Data Science

After the Challenger exploded in 1986, killing all 7 astronauts aboard, an investigation into the cause was immediately launched. 

In the cold temperatures the night before the launch, the o-rings that seal off the fuel tanks from the rocket boosters became inflexible, so they did not seal properly, which led to the fuel tank explosion. NASA knew that there could be o-ring problems, but performed the analysis of their data incorrectly and ended up massively underestimating the risk associated with the cold temperatures. 

In this episode, we'll unpack the mistakes they made. We'll talk about how they excluded data points that they thought were irrelevant but which actually were critical to recognizing a fatal pattern.

Statistical Mistakes and the Challenger Disaster

Let’s talk money. As a “hot” career right now, data science can pay pretty well. But for an individual person matched with a specific job or industry, how much should someone expect to make? 

Since Katie was on the job market lately, this was something she’s been researching, and it turns out that data science itself (in particular linear regressions) has some answers. 

In this episode, we go through a survey of hundreds of data scientists, who report on their job duties, industry, skills, education, location, etc. along with their salaries, and then talk about how this data was fed into a linear regression so that you (yes, you!) can use the patterns in the data to know what kind of salary any particular kind of data scientist might expect.

Careers in Data Science

This machine learning algorithm beat the human champions at Jeopardy.  What is... Watson?

Watson

Artificial intelligence includes a number of different strategies for how to make machines more intelligent, and often more human-like, in their ability to learn and solve problems.  An ambitious group of researchers is working right now to classify all the approaches to AI, perhaps as a first step toward unifying these approaches and move closer to strong AI.  In this episode, we'll touch on some of the most provocative work in many different subfields of artificial intelligence, and their strengths and weaknesses.

Relevant links:
https://www.technologyreview.com/s/544606/can-this-man-make-aimore-human/
https://www.youtube.com/watch?v=B8J4uefCQMc
http://venturebeat.com/2013/11/29/sentient-code-an-inside-look-at-stephen-wolframs-utterly-new-insanely-ambitious-computational-paradigm/
http://www.slate.com/articles/technology/bitwise/2014/03/stephen_wolfram_s_new_programming_language_can_he_make_the_world_computable.html

Paradigms in Artificial Intelligence

It's storytime this week--the story, from beginning to end, of how Katie designed and built the main project for Udacity's Intro to Machine Learning class, when she was developing the course.  The project was to use email and financial data to hunt for signatures of fraud at Enron, one of the biggest cases of corporate fraud in history; that description makes the project sound pretty clean but getting the data into the right shape, and even doing some dataset merging (that hadn't ever been done before), made this project much more interesting to design than it might appear.  Here's the story of what a data analysis like this looks like...from the inside.

Inside a Data Analysis: Fraud Hunting at Enron

Competing in a machine learning competition on Kaggle is a kind of rite of passage for data scientists.  Losing unexpectedly at the very end of the contest is also something that a lot of us have experienced.  It's not just bad luck: a very specific combination of overfitting on popular competitions can take someone who is in the top few spots in the final days of a contest and bump them down hundreds of slots in the final tally.

How to Lose at Kaggle

A huge part of the ascent of deep learning in the last few years is related to advances in computer hardware that makes it possible to do the computational heavy lifting required to build models with thousands or even millions of tunable parameters. This week we'll pretend to be electrical engineers and talk about how modern machine learning is enabled by hardware.

Data Science

Kai