ProcessingJS Functions

ProcessingJS

Shapes

• rect(x, y, w, h)
• ellipse(x, y, w, h)
• triangle(x1, y1, x2, y2, x3, y3)
• line(x1, y1, x2, y2)
• point(x, y)
• arc(x, y, w, h, start, stop)
• bezier(x1, y1, cx1, cy1, cx2, cy2, x2, y2)
• quad(x1, y1, x2, y2, x3, y3, x4, y4)
• image(image, x, y, width*, height*)

See also: ellipseMode, rectMode, imageMode, strokeCap, bezierPoint, bezierTangent, curve, curvePoint, curveTangent, curveTightness

Complex Shapes

• beginShape() / endShape() / vertex()
• curveVertex()
• bezierVertex()

See also: strokeJoin, curveTightness

(keep reading...)

Developing Front End Development

I’ve invested hundreds of hours into online course programs, and have thus curated a specific subset that aligns well with my current goals. One or more of the URLs inevitably link to a site that wants money every month, however, this can be subverted via “free trial” offers.

(No particular order)

https://www.khanacademy.org

https://www.codecademy.com

https://www.udacity.com

https://learn.freecodecamp.org

https://learn.co
https://www.edx.org

Codecademy JavaScript Functions and CSS ( a review)

Review Functions

Give yourself a pat on the back, you just navigated through functions!

In this lesson, we covered some important concepts about functions:

• A function is a reusable block of code that groups together a sequence of statements to perform a specific task.
• A function declaration :
  

After a Full Day of Coding

Nine hours later, I am happy to have produced a couple of products. One is a very basic mock form for ordering food delivery online. The other is a shopping list that allows one to add and remove items.

More importantly than what I have produced today is what I have learned. 

I think I'll brew some more coffee and settle in for another few hours.

Form - Version 1.0	Form - Version 2.0

Grocery List Login Screen	Add/Remove Items to List

I made a drumkit

I followed through day 1 of JavaScript30 and produced a viable (desktop version) drumkit! It is currently hosted on GitHub.

HTML Document Standards - Review

I'm trying my hand at a free trial of Codecademy. Having just completed a very basic and brief introduction to HTML, I thought I'd leave the review here.

----------------------------------------------------------------------------------------------------

INTRODUCTION TO HTML

Review

Congratulations on completing the first lesson of HTML! You are well on your way to becoming a skilled web developer.

Let's review what you've learned so far:

1. HTML stands for HyperText Markup Language and is used to create the structure and content of a webpage.

Reset <-- FreeCodeCamp

After thinking about it, I've decided to continue my work with FreeCodeCamp for now.  I spent a good deal of time learning R, but for whatever reason, Python just appeals to me more than R does. Admittedly, R does have a steep learning curve, whereas Python is more straightforward. Either way, I don't have much of a portfolio, and FreeCodeCamp covers a good deal of design elements as well as coding, all while leading to a tangible portfolio.

Now What?

I've spent the last several months at a new job, and have not dedicated time to studying computer science. As I find myself returning to my studies, I can't help but feel overwhelmed. There are a seemingly endless number of languages, software packages, and other options that I don't quite know where I just start. For example, I feel like I am studying music theory, and every time I pick up an instrument I am faced with deciding; drums, bass, guitar, keyboard, piano, mandolin, or banjo? Then, once I've decided which instrument to work with, I have to narrow it down to a particular style of music; punk, folk, classic rock, hip hop, waltz, swing, polka, dubstep, jazz, or country?

It's as if I'm wasting time trying to narrow down an endless set of options to just one, focused area. It's clear that if I am to get good at anything, I need to focus on one language, not all of them.

Hmm...

Data Analysis Checklist

I've spent the evening and early morning hours reading The Elements of Data Analytic Style - A guide for people who want to analyze data by Jeff Leek (This book is for sale at http://leanpub.com/datastyle), and wanted to post this amazing data analysis checklist here so that I may continually refer back to it. 

The data analysis Checklist -- A way to evaluate the quality of a reported data analysis.

14.1 Answering the question

1. Did you specify the type of data analytic question (e.g.

exploration, association causality) before touching the

data?

2. Did you define the metric for success before beginning?

3. Did you understand the context for the question and

the scientific or business application?

4. Did you record the experimental design?

5. Did you consider whether the question could be answered with the available data?

Half a Year Later...

Wow, I can't believe it's been nearly half a year since I worked on my computer science studies. I've spent the last three months or so developing wood burning skills, which I have greatly enjoyed. I've also been missing my time in the ether. I'm going to attempt to divide my extracurricular endeavors between crafting and coding.

I'm learning R, in R

I've recently discovered an amazing package for R called Swirl. I realize it's been around for some time now, but it's new to me, and I love it. Swirl() is a straight-forward, user-friendly approach to diving right into R. Each lesson is delivered interactively and in short bursts. The lessons are succinct and detailed, and leave the user feeling informed. For anyone looking to get started with R, I strong recommend Swirl().

library(swirl)
swirl()

swirl: learn R, in R

R Programming by Johns Hopkins University

I am about to begin R Programming from Johns-Hopkins by way of Coursera! This is course 2 of 10 that I am working through on my way to becoming a Data Scientist.

In this course, I will learn how to program in R and how to use R for effective data analysis. I will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.

Sunflow Data Science & Analytics

My name is Austin Craver and I am a budding Data Scientist. 

I just launched www.SunflowData.com to act as a repository for my progress and work.

It is called Sunflow Data.

Discovering new ways to take raw data and turn it into meaningful results. We strive to continually ensure that the analysis is relevant, and we are committed to providing clean, in-depth datasets.

Metis - Demystifying Data Science Conference

July 24th: For Aspiring Data Scientists

All times in EST

• 10:00am

  

  KEYNOTE

  A Badass’s Guide to Breaking into Data

  KEYNOTE: LILLIAN PIERSON

• 10:30am

  

  Advice to Aspiring Data Scientists: Start a Blog

  DAVID ROBINSON

  
  

Types of Neural Networks

I stumbled upon this via Twitter, and I'm leaving it here for future reference.

Source: Dr. Kirk D Borne

Data Science Specialization = My New Focus

I am focusing entirely on Data Science for the next year. I really liked studying HTML5, CSS, JavaScript, jQuery, SQL, C, and more, but alas I must focus. I must focus on one thing and learn to do it well. That thing is Data Science.

I recently received a scholarship that covered the $50.00 required to take The Data Scientist's Toolbox from Coursera via Johns Hopkins, for which I am truly grateful. However, it was just an intro to the comprehensive specialization and I finished what was presented as a four-week course in two sittings. I am trying to accept that it will all be a joyful uphill journey from here and just have fun with the process as a whole. Never before have I so thoroughly enjoyed the learning process.

The following excerpt is from a free lesson via DataCamp:

At a minimum, a data scientist should...

I wish I could remember where I copied and pasted this from, but alas, I cannot. Just the same, here's a great overview of what a data scientist would benefit from understanding.

At a minimum, a data scientist needs to be proficient with concepts such as probability, correlation, variables, distributions, regression, null hypothesis significance tests, confidence intervals, t-test, ANOVA, and chi-square. At an advanced stage, data scientists need concepts and algorithms such as logistic regression, support vector machines (SVMs) and Bayesian methods. Common statistical analysis tools such as Excel, R and SAS are very famous among data scientists.

Telling Stories With Data

I'm moving through some great data science material via Cognitive Class (Big Data University) via a course titled (appropriately) Introduction to Data Science. One of the modules discussed one of the many areas in which a great data scientist must possess strength, storytelling.

1. Tell readers, at the outset, what they might gain by reading your paper.
2. Make the aim of the work clear. 
3. Explain the significance of your contribution.
4. Have you set your work in the appropriate context by giving sufficient background (including a complete set of relevant references) to your work?
5. Have you addressed the question of practicality and usefulness?
6. Have you identified future developments that might result from your work?
7. Have you structured your paper in a clear and logical fashion?

Original version of the cited text...

Harvard University...

I just completed three full days of on-site continuing education at Harvard University. (The $1,200 fee was waived by the Computer Science department.) We engaged in 22 hours of in-person instruction and I completed 15 hours (closer to a hundred?) of preparatory work.

2D, 360 Degree, Virtual Reality...

A long-time friend of mine is in a local band called THE HORRiFiCS and I am pleased to be doing a blended photo shoot with them in 11 hours. I've been focusing my photographic efforts almost entirely on local businesses for several months, so it's a nice change of pace. I'm taking my portable photo studio for more traditional shots, then we're going on-location, and along the way, I'll be capturing some 360° photos and possibly videos!!!

My Tableau Public Profile

I am super excited (and tired (it's 3:09 AM)) to be diving further into the boundless world of Data Science. One of my best friends was in town a few days ago and he introduced me to Tableau. A couple of days later, I had a fully licensed copy installed on my machine. Two days after that, I got to attend a roundtable discussion at Gleneagles Country Club in Plano, Texas. I was surrounded by a small group of community leaders in Data Science. The cities of Plano and Dallas had representatives there, as well as a variety of Data Scientists from a broad range of companies. It was a great experience, and I can't wait to attend other such events.

Austin Craver - Data Profile Page

I have spent the last several hours pouring over datasets and doing my best to produce something meaningful, all while telling a story.



I have since created two original data visualizations. One shows male and female life expectancies around the world. The other shows Internet usage by country.

Tableau/Prep

As I continue my journey, things continue to fall into place. For example, I was just granted a one-year research and education license of both Tableau and Tableau Prep!!! I cannot wait to grab a dataset and dive into the next chapter of my data science life.

I just download, installed, and registered Tableau and Tableau Prep! 

Tensors Flowing

In approximately 12 hours, one of my best friends will be arriving from Houston and we plan on spending the day coding. I am starting Chingu's Voyage 6 and look forward to gleaning as much as I can from his boundless programming expertise. In the meantime, since he mentioned TensorFlow, I have read up on it, which lead me to study tensors in general. The more I learn about machine learning and data science, the more confident I am that I will be learning much more.

Python Programming

I'm studying intermediate programming in Python and the DataCamp course I'm taking is using Google Sheets. All of the following information was copied and pasted entirely for me to easily locate in the future. ---

DATEDIF

Calculates the number of days, months, or years between two dates.

Sample Usage

DATEDIF(DATE(1969, 7, 16), DATE(1969, 7, 24), "D")

DATEDIF(A1, A2, "YM")

DATEDIF("7/16/1969", "7/24/1969", "Y")

Syntax

JavaScript Variables

Going to spend some time this weekend studying JavaScript. The image below was extracted from a screenshot of a course I'm taking via SoloLearn.

According to the website:

Naming Variables

There are some other rules to follow when naming your JavaScript variables:

- You must not use any special symbols, like my#num, num%, etc.
- Be sure that you do not use any of the following JavaScript reserved words.

Join the learning adventure: Learn JavaScript