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

( 33 Reviews )

Created by: Bill Howe

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Overall Score : 60 / 100

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.

Time: 11h

Instructor Details

Bill Howe

3.0 Rating
33 Reviews

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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By Claudio G on 26-Apr-16

Much of the assignment was out of date. The content was not related to big portion of the assignment. There was no way of getting clarification over the outdated assignment content.

By Ã©ÂƒÂ‘Ã©Â”ÂÃ¥Â½Â¬ on 21-Jun-16

I'll say the same about this class as the rest of the specialization, if you have the skills to complete this course then you don't need to take this course. If you don't have the skills to complete this course, you will not complete this course. The course instruction is at 10000 feet level and the assignments are very challenging and the course will NOT teach you the skills required to complete the assignments. The AWS final assignment is a very much throw you into the deep end with no real instruction (well at least completely outdated instructions) and will expect you to swim (or more likely for most people, to drown). I recommend the Machine Learning Course (from Bill's colleagues) at University of Washington. That is a course where you get some real instruction and understanding of how to complete assignments (though still very challenging).

By Trevor L on 13-Mar-17

The big assignment at the end contains instructions that are outdated and incomplete. Given the length of the course, it feels like you already need to know the material before even taking it.

By Mayank P on 21-Jul-16

This course is really bad. The instruction is not enough to solve the programming assignment. The almost contents aren't related to communicating data science result.

By Bishnu P on 17-Sep-16

I was very upset by the end of this course. The documentation was terrible. I ended up spending way to much on AWS even though I managed it the best I could Terminating instances. In general, I think this course would have been more fulfilling if the documentation was appropriate.

By Hariharan L on 27-May-16

This course is not maintained. It's flat out exploitative to throw students at an AWS assignment without updated instructions and with outdated versions of pig scripts, etc. They're setting students up to hemorrhage money on AWS and possibly not get anything out of it. Under no circumstances should you take this course or even this specialization so long as this assignment is gating it.

By Matt C on 20-Feb-16

The lectures are excellent, but do not take this course if you are not already proficient in a graphing package, whether it's R, python, or something else much more sophisticated than Excel. Otherwise you will be faced with the painfully frustrating task of learning a package while trying to complete an assignment, all with a short deadline.

By Deepanshu P on 22-Mar-17

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

By Serkan A Y on 7-Jan-16

Nice course

By Dwight S on 20-Apr-16

Nice lectures with lot of good information. AWC setup instruction need to update according new AWC interface.

By Paul L on 18-Nov-16

The peer-review assignment is not properly designed. From my own experience, colleagues tend to underestimate other people's projects. In addition, the peer-review had an extra/optional advanced component (analysing criminal patterns for a second city; comparing patterns across two cities), which I carried out but got no extra credit for. The extra work was not even part of the assignment classification -- there should be a bonus question for students who carry out the advanced part of the assignment!

By Shuang F on 1-Apr-16