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.