Nate Busa

data-engineering. How to build an awesome data engineering team

deepcredit. How to predict credit defaulting?

geo-services.scikit-learn. Geo-Located Data: Extracting Patterns from Mobile Data using Scikit-Learn and Cassandra

deepchurn. A few demos how to use deep learning for classification of small data sets for marketing and cyber-security

kernelgateway_demos. Some demo's to get you started exposing APIs from your jupyter notebooks.

cassandra_mock. A minimalist mock of cassandra in python

deepnumbers. A set of educational deep learning demos applied to the MNIST dataset

geo-services-tutorial. Geo-Located Data: Extracting Patterns from Mobile Data Using Scikit-Learn and Cassandra Learn how to extract patterns and detect anomalies within geo-located data, using machine learning clustering algorithms using Scikit-Learn and Cassandra with Python, Scikit-Learn, and Scala

datafaucet. Productivity Utilities for Data Science with Python Notebooks

ansible-role-centos-jupyterhub. Jupyterhub on CentOS 7, configurable spawner (sudo, docker), jupyter lab support

restr. An exercise combining akka actors and phantom a scala asynchrounous client library for cassandra.

databox. A configurable datalab-in-a-box environment

ansible-role-centos-python. A python ansible provisioning role for centos/rhel

REngine. General Java interface to R supporting multiple back-ends such as JRI and Rserve

jupyterhub-ansible-deploy. Provisioning a datalab with jupyterhub and tons of data science libraries for Python, R, and Scala

Java2R. Some examples about how to connect java to R using RCaller and RServe/REngine

dsp-titanic. Data Science Example for CI/CD Data Science Platform with Concourse, Kubernetes, Binderhub

resume. Up to date resume, and other work/passion related material

breakfast. How to prepare breakfast in a reactive, asynchronous way in scala

autoscience. An educational project on how to build data-driven apps with Jupyter, Python, Spark, and some frontend magic.

kaggle-titanic. A tutorial for Kaggle's Titanic: Machine Learning from Disaster competition. Demonstartes basic data munging, analysis, and visualization techniques. Shows examples of supervised machine learning techniques.

dlf-tutorial. A tutorial on how to use the datalabframework fro ETL and ML

natbusa.github.io. Nat Busa - Data Driven Stories

wikipedia. A wikipedia live search tutorial using hadoop, cassandra, python, and angular.js

Play20. Play framework 2.0

docker-stacks. Opinionated stacks of ready-to-run Jupyter applications in Docker.

spark-notebook. Interactive and Reactive Data Science using Scala and Spark.

pyzmq. Py0MQ: Python bindings for zeromq

coral. Programming streaming patterns on top of akka