University of Washington

Communicating Data Science Results

This course is part of Data Science at Scale Specialization

Taught in English

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Bill Howe

Instructor: Bill Howe

16,553 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

3.4

(142 reviews)

7 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

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Assessments

1 quiz

Course

Gain insight into a topic and learn the fundamentals

3.4

(142 reviews)

7 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your subject-matter expertise

This course is part of the Data Science at Scale Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
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There are 3 modules in this course

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.

What's included

14 videos1 peer review

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.

What's included

14 videos

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.

What's included

17 videos1 quiz1 programming assignment

Instructor

Bill Howe
University of Washington
4 Courses87,426 learners

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Recommended if you're interested in Data Analysis

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Learner reviews

Showing 3 of 142

3.4

142 reviews

  • 5 stars

    33.80%

  • 4 stars

    22.53%

  • 3 stars

    16.90%

  • 2 stars

    7.74%

  • 1 star

    19.01%

AP
4

Reviewed on Jun 17, 2017

FQ
4

Reviewed on Nov 11, 2016

BL
5

Reviewed on Aug 6, 2019

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