Every two weeks, we find the most interesting data science links from around the web and collect them in Data Science Briefings, the DataMiningApps newsletter. Subscribe now for free if you want to be the first to get up to speed on interesting resources.
- Pyxley: Python powered dashboards
This Python package aims to offer a Python-equivalent of the Shiny framework in R, providing a framework that allows data scientists to create interactive web applications without having to write Javascript, HTML, or CSS.
- Deepdream: Avoiding Kitsch
An interesting article on the large amount of websites which let you explore Google’s “deepdream” network, but do not attempt to re-train the neural net on new, interesting image sets, resulting in the same “puppyslug”-style images appearing everywhere. The author shows some examples resulting from newly trained models.
- Cute robot politely shows self-awareness
Right, so maybe the ability to solve a deductive reasoning puzzle is not quite yet “self-awareness” as this article puts it, but the video showing three cute robots still remains worth a watch.
- Exploring the shapes of stories using Python and sentiment APIs
Interesting article discussing the shape on stories, derived by using sentiment analysis techniques, inspired by Vonnegut.
- Recommending Subreddits by Computing User Similarity: An Introduction to Machine Learning in Python
Interesting introductory article developing a recommender system.
- Check out this d3.js gallery with more than 2400 examples
If you were looking for a way to get started with d3.js, this repository of examples should have you covered.
- Microsoft launches its Jupyter service
Following in the wake of Wakari and others, Microsoft has launched a Jupyter-powered data science environment.
- Continuum Analytics Secures $24 Million Series A Round to Empower Next Phase of Data Science
Speaking of Wakari, Continuum, the company behind Wakari, Anaconda and Conda has secured a new round of funding.
- Ibis
Ibis is a young Python data analysis framework with the goal of enabling data scientists and data engineers to be as productive working with big data as they are working with small and medium data today. The aim is to use Python as a true first-class language for Apache Hadoop, without compromises in functionality, usability, or performance.
