Data Skeptic

This episode introduces the idea of a Markov Chain. A Markov Chain has a set of states describing a particular system, and a probability of moving from one state to another along every valid connected state. Markov Chains are memoryless, meaning they don't rely on a long history of previous observations. The current state of a system depends only on the previous state and the results of a random outcome.

Markov Chains are a useful way method for describing non-deterministic systems. They are useful for destribing the state and transition model of a stochastic system.

As examples of Markov Chains, we discuss stop light signals, bowling, and text prediction systems in light of whether or not they can be described with Markov Chains.

Direct download: MINI_Markov_Chains.mp3
Category:miniepisode -- posted at: 12:00am PDT

Nicole Goebel joins us this week to share her experiences in oceanography studying phytoplankton and other aspects of the ocean and how data plays a role in that science.


We also discuss Thinkful where Nicole and I are both mentors for the Introduction to Data Science course.

Last but not least, check out Nicole's blog Data Science Girl and the videos Kyle mentioned on her Youtube channel featuring one on the diversity of phytoplankton and how that changes in time and space.

Direct download: Oceanography_and_Data_Science.mp3
Category:general -- posted at: 12:06am PDT

This episode explores Ordinary Least Squares or OLS - a method for finding a good fit which describes a given dataset.

Direct download: MINI_Ordinary_Least_Squares_Regression.mp3
Category:miniepisode -- posted at: 12:43am PDT