Coursera Design Of Experiments
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The Art of Randomness
Harness the power of randomness (and Python code) to solve real-world problems in fun, hands-on experiments—from simulating evolution to encrypting messages to making machine-learning algorithms! The Art of Randomness is a hands-on guide to mastering the many ways you can use randomized algorithms to solve real programming and scientific problems. You’ll learn how to use randomness to run simulations, hide information, design experiments, and even create art and music. All you need is some Python, basic high school math, and a roll of the dice. Author Ronald T. Kneusel focuses on helping you build your intuition so that you’ll know when and how to use random processes to get things done. You’ll develop a randomness engine (a Python class that supplies random values from your chosen source), then explore how to leverage randomness to: Simulate Darwinian evolution and optimize with swarm-based search algorithms Design scientific experiments to produce more meaningful results by making them truly random Implement machine learning algorithms like neural networks and random forests Use Markov Chain Monte Carlo methods to sample from complex distributions Hide information in audio files and images, generate art, and create music Reconstruct original signals and images from only randomly sampled data Scientific anecdotes and code examples throughout illustrate how randomness plays into areas like optimization, machine learning, and audio signals. End-of-chapter exercises encourage further exploration. Whether you’re a programmer, scientist, engineer, mathematician, or artist, you’ll find The Art of Randomness to be your ticket to discovering the hidden power of applied randomness and the ways it can transform your approach to solving problems, from the technical to the artistic.
Designing with Data
Author: Rochelle King
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2017-03-29
On the surface, design practices and data science may not seem like obvious partners. But these disciplines actually work toward the same goal, helping designers and product managers understand users so they can craft elegant digital experiences. While data can enhance design, design can bring deeper meaning to data. This practical guide shows you how to conduct data-driven A/B testing for making design decisions on everything from small tweaks to large-scale UX concepts. Complete with real-world examples, this book shows you how to make data-driven design part of your product design workflow. Understand the relationship between data, business, and design Get a firm grounding in data, data types, and components of A/B testing Use an experimentation framework to define opportunities, formulate hypotheses, and test different options Create hypotheses that connect to key metrics and business goals Design proposed solutions for hypotheses that are most promising Interpret the results of an A/B test and determine your next move
Computer-Assisted Experiment Design in Psychology
Author: St. Clements University Academic Staff - Türkiye
language: en
Publisher: Prof. Dr. Bilal Semih Bozdemir
Release Date:
Computer-Assisted Experiment Design in Psychology The Need for Efficient Experiment Design Understanding Experiment Design Challenges Limitations of Traditional Experiment Design Methods Introducing Computer-Assisted Experiment Design Benefits of Computer-Assisted Experiment Design Improved Statistical Power and Precision Enhanced Experimental Control and Validity Reduced Time and Resources for Experiment Execution Optimized Participant Recruitment and Allocation Key Considerations in Computer-Assisted Experiment Design Experimental Variables and Hypotheses Identifying Independent and Dependent Variables Establishing Appropriate Control Conditions Minimizing Confounding Factors Designing Data Collection Protocols Selecting Appropriate Outcome Measures Ensuring Ethical Considerations Leveraging Computational Algorithms in Experiment Design Factorial Designs and Response Surface Methodology Adaptive Designs and Sequential Experimentation Bayesian Optimization and Adaptive Randomization Machine Learning Approaches in Experiment Design Case Studies in Computer-Assisted Experiment Design Improving Clinical Trial Design and Efficiency Enhancing Behavioral Intervention Studies Optimizing User Experience Research Integrating Computer-Assisted Design with Existing Workflows Overcoming Challenges and Limitations Ensuring Reproducibility and Transparency Addressing Regulatory Concerns and Best Practices Ethical Considerations in Automated Experiment Design Training and Upskilling Researchers Collaboration between Researchers and Computer Scientists The Future of Computer-Assisted Experiment Design Emerging Trends and Innovations Integrating with Artificial Intelligence and Machine Learning Enhancing Interdisciplinary Collaboration Expanding Applications beyond Psychology Ensuring Responsible and Equitable Implementation Conclusion: Unlocking the Potential of Computer-Assisted Experiment Design