Category: javascript

The 10 Fundamental Concepts of JavaScript

The 10 Fundamental Concepts of JavaScript

Another pearl of a resource on Twitter is this thread by Madison on 10 of fundamentalal concepts of Javascript — and programming in general for that matter.

For your convience, I copied the links below. Just click them to browse to the resource and learn more about the concept

Click to learn more about each concept

  1. Variables & Scoping
  2. Data types
  3. Objects, Funtions & Arrays
  4. Document Object Model (DOM)
  5. Prototypes & this.
  6. Events
  7. Flow Control (specifically, for-loops)
  8. Security & (web) Accesibility
  9. Good coding practices (to which I’ve linked before)
  10. Async

This 10-step list was compiled as apart of this interesting podcast, which I recommend you listen to as well.

Want to learn more?

According to many, this is the best book to continue learning more about JavaScript.

There’s a (now classic) conference talk that comes with this book, which I can also recommend you watch:

Learn to style HTML using CSS — Tutorials by Mozilla

Learn to style HTML using CSS — Tutorials by Mozilla

Cascading Stylesheets — or CSS — is the first technology you should start learning after HTML. While HTML is used to define the structure and semantics of your content, CSS is used to style it and lay it out. For example, you can use CSS to alter the font, color, size, and spacing of your content, split it into multiple columns, or add animations and other decorative features.

https://developer.mozilla.org/en-US/docs/Learn/CSS

I was personally encoutered CSS in multiple stages of my Data Science career:

  • When I started using (R) markdown (see here, or here), I could present my data science projects as HTML pages, styled through CSS.
  • When I got more acustomed to building web applications (e.g., Shiny) on top of my data science models, I had to use CSS to build more beautiful dashboard layouts.
  • When I was scraping data from Ebay, Amazon, WordPress, and Goodreads, my prior experiences with CSS & HTML helped greatly to identify and interpret the elements when you look under the hood of a webpage (try pressing CTRL + SHIFT + C).

I know others agree with me when I say that the small investment in learning the basics behind HTML & CSS pay off big time:

I read that Mozilla offers some great tutorials for those interested in learning more about “the web”, so here are some quicklinks to their free tutorials:

Screenshot via developer.mozilla.org/en-US/docs/Learn/CSS/CSS_layout/Introduction
Visualizing and interpreting Cohen’s d effect sizes

Visualizing and interpreting Cohen’s d effect sizes

Cohen’s d (wiki) is a statistic used to indicate the standardised difference between two means. Resarchers often use it to compare the averages between groups, for instance to determine that there are higher outcomes values in a experimental group than in a control group.

Researchers often use general guidelines to determine the size of an effect. Looking at Cohen’s d, psychologists often consider effects to be small when Cohen’s d is between 0.2 or 0.3, medium effects (whatever that may mean) are assumed for values around 0.5, and values of Cohen’s d larger than 0.8 would depict large effects (e.g., University of Bath).

The two groups’ distributions belonging to small, medium, and large effects visualized

Kristoffer Magnusson hosts this Cohen’s d effect size comparison tool on his website the R Psychologist, but recently updated the visualization and its interactivity. And the tool looks better than ever:

Moreover, Kristoffer adds some nice explanatons of the numbers and their interpretation in real life situations:

If you find the tool useful, please consider buying Kristoffer a coffee or buying one of his beautiful posters, like the one above, or below:

Frequentisme betekenis testen poster horizontaal image 0

By the way, Kristoffer hosts many other interesting visualization tools (most made with JavaScript’s D3 library) on statistics and statistical phenomena on his website, have a look!

A free, self-taught education in Computer Science!

A free, self-taught education in Computer Science!

The Open Source Society University offers a complete education in computer science using online materials.

They offer a proper introduction to the fundamental concepts for all computing disciplines. Evyerthing form algorithms, logic, and machine learning, up to databases, full stack web development, and graphics is covered. Moreover, you will acquire skills in a variety of languages, including Python, Java, C, C++, Scala, JavaScript, and many more.

According to their GitHub page, the curriculum is suited for people with the discipline, will, and good habits to obtain this education largely on their own, but who’d still like support from a worldwide community of fellow learners.

Curriculum

  • Intro CS: for students to try out CS and see if it’s right for them
  • Core CS: corresponds roughly to the first three years of a computer science curriculum, taking classes that all majors would be required to take
  • Advanced CS: corresponds roughly to the final year of a computer science curriculum, taking electives according to the student’s interests
  • Final Project: a project for students to validate, consolidate, and display their knowledge, to be evaluated by their peers worldwide
  • Pro CS: graduate-level specializations students can elect to take after completing the above curriculum if they want to maximize their chances of getting a good job

It is possible to finish Core CS within about 2 years if you plan carefully and devote roughly 18-22 hours/week to your studies. Courses in Core CS should be taken linearly if possible, but since a perfectly linear progression is rarely possible, each class’s prerequisites are specified so that you can design a logical but non-linear progression based on the class schedules and your own life plans.

Links to the contents

Links to the curriculum (v8.0.0)

Curated Regular Expression Resources

Curated Regular Expression Resources

Regular expression (also abbreviated to regex) really is a powertool any programmer should know. It was and is one of the things I most liked learning, as it provides you with immediate, godlike powers that can speed up your (data science) workflow tenfold.

I’ve covered many regex related topics on this blog already, but thought I’d combine them and others in a nice curated overview — for myself, and for you of course, to use.

If you have any materials you liked, but are missing, please let me know!

Contents


Introduction & Learning

Reading

Tutorials (interactive)

Video

Corey Shafer

The Coding Train

Language-specific

Python

Corey Shafer

R

Roger Peng

Testing & Debugging

debuggex.com

regex101.com

regextester.com | regexpal.com

regexr.com

ExtendsClass.com/regex-tester

rubular.com

pythex.com

Fun

Building a $86 million car theft AI in 57 lines of JavaScript

Building a $86 million car theft AI in 57 lines of JavaScript

Tait Brown was annoyed at the Victoria Police who had spent $86 million Australian dollars on developing the BlueNet system which basically consists of an license-plate OCR which crosschecks against a car theft database.

Tait was so disgruntled as he thought he could easily replicate this system without spending millions and millions of tax dollars. And so he did. In only 57 lines of JavaScript, though, to be honest, there are many more lines of code hidden away in abstraction and APIs…

Anyway, he built a system that can identify license plates, read them, and should be able to cross check them with a criminal database.

Via Medium

I really liked reading about this project, so please do so if you’re curious via the links below:

Part 1: How I replicated an $86 million project in 57 lines of code

Part 2: Remember the $86 million license plate scanner I replicated?

Part X: the code on Github

Cover image via Medium via Freepik