Johns Hopkins University
Data Science Decisions in Time: Using Data Effectively
Johns Hopkins University

Data Science Decisions in Time: Using Data Effectively

Thomas Woolf

Instructor: Thomas Woolf

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

25 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

25 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • By the end of the course you will: (1) understand sequential testing and thus when to stop collecting data and (2) how this concept is used today.

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Recently updated!

August 2024

Assessments

11 assignments

Taught in English

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This course is part of the Data Science Decisions in Time Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
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There are 5 modules in this course

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 Wald’s 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.

What's included

5 videos2 readings2 assignments1 discussion prompt

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

What's included

3 videos1 reading2 assignments1 discussion prompt

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.

What's included

2 videos1 reading2 assignments1 discussion prompt

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.

What's included

3 videos1 reading2 assignments1 discussion prompt

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.

What's included

2 videos1 reading3 assignments1 discussion prompt

Instructor

Thomas Woolf
Johns Hopkins University
4 Courses380 learners

Offered by

Recommended if you're interested in Data Analysis

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