About this Course
3.6
124 ratings
35 reviews
Specialization
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100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Hours to complete

Approx. 11 hours to complete

Suggested: 4 hours/week...
Available languages

English

Subtitles: English
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Hours to complete

Approx. 11 hours to complete

Suggested: 4 hours/week...
Available languages

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
Hours to complete
3 hours to complete

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....
Reading
14 videos (Total 49 min), 1 quiz
Video14 videos
02 Introduction: Motivating Examples3m
03 Data Types: Definitions3m
04 Mapping Data Types to Visual Attributes3m
05 Data Types Exercise2m
06 Data Types and Visual Mappings Exercises4m
07 Data Dimensions3m
08 Effective Visual Encoding3m
09 Effective Visual Encoding Exercise2m
10 Design Criteria for Visual Encoding2m
11 The Eye is not a Camera4m
12 Preattentive Processing4m
13 Estimating Magnitude3m
14 Evaluating Visualizations3m
Week
2
Hours to complete
1 hour to complete

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. ...
Reading
14 videos (Total 85 min)
Video14 videos
Barrow Study Problems4m
Reifying Ethics: Codes of Conduct6m
ASA Code of Conduct: Responsibilities to Stakeholders4m
Other Codes of Conduct6m
Examples of Codified Rules: HIPAA3m
Privacy Guarantees: First Attempts6m
Examples of Privacy Leaks6m
Formalizing the Privacy Problem7m
Differential Privacy Defined9m
Global Sensitivity5m
Laplacian Noise4m
Adding Laplacian Noise and Proving Differential Privacy5m
Weaknesses of Differential Privacy7m
Week
3
Hours to complete
4 hours to complete

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....
Reading
17 videos (Total 71 min), 2 quizzes
Video17 videos
Reproducibility Gold Standard5m
Anecdote: The Ocean Appliance4m
Code + Data + Environment3m
Cloud Computing Introduction2m
Cloud Computing History5m
Code + Data + Environment + Platform3m
Cloud Computing for Reproducible Research3m
Advantages of Virtualization for Reproducibility5m
Complex Virtualization Scenarios3m
Shared Laboratories3m
Economies of Scale4m
Provisioning for Peak Load2m
Elasticity and Price Reductions5m
Server Costs vs. Power Costs2m
Reproducibility for Big Data5m
Counter-Arguments and Summary4m
Quiz1 practice exercise
AWS Credit Opt-in Consent Form2m

Instructor

Avatar

Bill Howe

Director of Research
Scalable Data Analytics

About 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....

About the Data Science at Scale Specialization

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....
Data Science at Scale

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.