Acerca de este curso: 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.
Creada por: University of Washington
Enseñado por: Bill Howe, Director of Research
Scalable Data Analytics
| Información básica | Curso 3 de 4 en Data Science at Scale Specialization |
| Idioma | English |
| Cómo aprobar | Aprueba todas las tareas calificadas para completar el curso. |
| Calificaciones del usuario |
Programa
SEMANA 1
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 videos
- Vídeo: 02 Introduction: Motivating Examples
- Vídeo: 03 Data Types: Definitions
- Vídeo: 04 Mapping Data Types to Visual Attributes
- Vídeo: 05 Data Types Exercise
- Vídeo: 06 Data Types and Visual Mappings Exercises
- Vídeo: 07 Data Dimensions
- Vídeo: 08 Effective Visual Encoding
- Vídeo: 09 Effective Visual Encoding Exercise
- Vídeo: 10 Design Criteria for Visual Encoding
- Vídeo: 11 The Eye is not a Camera
- Vídeo: 12 Preattentive Processing
- Vídeo: 13 Estimating Magnitude
- Vídeo: 14 Evaluating Visualizations
Calificado: Crime Analytics: Visualization of Incident Reports
SEMANA 2
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 videos
- Vídeo: Barrow Study Problems
- Vídeo: Reifying Ethics: Codes of Conduct
- Vídeo: ASA Code of Conduct: Responsibilities to Stakeholders
- Vídeo: Other Codes of Conduct
- Vídeo: Examples of Codified Rules: HIPAA
- Vídeo: Privacy Guarantees: First Attempts
- Vídeo: Examples of Privacy Leaks
- Vídeo: Formalizing the Privacy Problem
- Vídeo: Differential Privacy Defined
- Vídeo: Global Sensitivity
- Vídeo: Laplacian Noise
- Vídeo: Adding Laplacian Noise and Proving Differential Privacy
- Vídeo: Weaknesses of Differential Privacy
SEMANA 3
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 videos, 1 cuestionario de práctica
- Vídeo: Reproducibility Gold Standard
- Vídeo: Anecdote: The Ocean Appliance
- Vídeo: Code + Data + Environment
- Vídeo: Cloud Computing Introduction
- Vídeo: Cloud Computing History
- Vídeo: Code + Data + Environment + Platform
- Vídeo: Cloud Computing for Reproducible Research
- Vídeo: Advantages of Virtualization for Reproducibility
- Vídeo: Complex Virtualization Scenarios
- Vídeo: Shared Laboratories
- Vídeo: Economies of Scale
- Vídeo: Provisioning for Peak Load
- Vídeo: Elasticity and Price Reductions
- Vídeo: Server Costs vs. Power Costs
- Vídeo: Reproducibility for Big Data
- Vídeo: Counter-Arguments and Summary
- Cuestionario de práctica: AWS Credit Opt-in Consent Form
Calificado: Graph Analysis in the Cloud
Preguntas Frecuentes
Cómo funciona
Trabajo del curso
Cada curso es como un libro de texto interactivo, con videos pregrabados, cuestionarios y proyectos.
Ayuda de tus compañeros
Conéctate con miles de estudiantes y debate ideas y materiales del curso, y obtén ayuda para dominar los conceptos.
Certificados
Obtén reconocimiento oficial por tu trabajo y comparte tu éxito con amigos, compañeros y empleadores.
Creadores
University of Washington
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.
Tarifa
| Comprar curso | |
|---|---|
| Accede a los materiales del curso | Disponible |
| Accede a los materiales con calificación | Disponible |
| Recibe una calificación final | Disponible |
| Obtén un Certificado de curso para compartir | Disponible |
Calificaciones y revisiones
very good course for learner
The information from the last assignment is split into Forums and Tasks description. This is very easy to fix and not doing it shows passivity from the organizers
Nice course into data science
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I wish there is a coherent explanation of procedure to do graph analysis on AWS. The required details are provided in bits and pieces in the discussion forum and in github. I had to waste a lot of my time figuring this out. If you are new to this be ready to spend a lot of time or better take some other course where all explanations will be provided. But if you have some experience then this course is great.