Data scientists are expected to know a lot — machine learning, computer science, statistics, mathematics, data visualization, communication, and deep learning. Within those areas there are dozens of languages, frameworks, and technologies data scientists could learn. How should data scientists who want to be in demand by employers spend their learning budget? Which skills are most in demand for data scientists?
Open source computing is hugely important to software development. It is the model that everyone benefits from. The open source foundations that support this development play a crucial role. Open source foundations have emerged to help sustain and manage open source projects. These foundations provide space for companies and people with a stake in open source software (OSS) project to come together. Their status as independent, non-profit entities provides neutral ground for competing companies to work together. Let’s see who’s behind many of the tools software developers and data scientists use every day.
Deep learning continues to be the hottest thing in data science. Deep learning frameworks are changing rapidly. Just five years ago, none of the leaders other than Theano were even around. I wanted to find evidence for which frameworks merit attention, so I developed this power ranking. I used 11 data sources across 7 distinct categories to gauge framework usage, interest, and popularity. Without further ado, here are the Deep Learning Framework Power Scores
A more refined framework is needed to provide a richer common lexicon for thinking and communicating about data in machine learning. A framework along the lines of the one in this article should lead practitioners, especially newer practitioners, to develop better models faster. With 7 Data Types to reference we should all be able to more quickly evaluate and discuss the encoding options and imputation strategies available. Hope that this article will provide a useful taxonomy of groups that for more actionable steps for data scientists.
Let’s briefly look at the types of chips available for deep learning. I’ll simplify the major offerings by comparing them to Ford cars. CPUs alone are really slow for deep learning. You do not want to use them. They are fine for many machine learning tasks, just not deep learning. The CPU is the horse and buggy of deep learning. GPUs are much faster than CPUs for most deep learning computations.
Automated machine learning doesn’t replace the data scientist, but it might be able to help you find good models faster. TPOT bills itself as your Data Science Assistant. TPOT is meant to be an assistant that gives you ideas on how to solve a particular machine learning problem by exploring pipeline configurations that you might have never considered, and then leaves the fine-tuning to more constrained parameter tuning techniques such as grid search.
In this guide, we’ll look at methods from the os and shutil modules. The os module is the primary Python module for interacting with the operating system. The shutil module also contains high-level file operations. For some reason, you make directories with os but move and copy them with shutil. There are many ways to copy files and directories in Python. Go figure.
Docker is a platform to develop, deploy, and run applications inside containers. Docker is essentially synonymous with containerization. If you’re a current or aspiring software developer or data scientist, Docker is in your future. Don’t fret if you aren’t yet up to speed — this article will help you understand the conceptual landscape — and you’ll get to make some pizza along the way. By the end of the series (and with a little practice) you should know enough Docker to be useful.
Docker Platform bundles code files and dependencies. It promotes easy scaling by enabling portability and reproducibility. In this article, you will learn a dozen additional terms from the Docker ecosystem that you need to know. Docker terms are broken into two categories for easier mental model creation: Essentials and Scaling. Let’s hit the eight essentials first. Docker services allow you to scale containers across multiple Docker Daemons and make Docker Swarms possible. Here’s the one line explanation to help you keep these dozen terms straight.
A Dockerfile instruction is a capitalized word at the start of a line followed by its arguments. Each line in a Dockerfile can contain an instruction. Instructions are processed from top to bottom when an image is built. In this article, I’m assuming you are using a Unix-based Docker image. You can also use Windows-based images, but that’s a slower, less-pleasant, less-common process. So use Unix if you can. Let’s do a quick once-over of the dozen Dockerfile instructions we’ll explore.