Quantum computing is now a commercial reality. Here’s the story of the companies that are currently using it in operations and how this will soon disrupt artificial intelligence and deep learning.
One of the things we are most excited about as a newly open source company is the potential to help kickstart a larger ecosystem of GPU computing. This is why we are particularly excited about our work with Continuum Analytics and H2O.ai to found the GPU Open Analytics Initiative (GOAI) and its first project, the GPU Data Frame (GDF), as our first step toward an open ecosystem of end-to-end GPU computing. A revolution is occurring across the GPU software stack, driven by the disruptive performance gains GPUs have seen generation after generation. The modern field of deep learning would have not been possible without GPUs (with special credit due to Nvidia for innovating both on the hardware and software side), and as a database we’re often seeing two-or-more orders of magnitude performance gains compared to CPU systems.
Nuts-ml is a new data pre-processing library in Python for GPU-based deep learning in vision. It provides common pre-processing functions as independent, reusable units. These so called ‘nuts’ can be freely arranged to build data flows that are efficient, easy to read and modify.
Why on Earth would a data scientist need to know about qualitative research? There are plenty of reasons. Here are a few. Though I’ve had training in qualitative methods, I’m a quant specialist and have been for more than 30 years. However, I’m a user of qualitative research and have been throughout my career. Unless our area of data science has no relationship at all with human beings, it’s is very relevant to quantitative researchers. The closer what we do is to marketing research – user experience (UX) and customer relationship management (CRM) being two examples – the more useful it becomes. Qualitative research provides background and context that makes quantitative analysis such as predictive analytics more useful to decision makers. Consumer surveys do as well and frequently these two kinds of research are combined, with a qualitative phase preceding the survey and sometimes following it in a third phase. How is qualitative research useful? There are many ways and here are a few illustrations.
Below are some podcasts I listen to that relate to data science and statistics. Each of them has something slightly different to offer, so if this is an area of interest to you then I recommend you give these a try!
We always say ‘if you are not looking at the data, you are not doing science’- and for big data you are very dependent on summaries (as you can’t actually look at everything). Simple question: is there an easy way to summarize big data in R? The answer is: yes, but we suggest you use the replyr package to do so. Let’s set up a trivial example.
This is the second post about the Marketing Multi-channel Attribution Model with Markov chains (here is the first one). Even though the concept of the first-order Markov chains is pretty simple, you can face other issues and challenges when implementing the approach in practice. In this article, we will review some of them. I tried to organize this article in a way that you can use it as a framework or can help you to create your own.
This post summarizes and links to the individual tutorials which make up this introductory look at data science for newbies, mainly focusing on the tools, with a practical bent, written by a software engineer from the perspective of a software engineering approach.