Jupyter DeclarativeWidgets make data exploration easy!

Over the past year, we have built many demonstrations using declarativewidgets to create dashboards and good-enough applications in the Jupyter Notebook. We’ve discovered several patterns that are in great need of simplification. One of these patterns is data exploration. By data exploration, I mean the ability for the user to query and visualize a data set. In declarativewidgets, we make it simple to bind visual elements to […]

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Visualizing Big Data with Spark and Scala

While exploring data analytics with Apache Spark, the team came to the realization that there are many Python examples, but resources for Scala are somewhat lacking. In particular, there are few data visualization examples in Scala. Python’s predominant visualization module is Matplotlib, but we struggled to find a Scala library that offered the same breadth of functionality and granularity of control. Brunel In our […]

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Load Your Data Into a Jupyter Notebook

You’ve heard all the flashy statistics about big data, like how every day more than 2.5 quintillion bytes of data is created and that more data has been created in the last two years alone than every previous year combined (IBM). Here’s another one to add to the list: 99.5% of newly created data is never analyzed (MIT). Only half a […]

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InterPlanetary File System (IPFS) on Jupyter

The fundamental concept of IPFS is that instead of looking for locations, as with HTTP, you look for the content of a file.  (erisindustries) When you say Blockchain, Git, BitTorrent, I hear: Directed acyclic graphs (like git) with hashed hierarchical checkpoints (like a blockchain or merkle trees) distributed peer to peer (like bit torrent). This is literally what IPFS is, and […]

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Jupyter Notebooks as RESTful Microservices

“Data science enables the creation of data products.” – Mike Loukides in What is data science? Data products take on many forms. Web articles, dashboard applications, and cloud services are all common vehicles for delivering value from data. Tools that help produce artifacts such as these are a necessary part of any data mining methodology. In the Project Jupyter ecosystem, many […]

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Case Study: Cloud Analytics for an Assisted Reproductive Market

Imagine you are the CIO of a healthcare firm and a critical business decision is pending marketplace insights. Your data science skills are limited, your data is too big for your spreadsheet application, and your budget cannot afford a proprietary software solution. What do you do? Businesses often have situational analytic needs that require access across multiple data sources. Healthcare […]

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Apache Spark – Utilizing Access Point Wi-Fi Data

Intro Did you know that Wi-Fi routers used within your home or outside in public are capable of collecting a wealth of information about your mobile devices even if you never actually sign in and connect to the Internet? Wi-Fi is ubiquitous in today’s world and cell phones and other mobile devices are almost always either passively or actively probing […]

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Dynamic Dashboards from Jupyter Notebooks

A Jupyter notebook is an excellent means of capturing code, text, widgets, graphics, and other rich media in a computational narrative that distills data into insights. Today, notebook authors can share their notebooks for others to view and run in the Jupyter Notebook web application. Authors can also transform their notebooks into a variety of static formats for ease-of-viewing in […]

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