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CourseraData ScienceFree courseCertificate

Communicating Data Science Results

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

3.0
33 reviews

Created by: Bill Howe

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Overall Score : 60 / 100

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Course Description

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 visualizations2. Explain the state-of-the-art in privacy, ethics, governance around big data and data science3. Use cloud computing to analyze large datasets in a reproducible way.
Read More product length iconTime: 11h

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Instructor Details

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Bill Howe is the Director of Research for Scalable Data Analytics at the UW eScience Institute and holds an Affiliate Assistant Professor appointment in Computer Science & Engineering, where he leads a group studying data management, analytics, and visualization systems for science applications. Howe has received awards from Microsoft Research and honors for papers in scientific data management, and serves on a number of program committees, organizing committees, and advisory boards in the area, including the advisory board of the Data Science certificate program at UW. He holds a Ph.D. in Computer Science from Portland State University and a Bachelor's degree in Industrial & Systems Engineering from Georgia Tech.

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3.0

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By P.Parasuraman on 7-Aug-19

Too little people participated and long peer review time.But the course content is good.

By Anoubhav A on 8-Jun-19

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 spend 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.

By Jose A R N on 13-Nov-18

The material was very general and I think a little bit superficial especially the first week concerning visualisation. There was very little connection between the videos and the actual required skills for the assignments and although I like learning by doing a little bit of guidance would have been nice so that you know that you are doing things in the best or most appropriate way.

By vvs m k on 3-Nov-18

The assignments for this course are outdated and not well supported.

By chandra m m on 7-Jan-18

Really disappointed by his way of teaching. He assumes we know every thing before hand, database, server etc. He just has basic concepts in his lecture classes while intermediate level implementations of it in different languages. He just instructs check out this tutorial online and do this assignment. If you are already familiar with all the languages and software platforms that he is using than you can go ahead with the course or you will end up like me where you will have to take up different courses to just complete assignments of this one.

By Katarzyna S on 7-Dec-17

Guest lecture is interesting, other lectures are of quite low quality

By Nivedita P on 8-Jul-17

Not really the same quality as the first two courses in this specialisation. The lectures videos are somewhat disconnected from the assignments.

By Ashwani B on 27-Jun-17

very good course for learner

By Subhash P on 23-Jun-17

yikes update the github resources please

By Muhammad A A on 18-Jun-17

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 Dariush on 13-Jun-17

I took it when the specialization was just a single, 12 week course. The assignments are barely updated and you have to rely on instructions found in the forum. It has audio quality issues as well. Otherwise, the content it top notch.

By Deepanshu P on 22-Mar-17

The course does not has lecture slides that is better for students to understand.

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Communicating Data Science Results
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  • Video Duration: icon 11h
  • Price: Free
  • Provider: icon Coursera
  • University: icon University of Washington
  • Language: icon English
  • Certificate: icon Yes

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