Data Science Decisions in Time: Using Data Effectively from Coursera

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.

What's inside

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 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.

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Builds deep understanding of math and algorithms

Taught by experienced and well-respected instructors

Designed for professionals working with data

Provides a comprehensive study of sequential decisions

Covers a range of topics, from basic concepts to advanced techniques

Prerequisites may be required for some students

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Science Decisions in Time: Using Data Effectively with these activities:

Review Probability and Linear Algebra

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Refreshing your knowledge of probability and linear algebra will provide a solid foundation for understanding the concepts in this course.

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Review your notes or textbooks on probability and linear algebra.
Solve practice problems to test your understanding.
Seek help from a tutor or mentor if needed.

Curate Resources on Markov Processes

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Compiling resources related to Markov Processes will provide you with a valuable collection for future reference and deeper exploration.

Browse courses on Markov Processes

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Search for online resources on Markov Processes, including articles, tutorials, and videos.
Organize the resources into a structured format, such as a document or spreadsheet.
Annotate each resource with a brief description and your evaluation of its quality.

Review Pre-Calculus

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Review the trigonometric identities and properties of functions.

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Revisit unit circle and special triangle ratios.
Refresh properties of sine, cosine, and tangent functions.
Examine the graphs of trigonometric functions and their transformations.
Practice solving trigonometric equations.

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Industry Expert Mentorship

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Connect with experienced professionals to gain insights into real-world applications and best practices in sequential decision-making.

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Identify potential mentors in relevant fields
Reach out and schedule informational interviews
Seek guidance on career development and technical challenges

Discuss Sequential Decision Making

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Engaging in discussions with peers will help you clarify your understanding of sequential decision making and gain new perspectives.

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Find a study partner or join a study group.
Choose a topic related to sequential decision making.
Discuss your ideas and insights with your partner or group.
Work together to solve problems and answer questions.

Collaborative Study Groups

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Enhance understanding and critical thinking by discussing concepts, solving problems, and sharing perspectives with peers.

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Form a study group with 2-4 other students
Set regular meeting times and goals
Take turns presenting concepts, leading discussions, and solving problems

Explore Markov Chain Tutorials

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Following this series of tutorials will help provide a conceptual understanding of how Markov Chains work and how they can be applied to real-world scenarios.

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Identify introductory Markov Chain tutorials with high ratings.
Follow the instructions and work through the examples.
Apply the concepts you learned to solve practice problems.
Document the key concepts and techniques you learned.

Code Challenges

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Reinforce theoretical concepts and improve algorithmic thinking by solving challenging programming problems.

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Identify key concepts and algorithms
Break down the problem into smaller steps
Implement solutions and test correctness

Build a Markov Chain Model

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Building a Markov Chain model will provide you with hands-on experience and a deeper understanding of how they work.

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Identify a real-world scenario that can be modeled using a Markov Chain.
Create a state transition matrix for your model.
Implement the model in a programming language.
Analyze the output of your model and draw conclusions.

Interactive Simulations

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Develop a deeper understanding of complex concepts by creating interactive simulations that illustrate their behavior.

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Choose a specific concept or model to simulate
Design the simulation interface and logic
Implement the simulation using appropriate software tools

Data Analysis Project

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Apply sequential decision-making principles to real-world data and present findings in a well-structured report.

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Identify a suitable dataset and problem statement
Develop a decision-making model and algorithm
Analyze data and evaluate results

Solve Markov Decision Process Problems

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Solving Markov Decision Process problems will strengthen your understanding of the concepts and improve your problem-solving skills.

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Find a collection of Markov Decision Process practice problems.
Solve the problems using the concepts you learned in the course.
Analyze your solutions and identify areas for improvement.
Document the key concepts and techniques you used.

Study 'Reinforcement Learning: An Introduction'

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This book provides a comprehensive overview of reinforcement learning, which builds on the concepts of Markov Decision Processes.

View Reinforcement Learning, second edition: An... on Amazon

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Read the book thoroughly, taking notes on key concepts.
Solve the exercises and practice problems provided in the book.
Discuss the book's concepts with peers or a mentor.

Contribute to an Open-Source Markov Chain Library

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Contributing to an open-source Markov Chain library will provide you with practical experience and strengthen your understanding of the underlying algorithms.

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Identify a reputable open-source Markov Chain library.
Review the library's documentation and codebase.
Identify an area where you can contribute, such as adding a new feature or improving the documentation.
Submit a pull request with your changes.

Career center

Learners who complete Data Science Decisions in Time: Using Data Effectively will develop knowledge and skills that may be useful to these careers:

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Data Science Decisions in Time

Using Data Effectively

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