12 Devs of Xmas I’d like to start off by asking what seems like quite a simple question: What is the difference between data and information? And taking it a step further, where does knowledge fit into this definition? Not as easy to answer as you might expect, is it! Before we start getting our hands dirty creating some awesome data visualisations, it’s going to be useful to learn the definitions of – and difference between – these three words. Data is a set of unprocessed facts (the word comes from the latin datum which means “that which is given”). Information is what you get after you’ve processed the data. Knowledge is perhaps the most difficult to define, but the definition which I think makes most sense in this instance is how we use our past experiences to decide what to do based on information. So what are we going to build, and what with? There are a number of tools out there to help you to create charts and visualisations. A brief introduction to d3 There are four main parts to a d3 project:
Tutorials · mbostock/d3 Wiki Wiki ▸ Tutorials Please feel free to add links to your work!! Tutorials may not be up-to-date with the latest version 4.0 of D3; consider reading them alongside the latest release notes, the 4.0 summary, and the 4.0 changes. Introductions & Core Concepts Specific Techniques D3 v4 Blogs Books Courses D3.js in Motion (Video Course)Curran Kelleher, Manning Publications, September 2017D3 4.x: Mastering Data Visualization Nick Zhu & Matt Dionis, Packt. Talks and Videos Meetups Research Papers D3: Data-Driven DocumentsMichael Bostock, Vadim Ogievetsky, Jeffrey HeerIEEE Trans.
SPARQL SPARQL (pronounced "sparkle", a recursive acronym for SPARQL Protocol and RDF Query Language) is an RDF query language, that is, a semantic query language for databases, able to retrieve and manipulate data stored in Resource Description Framework format.[2][3] It was made a standard by the RDF Data Access Working Group (DAWG) of the World Wide Web Consortium, and is recognized as one of the key technologies of the semantic web. On 15 January 2008, SPARQL 1.0 became an official W3C Recommendation,[4][5] and SPARQL 1.1 in March, 2013.[6] SPARQL allows for a query to consist of triple patterns, conjunctions, disjunctions, and optional patterns.[7] Implementations for multiple programming languages exist.[8] "SPARQL will make a huge difference" making the web machine-readable according to Sir Tim Berners-Lee in a May 2006 interview.[9] Advantages[edit] The example below demonstrates a simple query that leverages the ontology definition "foaf", often called the "friend-of-a-friend" ontology.
Vega Integrating AllegroGraph with MongoDB Introduction AllegroGraph has implemented extensions allowing users to query MongoDB databases using SPARQL and to execute heterogeneous joins, even though MongoDB, a NoSQL JSON document store, does not natively support joins, SPARQL or RDF-enabled linked data. In this document, we describe how to configure AllegroGraph and MongoDB to work together. Interfacing with MongoDB The steps for using MongoDB with AllegroGraph are: Installing MongoDBSynchronizing MongoDB data with AllegroGraph dataConfiguring AllegroGraph with MongoDB connection settings Please note that populating and maintaining the MongoDB database is separate from adding or deleting triples from the AllegroGraph triple-store. Installing MongoDB MongoDB is not a Franz Inc. product. In the rest of this document, we assume you have the MongoDB server installed and running on a computer you can access. Synchronizing MongoDB data with AllegroGraph You must link AllegroGraph data and MongoDB data referring to the same object. ? Footnotes
Predicting future returns of trading algorithms: Bayesian cone | Quantopian Blog Authors: Sepideh Sadeghi and Thomas Wiecki Foreword by Thomas This blog post is the result of a very successful research project by Sepideh Sadeghi, a PhD student at Tufts who did an internship at Quantopian over the summer 2015. All of the models discussed here-within are available through our newly released library for finance performance and risk analysis called pyfolio. When evaluating trading algorithms we generally have access to backtest results over a couple of years and a limited amount of paper or real money traded data. Here, we will briefly introduce two Bayesian models that can be used for predicting future daily returns. All of these models are available through our newly released library for finance performance and risk analysis called pyfolio. How do we get the model inputs? At Quantopian we have built a world-class backtester that allows everyone with basic Python skills to write a trading algorithm and test it on historical data. Why Bayesian models? Normal model T-model
Jena (framework) Jena supports serialisation of RDF graphs to: The Problem with Data Science | Quantopian Blog Data Science is about learning from data, often using Machine Learning and statistics. To do so, we can build statistical models that provide answers to our questions or make predictions based on data we have collected. Ideally, we build the model that most accurately describes our data, makes the best predictions, and provides the answers of interest. Once we have our dream model we just have to figure out how to fit it to data (i.e. do inference). Unfortunately, as anyone who has done such a thing can attest, it can be extremely difficult to fit your dream model and requires you to take many short-cuts for mathematical convenience. So a lot of times we don't build the models we think best capture our data but rather the models we can make inference on. Think about that for a second, you're not tied to pre-specified statistical model like a frequentist T-Test that some statistician worked out how to do inference on. What about Machine Learning?
mikeaddison93/blueprints » Build a web app fast: Python, HTML & JavaScript resources Wanna build a web app fast? Know a little bit about programming but want to build a modern web app using two well-supported, well-documented, and universally accessible languages? You’ll love these Python, HTML/CSS, and JavaScript resources. I’ve been sharing these documents with friends who ask me, “I want to start programming and build a web app, where do I start?”. Python Resources Python is the core programming language used at Parse.ly. I’ve written a blog post with some original materials for learning Python, import this — learning the Zen of Python with code and slides. This is a good starting point, but you may also find these resources very helpful: For absolute beginners, “Learn Python the Hard Way”. HTML/CSS Resources In order to build up web applications, you’ll need to write your front-ends in HTML and CSS. Since HTML is basically useless without CSS, you can get by with a short tutorial on HTML and then more advanced tutorials on CSS styling. JavaScript Resources Django Tornado
mikeaddison93/mongo-tools ThomasSiegmund/D3TableFilter