Data Science Decisions in Time: Using Data Effectively

Johns Hopkins University via Coursera

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Overview

Data Science Decisions in Time: Using Data Effectively is a course designed to empower professionals with the ability to harness data science for timely and impactful decision-making. In an era where organizations must act quickly in the face of changing conditions, this course equips learners with the tools to analyze data, extract insights, and apply them to real-world problems in a dynamic environment. The course covers key data science concepts such as data collection, cleaning, visualization, modeling, and interpretation, with...

Syllabus

Wald and Sequential Decisions

This module introduces the class and the approach to teaching it to be used for the next five weeks. We begin with simple sequential data, similar to Walds model: data arrives from a distribution and is not time dependent. This can be generative data. We then explore increasingly complex data from distributions collected for health or business reasons. We finish the week with connections to code work and to AI.

Thompson Sampling

This module is the bridge into Markov Processes and Markov Chains. Thompson sampling is an old algorithm, that has been revived and is currently in-use on many challenging problems. By understanding this material and the connections to last week and to the week ahead, students will be well positioned to have mastered this first course in the specialization

Change Points

Change points are locations where the previously stationary distributions of the last two modules shift to a new distribution In a manufacturing line this could be due to a new batch of materials that arrive with different characteristics, so the failure rate changes.

Markov Chains

Markov chains describe a sequence of state changes. They are often used to describe complex transitions between states and are a primary modeling tool for improving understanding of a complex system. We will use them as a model for how sequential data may be produced by a more complex system.

Markov Decision Processes

The next step in modeling ability is Markov processes with decisions. This connects to modern research in reinforcement learning and enables optimization over the sets of decisions for an optimal outcome. In this last week of the first course we will cover the basics of how these Markov Decision Processes can be parameterized and what they mean.