- This is Not the Fourth Industrial Revolution (Slate) — the phrase “the fourth Industrial Revolution” has been around for more than 75 years. It first came into popular use in 1940.
- Huginn — MIT-licensed system for building agents that perform automated tasks for you online. They can read the Web, watch for events, and take actions on your behalf. Huginn’s Agents create and consume events, propagating them along a directed graph. Think of it as a hackable Yahoo! Pipes plus IFTTT on your own server.
- Evidence-Oriented Programming — design programming language syntax and features based on what research shows works. They tested Perl and Java, found apparently not detectably easier to use for novices than a language that my student at the time, Susanna Kiwala (formerly Siebert), created by essentially rolling dice and picking (ridiculous) symbols at random.
- Deep Detect — open source deep learning service.
"programming languages" entries
Python's simplicity makes it accessible to learners and teachers alike.
Download a free copy of Python in Education. Editor’s note: this is an excerpt from Python in Education, a free report written by Nicholas Tollervey.
I am going to answer a very simple question: which features of the Python language itself make it appropriate for education? This will involve learning a little Python and reading some code. But don’t worry if you’re not a coder! This chapter will hopefully open your eyes to how easy it is to learn Python (and thus, why it is such a popular choice as a teaching language).
When I write a to-do list on a piece of paper, it looks something like this:
Shopping Fix broken gutter Mow the lawn
This is an obvious list of items. If I wanted to break down my to-do list a bit further, I might write something like this:
Shopping: Eggs Bacon Tomatoes Fix broken gutter: Borrow ladder from next door Find hammer and nails Return ladder! Mow the lawn: Check lawn around pond for frogs Check mower fuel level
Intuitively, we understand that the main tasks are broken down into sub-tasks that are indented underneath the main task to which they relate. This makes it easy to see, at a glance, how the tasks relate to each other.
Moving beyond traditional tools makes data analysis faster and more powerful
Garrett Grolemund is an O’Reilly author and teaches classes on data analysis for R Studios.
We sat down to discuss why data scientists, statisticians, and programmers alike can use the R language to make data analysis easier and more powerful.
Key points from the full video (below) interview include:
- R is a free, open-source language that has its roots in S-PLUS [Discussed at the 0:27 mark]
- What does it mean for R to be a programming language versus just a data analysis tool? [Discussed at the 1:00 mark]
- R comes with many useful data analysis methods already implemented, so you don’t have to start from scratch. [Discussed at the 4:23 mark]
- R is a mix of functional and object-oriented programming that is optimal for handling data structures that data analysts expect (e.g. vectors) [Discussed at the 6:08 mark]
- A discussion of using R in conjunction with other languages like Python, along with packages that help with this [Discussed at the 7:30 mark]
- Getting started using R isn’t really any harder than using a calculator [Discussed at the 9:28 mark]
You can view the entire interview in the following video.