- Exact Maximum Clique for Large or Massive Real Graphs — explanation of how BBMCSP works.
- Giving Robots and Prostheses the Human Touch — the team, led by mechanical engineer Veronica J. Santos, is constructing a language of touch that both a computer and a human can understand. The researchers are quantifying this with mechanical touch sensors that interact with objects of various shapes, sizes, and textures. Using an array of instrumentation, Santos’ team is able to translate that interaction into data a computer can understand. The data is used to create a formula or algorithm that gives the computer the ability to identify patterns among the items it has in its library of experiences and something it has never felt before. This research will help the team develop artificial haptic intelligence, which is, essentially, giving robots, as well as prostheses, the “human touch.”
- boltons — things in Python that should have been builtins.
- Everything We Wish We’d Known About Building Data Products (DJ Patil and RusJan Belkin) — Data is super messy, and data cleanup will always be literally 80% of the work. In other words, data is the problem. […] “If you’re not thinking about how to keep your data clean from the very beginning, you’re fucked. I guarantee it.” […] “Every single company I’ve worked at and talked to has the same problem without a single exception so far — poor data quality, especially tracking data,” he says.“Either there’s incomplete data, missing tracking data, duplicative tracking data.” To solve this problem, you must invest a ton of time and energy monitoring data quality. You need to monitor and alert as carefully as you monitor site SLAs. You need to treat data quality bugs as more than a first priority. Don’t be afraid to fail a deploy if you detect data quality issues.
Meg Blanchette interviews Continuum's Peter Wang about the growing role of OSS in the enterprise.
If you attend OSCON this year, you may notice a bit more attention paid to the enterprise side of tech. That is on purpose, as we have been noticing the open source and enterprise worlds edging closer and closer. Companies traditionally nervous about open source are either recognizing the inherent value, or their developers are using it and they don’t even realize. Open source is, in turn, seeing the benefits an established company can bring a project and the various opportunities available.
In that spirit, I spoke with Peter Wang, from Continuum Analytics. Continuum is a good example of this new hybrid — offering open source technology, while also having an enterprise side. Here, we discuss the changing landscape and what that can mean for people who embrace change, and for those who don’t.
Maintaining a focus on fun and interactivity keeps students engaged and enthused while learning Java.
I consider myself extremely fortunate to be involved with Devoxx4Kids, a Not-for-Profit, 501(c)(3) registered organization in the U.S., whose goal is to deliver Science Technology Engineering Mathematics (STEM) workshops to kids at an early age around the world. We delivered over 40 workshops in the U.S. alone last year on topics ranging from Python, Scratch, and Minecraft modding to NAO robots, Raspberry Pi, Arduino, and Little Circuits. Globally, we’ve delivered over 350 workshops and connected with approximately 5,000 students, with over 30% girls. Attendees from these workshops often leave with unique and inspirational stories to share. Read more…
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.
Software engineer and author Jason Myers on changing roles in a changing market.
We often hear about how the tech job market is booming and has space for newcomers, but what does that mean for the developers already in the market? In December of 2014, Fortune.com predicted that 2015 would be an excellent year for developers to change jobs. Citing Dice.com, they note that jobs are popping up all over the country. In fact, Dice’s survey also reports 40% of hiring managers seeing voluntary departures, a higher number than was seen just six months earlier.
These are all large, general numbers. What does a job change, and the changing market, look like for individual developers? To get a better sense, I spoke with Jason Myers, who is working on our upcoming Essential SQLAlchemy, 2e title. Jason recently went from working for the email marketing service Emma, Inc., to working for networking giant Cisco. Here, he talks about how a change like that feels, and how the market looks to him.
Think labels, not boxes
Python tuples have a surprising trait: they are immutable, but their values may change. This may happen when a tuple holds a reference to any mutable object, such as a list. If you need to explain this to a colleague who is new to Python, a good first step is to debunk the common notion that variables are like boxes where you store data.
In 1997 I took a summer course about Java at MIT. The professor, Lynn Andrea Stein — an award-winning computer science educator — made the point that the usual “variables as boxes” metaphor actually hinders the understanding of reference variables in OO languages. Python variables are like reference variables in Java, so it’s better to think of them as labels attached to objects.
Here is an example inspired by Lewis Carroll’s Through the Looking-Glass, and What Alice Found There.
Tweedledum and Tweedledee are twins. From the book: “Alice knew which was which in a moment, because one of them had ‘DUM’ embroidered on his collar, and the other ‘DEE’.”
Prepare for the future of computing with Python.
I was reminded of this when reading some recent articles worrying about the slow transition from Python 2 to Python 3, such as Python 3 is Killing Python. The authors of such articles, and Python developers in general, really like Python, and for the most part like Python 3. Their main concern is that the protracted 2-3 straddle will hurt Python’s popularity.
About five years ago, I started writing an introductory Python book for O’Reilly. It featured Python 2, which was dominant then. Unfortunately, the tides of business went out and took the book with them. Two years ago, the tides returned and the book was revived. Introducing Python: Modern Computing in Simple Packages is finally in production and early release.
When we rebooted the book, there was now a serious question of whether to feature Python 2 or 3. The other version might merit some sidebars or an appendix, but we really needed to pick just a single base for the code examples. And by now it seemed that Python 3 had become the right choice. If you’re wondering why the editors and I thought Python 3 was best for this book, let me give some of the reasons, more or less in order of importance.