Data Science Decisions in Time

Sequential Decisions builds from math and algorithms that can be understood and used by Coursera Students. This course will start from a consideration of the simplest type of data streams and then gradually advance to more complex types of data and more nuanced decisions being made on that data. You will be able to: (a) program optimal decisions for data arriving from known distribution functions, (b) define error bars and nuanced hedges about ongoing data streams to reflect missing data and/or missing knowledge, (c)understand and use the connections from these models to further understand Markov Chains and Markov Processes and how these ideas connect to Reinforcement Learning and (d) Understand better the nuances between time-independent, time-dependent, one-dimensional and multi-dimensional data. The course is aimed at those working with data, this includes both those charged with analyzing the data and those in charge of making decisions based on that data

• Understand sequential testing and stopping data collection
• Program optimal decisions for data from known distributions
• Define error bars and nuanced hedges on data streams
• Use connections to Markov Chains and Processes
• Differentiate time-independent and multi-dimensional data

• Basic familiarity with the topic and its common terminology
• Readiness to practice through applied exercises or case-based work