Data Science Decisions in Time
Offered By: Johns Hopkins University via Coursera
Course Description
Overview
This specialization is for data scientists, applied mathematics, statisticians, or computer scientists, involved with decision making from data. The four courses build a firm foundation in how decisions can be meaningfully made, with confidence, on data collected in a streaming application. The level is intermediate, assuming basic math and statistics skills.
Syllabus
Course 1: Data Science Decisions in Time: Using Data Effectively
- Offered by Johns Hopkins University. Sequential Decisions builds from math and algorithms that can be understood and used by Coursera ... Enroll for free.
Course 2: Data Science Decisions in Time:Sequential Hypothesis Testing
- Offered by Johns Hopkins University. This is part of our specialization on Making Decision in Time. For this second course we start with a ... Enroll for free.
Course 3: Data Science Decisions in Time: Information Theory & Games
- Offered by Johns Hopkins University. This is part of our specialization on Making Decision in Time. For this third course we start with an ... Enroll for free.
Course 4: Data Science Decisions in Time: Using Causal Information
- Offered by Johns Hopkins University. This is the fourth course in the specialization and is aimed at those with basic knowledge of ... Enroll for free.
- Offered by Johns Hopkins University. Sequential Decisions builds from math and algorithms that can be understood and used by Coursera ... Enroll for free.
Course 2: Data Science Decisions in Time:Sequential Hypothesis Testing
- Offered by Johns Hopkins University. This is part of our specialization on Making Decision in Time. For this second course we start with a ... Enroll for free.
Course 3: Data Science Decisions in Time: Information Theory & Games
- Offered by Johns Hopkins University. This is part of our specialization on Making Decision in Time. For this third course we start with an ... Enroll for free.
Course 4: Data Science Decisions in Time: Using Causal Information
- Offered by Johns Hopkins University. This is the fourth course in the specialization and is aimed at those with basic knowledge of ... Enroll for free.
Courses
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This is part of our specialization on Making Decision in Time. For this third course we start with an intriguing study on SFPark and build new insights into the ideas that flow from this direction. The ending point should bring new code and new algorithm insights into perspective, and use, by many computer and data scientists.
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This is the fourth course in the specialization and is aimed at those with basic knowledge of statistics, probability and linear algebra. It will prove to be especially interesting for those with datasets that are being used to make decisions: either business, medical, or technology based.
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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.
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This is part of our specialization on Making Decision in Time. For this second course we start with a landmark paper from Chernoff and build new insights into the ideas that his paper sparked. The ending point should bring new code and new algorithm insights into perspective, and use, by many computer and data scientists.
Taught by
Thomas Woolf
Tags
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