part of actually creating the visualisation).
  • This involves creating all the scales, axes, shapes, for simulations and other elements which you will need in the visualisation.

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How I Created an Interactive, Scrolling Visualisation with D3.js, and how you can too

  • With the dataset decided, I started exploring the data using Python and Pandas in Jupyter notebooks, looking for any interesting trends or points I would want to emphasise in a visualisation.
  • The bubbles would then change form and colour based on the type of data I was trying to highlight (ie gender discrepancies, median salary, category grouping etc.).
  • I would strongly suggest that you take a deep dive into your data before starting any of the visualisation process, because you want to have some semblance of the interactions you have in mind before actually sitting down to code the final product.
  • In this article, I will focus on the elements which I found confusing, and put a greater emphasis on the part of actually creating the visualisation).
  • This involves creating all the scales, axes, shapes, for simulations and other elements which you will need in the visualisation.

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Undiscoverable UI Madness

  • We have a couple Mac users in the office so I went around and asked them how to do some things on macOS, which as we all know, is much better than iPadOS at making important, useful UI elements easily discoverable.
  • When I asked them how to right click on a folder, and 1 knew how with the Magic Mouse, but the other 2 agreed they needed to do it on their trackpad because Apple’s mouse only has a single click.
  • No one was able to, and telling them that they could by hitting Cmd+up arrow or Cmd+clicking on the directory name in the title bar was deemed the most hidden thing thus far.
  • I stopped there because we had to get back to work, but without even leaving the Finder and Desktop I was able to find a bunch of things that long-time Mac users had never known about because they never discovered them in their daily use.

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How I Created an Interactive, Scrolling Visualisation with D3.js, and how you can too

  • With the dataset decided, I started exploring the data using Python and Pandas in Jupyter notebooks, looking for any interesting trends or points I would want to emphasise in a visualisation.
  • The bubbles would then change form and colour based on the type of data I was trying to highlight (ie gender discrepancies, median salary, category grouping etc.).
  • I would strongly suggest that you take a deep dive into your data before starting any of the visualisation process, because you want to have some semblance of the interactions you have in mind before actually sitting down to code the final product.
  • In this article, I will focus on the elements which I found confusing, and put a greater emphasis on the part of actually creating the visualisation).
  • This involves creating all the scales, axes, shapes, for simulations and other elements which you will need in the visualisation.

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Marijuana use is on the rise among older adults

  • New research analyzing the data reported by thousands of adults aged 65 and older suggests that cannabis use has been distinctly on the rise in this age group.
  • Previous research — looking at data from 2005–2016 — indicated that an ever-increasing number of older adults started using cannabis.
  • — analyzed the data of 14, 896 older adults who responded to the National Survey on Drug Use and Health (NSDUH) in the years 2015–2018.
  • Dr. Han and Palamar assessed the cannabis use trend among older adults in the U.S. over the most recent 3-year period cover by the NSDUH.
  • Based on this information, the researchers estimate that the proportion of people aged 65 and over who use marijuana in some form has almost doubled within 3 years, increasing from 2.4% of NSDUH respondents in 2015 to 4.2% in 2018.

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Amazon Transcribe can now automatically redact personally identifiable information

  • Amazon Transcribe, the AWS-based speech-to-text service, launched a small but important new feature this morning that, if implemented correctly, can automatically hide your personally identifiable information from call transcripts.
  • Almost by default, that involves exchanging information like your name, address or a credit card number.
  • In my experience, some call centers stop the recording when you’re about to exchange credit card numbers, for example, but that’s not always the case.
  • With this new feature, Transcribe can automatically identify information like a social security number, credit card number, bank account number, name, email address, phone number and mailing address and redact that.
  • The tool automatically replaces this information with ‘[PII]’ in the transcript.
  • With this new Transcribe tool, at least some of this data will never be available for sharing (unless, of course, you keep a copy of the audio).

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san089/goodreads_etl_pipeline

  • ETL jobs are written in spark and scheduled in airflow to run every 10 minutes.
  • Also, checkout Airflow Connection for setting up connection to EMR and Redshift from Airflow.
  • Spinning up EMR cluster is preety straight forward.
  • Copy the ETL scripts to EMR and we have our EMR ready to run jobs.
  • You can follow the AWS Guide to run a Redshift cluster or alternatively you can use Redshift_Cluster_IaC.py Script to create cluster automatically.
  • The goodreadsfaker module in this project generates Fake data which is used to test the ETL pipeline on heavy load.
  • To test the pipeline I used goodreadsfaker to generate 11.4 GB of data which is to be processed every 10 minutes (including ETL jobs + populating data into warehouse + running analytical queries) by the pipeline which equates to around 68 GB/hour and about 1.6 TB/day.

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If you want to win everything, you have to be prepared to lose everything

  • Someone asked me what my trick is and I repeated what I’ve said often, to guests or even my children: The most important ingredient in the kitchen, is the number of the pizza delivery service.
  • Knowing she’d been successful at freelancing allowed her to focus on what was right for the company, versus what she needed to do to keep her job.
  • There’s a famous story about Apple engineers hiding a Sony engineer in a closet from Steve Jobs, which I can highly recommend you read.
  • Long story short, Steve Jobs wanted to build his own disk drive.
  • What I absolutely love about this story is that these people cared so much about their mission that they risked losing their jobs over it.
  • I try to tell this to everybody who works at TNW too.

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