Important note: The second assignment in this course covers the topic of Graph Analysis in the Cloud, in which you will use Elastic MapReduce and the Pig language to perform graph analysis over a moderately large dataset, about 600GB. In order to complete this assignment, you will need to make use of Amazon Web Services (AWS). Amazon has generously offered to provide up to $50 in free AWS credit to each learner in this course to allow you to complete the assignment. Further details regarding the process of receiving this credit are available in the welcome message for the course, as well as in the assignment itself. Please note that Amazon, University of Washington, and Coursera cannot reimburse you for any charges if you exhaust your credit.
While we believe that this assignment contributes an excellent learning experience in this course, we understand that some learners may be unable or unwilling to use AWS. We are unable to issue Course Certificates for learners who do not complete the assignment that requires use of AWS. As such, you should not pay for a Course Certificate in Communicating Data Results if you are unable or unwilling to use AWS, as you will not be able to successfully complete the course without doing so.
Making predictions is not enough! Effective data scientists know how to explain and interpret their results, and communicate findings accurately to stakeholders to inform business decisions. Visualization is the field of research in computer science that studies effective communication of quantitative results by linking perception, cognition, and algorithms to exploit the enormous bandwidth of the human visual cortex. In this course you will learn to recognize, design, and use effective visualizations.
Just because you can make a prediction and convince others to act on it doesn’t mean you should. In this course you will explore the ethical considerations around big data and how these considerations are beginning to influence policy and practice. You will learn the foundational limitations of using technology to protect privacy and the codes of conduct emerging to guide the behavior of data scientists. You will also learn the importance of reproducibility in data science and how the commercial cloud can help support reproducible research even for experiments involving massive datasets, complex computational infrastructures, or both.
Learning Goals: After completing this course, you will be able to:
1. Design and critique visualizations
2. Explain the state-of-the-art in privacy, ethics, governance around big data and data science
3. Use cloud computing to analyze large datasets in a reproducible way.
提供方
教学大纲 - 您将从这门课程中学到什么
周
1完成时间为 3 小时
Visualization
Statistical inferences from large, heterogeneous, and noisy datasets are useless if you can't communicate them to your colleagues, your customers, your management and other stakeholders. Learn the fundamental concepts behind information visualization, an increasingly critical field of research and increasingly important skillset for data scientists. This module is taught by Cecilia Aragon, faculty in the Human Centered Design and Engineering Department.
14 个视频 （总计 49 分钟）, 1 个测验
14 个视频
02 Introduction: Motivating Examples3分钟
03 Data Types: Definitions3分钟
04 Mapping Data Types to Visual Attributes3分钟
05 Data Types Exercise2分钟
06 Data Types and Visual Mappings Exercises4分钟
07 Data Dimensions3分钟
08 Effective Visual Encoding3分钟
09 Effective Visual Encoding Exercise2分钟
10 Design Criteria for Visual Encoding2分钟
11 The Eye is not a Camera4分钟
12 Preattentive Processing4分钟
13 Estimating Magnitude3分钟
14 Evaluating Visualizations3分钟
周
2完成时间为 1 小时
Privacy and Ethics
Big Data has become closely linked to issues of privacy and ethics: As the limits on what we *can* do with data continue to evaporate, the question of what we *should* do with data becomes paramount. Motivated in the context of case studies, you will learn the core principles of codes of conduct for data science and statistical analysis. You will learn the limits of current theory on protecting privacy while still permitting useful statistical analysis.
14 个视频 （总计 85 分钟）
14 个视频
Barrow Study Problems4分钟
Reifying Ethics: Codes of Conduct6分钟
ASA Code of Conduct: Responsibilities to Stakeholders4分钟
Other Codes of Conduct6分钟
Examples of Codified Rules: HIPAA3分钟
Privacy Guarantees: First Attempts6分钟
Examples of Privacy Leaks6分钟
Formalizing the Privacy Problem7分钟
Differential Privacy Defined9分钟
Global Sensitivity5分钟
Laplacian Noise4分钟
Adding Laplacian Noise and Proving Differential Privacy5分钟
Weaknesses of Differential Privacy7分钟
周
3完成时间为 4 小时
Reproducibility and Cloud Computing
Science is facing a credibility crisis due to unreliable reproducibility, and as research becomes increasingly computational, the problem seems to be paradoxically getting worse. But reproducibility is not just for academics: Data scientists who cannot share, explain, and defend their methods for others to build on are dangerous. In this module, you will explore the importance of reproducible research and how cloud computing is offering new mechanisms for sharing code, data, environments, and even costs that are critical for practical reproducibility.
17 个视频 （总计 71 分钟）, 2 个测验
17 个视频
Reproducibility Gold Standard5分钟
Anecdote: The Ocean Appliance4分钟
Code + Data + Environment3分钟
Cloud Computing Introduction2分钟
Cloud Computing History5分钟
Code + Data + Environment + Platform3分钟
Cloud Computing for Reproducible Research3分钟
Advantages of Virtualization for Reproducibility5分钟
Complex Virtualization Scenarios3分钟
Shared Laboratories3分钟
Economies of Scale4分钟
Provisioning for Peak Load2分钟
Elasticity and Price Reductions5分钟
Server Costs vs. Power Costs2分钟
Reproducibility for Big Data5分钟
Counter-Arguments and Summary4分钟
1 个练习
AWS Credit Opt-in Consent Form2分钟
讲师
Bill Howe
Director of ResearchScalable Data Analytics
关于 华盛顿大学
Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world.
关于 大规模数据科学 专项课程
Learn scalable data management, evaluate big data technologies, and design effective visualizations.
This Specialization covers intermediate topics in data science. You will gain hands-on experience with scalable SQL and NoSQL data management solutions, data mining algorithms, and practical statistical and machine learning concepts. You will also learn to visualize data and communicate results, and you’ll explore legal and ethical issues that arise in working with big data. In the final Capstone Project, developed in partnership with the digital internship platform Coursolve, you’ll apply your new skills to a real-world data science project.
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