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ThomasSiegmund/D3TableFilter. » Build a web app fast: Python, HTML & JavaScript resources. Wanna build a web app fast?

» Build a web app fast: Python, HTML & JavaScript resources

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?”. These resources have also been useful to existing programmers who know C, C++ or Java, but who want to embrace dynamic and web-based programming. 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.

JavaScript Resources Django. Rapid Web Prototyping with Lightweight Tools. The Problem with Data Science. Data Science is about learning from data, often using Machine Learning and statistics.

The Problem with Data Science

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). Graphically, this is how I think the process should look like: 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.

Predicting future returns of trading algorithms: Bayesian cone. 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.

Predicting future returns of trading algorithms: Bayesian cone

Follow her on twitter here. All of the models discussed here-within are available through our newly released library for finance performance and risk analysis called pyfolio. For an overview of how to use it see the Bayesian tutorial. 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? What can be learned from the predictive models? Why Bayesian models? Normal model This is the statistical model: While My MCMC Gently Samples. D3 Tutorial Table of Contents. Learn HTML5 and Get CSS Training – Microsoft Virtual Academy. Tutorials · mbostock/d3 Wiki. Wiki ▸ Tutorials Please feel free to add links to your work!

Tutorials · mbostock/d3 Wiki

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 Data Visualization and D3.jsJonathan Dinu + Ryan Orban, Udacity, 2014Data Visualization and Infographics with D3.jsAlberto Cairo + Scott Murray, Knight Center, 2015 Talks and Videos Build Interactive JavaScript Charts with D3 v4 Ben Clinkinbeard, November 2016Introduction to D3Curran Kelleher, Bay Area D3 Meetup, April 2015Free tagtree screencast - thinking with joinsAugust 2014For Example (Write-up) Eyeo Festival, June 2013.Visualizing Data with Web Standards (Slides) W3Conf, November 2011.SVG Open Keynote (Slides) Microsoft Research, October 2011.Use the Force!

Meetups Research Papers. Vincent: A Python to Vega Translator — Vincent 0.4 documentation. Vega. jStat Documentation. API Reference · vega/datalib Wiki.