Johns Hopkins University
Data Science Decisions in Time Specialization
Johns Hopkins University

Data Science Decisions in Time Specialization

Make higher quality decisions based on learning from data in real time. Decisions often are made after all data is collected. But, many applications need decision while data is being collected.

Thomas Woolf

Instructor: Thomas Woolf

Included with Coursera Plus

Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 15 hours a week
Flexible schedule
Learn at your own pace
Get in-depth knowledge of a subject
Intermediate level

Recommended experience

3 months
at 15 hours a week
Flexible schedule
Learn at your own pace

Advance your subject-matter expertise

  • Learn in-demand skills from university and industry experts
  • Master a subject or tool with hands-on projects
  • Develop a deep understanding of key concepts
  • Earn a career certificate from Johns Hopkins University
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Specialization - 4 course series

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

Category: Control Chart
Category: A:B testing
Category: Time Series Analysis and Forecasting
Category: Predictive Analytics
Category: Machine Learning Methods
Category: Anomaly Detection
Category: Data-Driven Decision-Making
Category: Reinforcement Learning
Category: Sampling (Statistics)
Category: Testing for Vaccines
Category: Data Science
Category: working with sequential data
Category: Markov Model
Category: Bayesian Statistics
Category: Wald's ideas for stopping
Category: Statistical Analysis
Category: Statistical Methods

What you'll learn

Skills you'll gain

Category: Computational Thinking
Category: Biostatistics
Category: Algorithms
Category: Probability & Statistics
Category: Machine Learning Methods
Category: Data-Driven Decision-Making
Category: Connections to Wald and Chernoff
Category: Dynamic Hypothesis Testing
Category: Statistical Hypothesis Testing
Category: Image Analysis
Category: Medical Science and Research
Category: Analytics
Category: Algorithms for multiple hypothesis testing
Category: Data Science
Category: New approaches for blood analysis
Category: A/B Testing
Category: Computer Vision
Category: Bioinformatics
Category: Bayesian Statistics
Category: Statistical Inference
Category: Clinical Trials
Category: Data Structures
Category: Machine Learning Algorithms
Category: Statistical Methods

What you'll learn

Skills you'll gain

Category: Algorithms
Category: A:B testing
Category: Data Science
Category: Risk Modeling
Category: Game Theory
Category: Adaptive Game Play
Category: Data-Driven Decision-Making
Category: Information Theory
Category: Machine Learning
Category: Cybersecurity
Category: Zero Sum Games
Category: Artificial Intelligence

What you'll learn

Skills you'll gain

Category: Data Analysis
Category: Business Analytics
Category: Data-Driven Decision-Making
Category: Machine Learning
Category: Medical Science and Research
Category: Analytics
Category: Treatment Planning
Category: A/B Testing
Category: causal forests
Category: Decision Making
Category: structural equations
Category: Random Forest Algorithm
Category: Clinical Trials
Category: directed acyclic graphs
Category: causal models
Category: Personalized Service

Instructor

Thomas Woolf
Johns Hopkins University
4 Courses599 learners

Offered by

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