This course is part of the Data Science at Scale Specialization

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Bill Howe

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Data Science at Scale SpecializationUniversity of Washington

About this Course

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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.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 3 of 4 in the

Data Science at Scale Specialization

Approx. 7 hours to complete

English

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Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 3 of 4 in the

Data Science at Scale Specialization

Approx. 7 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Bill Howe

Director of Research

Scalable Data Analytics

84,936 Learners

4 Courses

Offered by

University of Washington

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Syllabus - What you will learn from this course

Week1

Week 1

2 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.

2 hours to complete

14 videos (Total 49 min)

14 videos

01 Introduction: What and Why3m02 Introduction: Motivating Examples3m03 Data Types: Definitions3m04 Mapping Data Types to Visual Attributes3m05 Data Types Exercise2m06 Data Types and Visual Mappings Exercises4m07 Data Dimensions3m08 Effective Visual Encoding3m09 Effective Visual Encoding Exercise2m10 Design Criteria for Visual Encoding2m11 The Eye is not a Camera4m12 Preattentive Processing4m13 Estimating Magnitude3m14 Evaluating Visualizations3m

Week2

Week 2

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.

1 hour to complete

14 videos (Total 85 min)

14 videos

Motivation: Barrow Alcohol Study5mBarrow Study Problems4mReifying Ethics: Codes of Conduct6mASA Code of Conduct: Responsibilities to Stakeholders4mOther Codes of Conduct6mExamples of Codified Rules: HIPAA3mPrivacy Guarantees: First Attempts6mExamples of Privacy Leaks6mFormalizing the Privacy Problem7mDifferential Privacy Defined9mGlobal Sensitivity5mLaplacian Noise4mAdding Laplacian Noise and Proving Differential Privacy5mWeaknesses of Differential Privacy7m

Week3

Week 3

5 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.

5 hours to complete

17 videos (Total 71 min)

17 videos

Reproducibility and Data Science5mReproducibility Gold Standard5mAnecdote: The Ocean Appliance4mCode + Data + Environment3mCloud Computing Introduction2mCloud Computing History5mCode + Data + Environment + Platform3mCloud Computing for Reproducible Research3mAdvantages of Virtualization for Reproducibility5mComplex Virtualization Scenarios3mShared Laboratories3mEconomies of Scale4mProvisioning for Peak Load2mElasticity and Price Reductions5mServer Costs vs. Power Costs2mReproducibility for Big Data5mCounter-Arguments and Summary4m

1 practice exercise

AWS Credit Opt-in Consent Form30m

Reviews

3.6

38 reviews

5 stars
35.55%
4 stars
23.70%
3 stars
17.03%
2 stars
8.14%
1 star
15.55%

TOP REVIEWS FROM COMMUNICATING DATA SCIENCE RESULTS

by BLAug 6, 2019

Too little people participated and long peer review time.

But the course content is good.

by APJun 17, 2017

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

by FQNov 11, 2016

Great and useful first week about visualization, although I wish it would cover more material . The ethics and cloud computing felt somewhat incomplete, but useful as well.

View all reviews

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.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Communicating Data Science Results

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