home | copyright ©2016, tim@menzies.us

overview | syllabus | src | submit |
chat

---

Home

Many software companies now learn their policies via data-driven methods. Modern practitioners treat every planned feature as an experiment, of which only a few are expected to survive. Key performance metrics are carefully monitored and analyzed to judge the progress of a feature. Even simple design decisions such as the color of a link are chosen by the outcome of software experiments.

This subject will explore methods for designing data collection experiments; collecting that data; exploring that data; then presenting that data in such a way to support business-level decision making for software projects.

News	Lectures	Homework	Review	Cool stuff
	Ethics Visualizations Defect prediction Privacy Mistakes Data reduction Discretization Bayes classifiers Incremental learning Decision trees Tables Stats Verification studies Lecture2: Misc On Average What is S.O.S.? Reading12345678	hw8 hw7 hw6 hw5 hw4 Project notes hw3 hw2 hw1	Review8 Review7 Review6 Review5 Review4 Review3 Review2	Discretization2 Discretization1 Anomaly detection Bayes nets