Data Science Decisions in Time: Using Data Effectively

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Data Science Decisions in Time: Using Data Effectively

This course is part of Data Science Decisions in Time Specialization

Instructor: Thomas Woolf

Included with

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5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

5 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

3 weeks to complete

at 10 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.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

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

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.

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

5 videos Total 37 minutes

Introduction to Specialization 2 minutes
Introduction to First Course 1 minute
Wald Sequential Sampling 6 minutes
Control Charts 12 minutes
Vaccine Sequential Testing 15 minutes

2 readings Total 20 minutes

Welcome Reading 10 minutes
Seeing into Wald's thinking 10 minutes

2 assignments Total 210 minutes

How well can you think in a Sequential Setting? 30 minutes
Quiz 1 180 minutes

1 discussion prompt Total 10 minutes

Introduce yourself to the class 10 minutes

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

3 videos Total 29 minutes

A:B testing with a simple Thompson Sampling Algorithm 9 minutes
Neglected Tropical Diseases: How can we tell the risk? 10 minutes
Neglected Tropical Diseases: Thinking in the Spatial Dimension 10 minutes

1 reading Total 10 minutes

Do you know how to setup Thompson Sampling? 10 minutes

2 assignments Total 210 minutes

What does it mean to use Thompson Sampling for a Sequential Decision? 30 minutes
Quiz 2 180 minutes

1 discussion prompt Total 10 minutes

Give an example of when you may use Thompson Sampling 10 minutes

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

2 videos Total 18 minutes

Introduction: Climate Change as an Example 7 minutes
Gaussian Models for Change Point Determination 11 minutes

1 reading Total 10 minutes

Would you recognize a Change Point if you saw it? 10 minutes

2 assignments Total 225 minutes

Can you connect Thompson Sampling and Sequential Sampling? 45 minutes
Untitled 180 minutes

1 discussion prompt Total 10 minutes

Describe a Change Point that you have observed in a dataset 10 minutes

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

3 videos Total 18 minutes

Bridges and Buildings: How to Model Markov Chains for Failure Rates 8 minutes
Making Plans for Failure: A Geometric Model 5 minutes
Chains as Matrix Operations 5 minutes

1 reading Total 10 minutes

How would you setup a Markov Chain? 10 minutes

2 assignments Total 230 minutes

Are you ready to use a Markov model for a Decision Process? 50 minutes
Untitled 180 minutes

1 discussion prompt Total 10 minutes

Please share any experiences you have had with preventive maintenance 10 minutes

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

2 videos Total 14 minutes

Making Decisions for Resource Use in an Emergency Department 7 minutes
How to Optimize Drug Treatment Plans 7 minutes

1 reading Total 10 minutes

Would you want to formulate your decisions within a Markov Decision Process framework? 10 minutes

3 assignments Total 410 minutes

Can you describe how well a Markov Decision Process works? 50 minutes
Quiz 5 180 minutes
Final Exam 180 minutes

1 discussion prompt Total 10 minutes

How would you optimize a drug treatment plan based on this week's material? Do you have a particular disease and drug regime that you can share knowledge about? 10 minutes

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Instructor

Thomas Woolf

Johns Hopkins University

4 Courses 1,020 learners

Offered by

Johns Hopkins University

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