Moving to tumblr

This blog is moving to a more permanent home. Check http://practical-data-science.tumblr.com/ for more data science news. 

A Programmer's Guide to Data Mining

A guide to practical data mining, collective intelligence, and building recommendation systems

Unix one-liners

Some great tips here.

Explain shell

A cool way to visualize the components of complex unix commands. Very useful.

Statistical Formulas for Programmers

Potentially a useful resource. 

What Beer Would You Like? Building a Recommendation System in Python

A nice little ipython notebook with some data and examples for building simple recommender systems in python

How Python became the language of choice for data science

A good read giving perspective on other language choices for doing data science.

Bokeh – Interactive web visualization library in Python

This is simply incredible, and it's already installed as part of the Anaconda library. 

Video: Practical Medium Data Analytics with Python

From the recent pydata conference.

Woosh: Full Text Search with Python

Add search functionality to your website or app!

scipy lecture notes

A great collection on many scipy topics.

dataset: databases for lazy people in python

This looks like a promising library, SQL databases without the sql? 

Booz Allen Field Guide to Data Science

I haven't read this, so I can't actually vouch for it. However, it's been getting a lot of coverage in my twitter stream.

The central limit theorem, explained with bunnies and dragons

cute. and educational.

Under the Hood: Facebook Entity Graph

Graphs are one way to represent relationships amongst things. Here's an interesting article on FB's entity graph.

Git Cheat Sheet

A list of common commands and options.

ggplot in python

One of the best features of the R programming language is its fantastic ggplot2 visualization library. Looks like there is now a python clone, complete with a nice functional interface. I don't think all the wrinkles have been ironed out, but still pretty cool. 

pandasql: manipulating pandas dataframes with SQL

Pretty cool. I haven't really put this through it's paces, but it's neat after a first impression.

Probabilistic Programming & Bayesian Methods for Hackers

An online book, looks like it's pretty approachable. Uses python. 

Advice for Applying Machine Learning

From Stanford Professor Andrew Ng

NBA Superstar Chris Bosh: Here’s Why You Should Learn to Code

From the Olympic gold medalist.

Teaching Kids Programming: A Collection of Resources

Some interesting material linked here, for a variety of age groups. 

A Practical Intro to Data Science

Some good supplementary materiel here. 

Kaggle Data Mining Competitions

A host of data mining competitions. Maybe a great source of data for you to play with 

When unix pipes get names

A useful trick that's new to me.

Slides: Machine Learning with scikit-learn

A nice guide on python's premier machine learning library from one of its developers. Looking forward to the second half of the class!

A Green Heatmap of Brooklyn

An interesting map showing the treelined streets and farmer's markets in Brooklyn.

Hadoop for Data Science (Slides & Video)

From a recent NYC Data Science Meetup

Interesting TED talk about data and algorithm and using data

How Amy Webb Hacked Online Dating

Simple Citi Bike Analysis

Inspired by a students digging into citi bike, i put together this very simple ipython notebook.

The Target Story

This was a fairly major story about the use (and abuse) of applied data science in retail. Target was able to discover that a young woman was pregnant before her father knew, by looking at her purchase patterns.

NY Times
Forbes

Python the Hard Way

Just a reminder to those looking for the free online version of Python the Hard Way.

Updated Syllabus

The syllabus has recently been updated to include course office hours.

1. Josh: Wednesdays, 2-3pm, KMC 8-171 and by appointment
2. Kumar: Mondays, 2-4pm, KMC 7-100

NYC Data Hack Week

A great opportunity to get involved with NYC's data community, network with those with a variety of data skills. From the site:

Passionate about data? Come to the DataWeek Challenge! An event on November 2nd that will challenge the city's hackers, data scientists, and data newbies to test their skills in the ultimate data hacking competition.
Held on November 2nd at the conclusion of this year's NYC DataWeek. The Data Week Challenge is a full day of hacking challenges that brings together developers with NYC Open Data, as well as hand picked data from a number of compaines in demonstrating the potential of it to address civic challenges in a fun, friendly competition.

Data ChallengesWill be announced soon! Some of the core areas you can look forward to are Data Visualization, Data Scraping, Analysis and Data Driven Application development, and we'll also have breakout talks about the most important issues facing the data world these days.

Things to look forward to:

• Meeting other amazing data entusiasts

• A friendly and fun data-driven competition.

• rizes for completing the best challenges.

• Sharing of hacks for sourcing, scraping, cleaning, and visualizing data

• A wide set of data to hack on from government and enterprise

Datasets released by google

Might be a good course project in here somewhere.

Guides on Git and Github

Github is a great way to store and share code you've written. However, if you've never used it before, it can seem a bit confusing! Here are some links that should prove helpful. Sign up for a free account and give 'em a shot

• Try Git: An interactive git tutorial
• Github Guides: Official Github Youtube tutorials
• Git for Beginners: A practical guide with many links

Think Stats

Think Stats, one of the texts that we'll be using for this course is available for free online from the author. If you prefer a hard copy, it's only about $20 on Amazon. 

New Room

It's strange that we would even need to think of this. Unfortunately the classroom we were initially assigned only had a single power outlet. Not enough for a programming-intensive class! Fortunately, we've located a better option. From now on, we'll be meeting in KMC 4-80. See you all there!

Course Objectives Posted

I've added an outline of some course objectives. This should give you a rough estimate of what you can expect to learn throughout the semester!

Syllabus Updated

A slight update to the course syllabus. Not much to worry about here. 

Homework 0 posted

The purpose of this initial homework assignment is to ensure that you're set up with the tools that we'll be using throughout this course, namely the unix terminal and the python programming language. Don't worry if these concepts are foreign at first, we'll be making sure that everyone gets up to speed. Find homework 0 here. 

Course Website

Here, hosted on Github. A repository for some key material and links to course resources.

Syllabus: Practical Data Science 2013

Here. Still somewhat in flux. Versioning on the course github here.