ctb

Trained by physicists, with a BA in pure math, a PhD in molecular developmental
biology, and lots of open source code to my name, I am currently a biologist
trapped in a computer science department.  I work at the intersection of big
sequence data, novel computer science data structures and algorithms, and
biological hypothesis generation & validation.   

ctb

# let's use the CSV library: http://docs.python.org/library/csv.html
import csv
import urllib
from datetime import datetime

# here workouts is GLOBAL
workouts = []

def load_data(url):
    data = []

ctb

# let's use the CSV library: http://docs.python.org/library/csv.html
import csv
import urllib
from datetime import datetime

def load_data(url):
    data = []
    
    fp = urllib.urlopen('http://lyorn.idyll.org/~t/transfer/workout.csv')
    reader = csv.reader(fp)

ctb

Random algorithms and probabilistic data structures are algorithmically efficient and can provide shockingly good practical results.  I will give a practical introduction, with live demos and bad jokes, to this fascinating algorithmic niche.  I will conclude with some discussions of how our group has applied this to large sequencing data sets (although this will not be the focus of the talk).

ctb

Titus Brown received his BA in Math from Reed College in 1997, and his PhD in
Developmental Biology at Caltech in 2006.  He has worked in digital evolution,  
climate measurements, molecular and evolutionary developmental biology, and     
both regulatory genomics and transcriptomics.  His current focus is on
using novel computer science data structures and algorithms to explore
big sequencing data sets from metagenomics and transcriptomics.     

ctb

ctb

ctb

{
 "metadata": {
  "name": "course-02-week2-solutions"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {

ctb

this is a random bit of text I want to give to other people

ctb

{
 "metadata": {
  "name": "hw3-solutions-1-and-2"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {