jboner

Latency Comparison Numbers (~2012)
----------------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

iamatypeofwalrus

What

Roll your own iPython Notebook server with Amazon Web Services (EC2) using their Free Tier.

What are we using? What do you need?

• An active AWS account. First time sign-ups are eligible for the free tier for a year
• One Micro Tier EC2 Instance
• With AWS we will use the stock Ubuntu Server AMI and customize it.
• Anaconda for Python.
• Coffee/Beer/Time

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens

thertrader

###############################################################################
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.

debasishg

General Background and Overview

1. Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
2. Models and Issues in Data Stream Systems
3. Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
4. Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
5. [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep=rep1&t

thertrader

This Shiny application is designed to help analysing trading strategies. It is an ongoing project that I improve when time allows. Feel free to get in touch should you have any suggestion.

*How to use the App as it is?
The App uses as input several csv files (one for each strategy). Each file has two columns: date and daily return. There is an example of such a file in the Github repository. The code is essentially made of 3 files.
-ui.R: controls the layout and appearance of the app
-server.R: contains the instructions needed to build the app. You can load as much strategies as you want as long as the corresponding csv file has the right format (see below).
-shinyStrategyGeneral.R: loads the required packages and launches the app
put ui.R and server.R file in a separate directory

In the server.R file change the inputPath, inputFile and keepColumns parameters to match your setting. The first two are self explanatory the third one is a list of column names within the csv file. Keep only date and daily return

chrishwiggins

ICERM Data Science: references and links

Talk delivered 2015-07-29 at ICERM workshop on "mathematics and data science"

PDF on slideshare of talk slides

context

• google search trends: data science

vasanthk

System Design Cheatsheet

Picking the right architecture = Picking the right battles + Managing trade-offs

Basic Steps

1. Clarify and agree on the scope of the system

• User cases (description of sequences of events that, taken together, lead to a system doing something useful)
  • Who is going to use it?
  • How are they going to use it?

cube-drone

Automation for the People

Long ago, the first time I read "The Pragmatic Programmer", I read some advice that really stuck with me.

"Don't Use Manual Procedures".

This in the chapter on Ubiquitous Automation. To summarize, they want you to automate all the things.

The trouble was that I hadn't much of an idea how to actually go