Andreas Weigend | Social Data Revolution | Fall 2014
School of Information | University of California at Berkeley | INFO 290A-03

Social Data Revolution

INFO 290A-03, CCN 41647
Fall 2014 (September 30 - November 25)
202 South Hall, School of Information, UC Berkeley
ischool.berkeley.edu/courses/i290a-sdr

This course is about the use, the importance, and the future of data. It is taught by Andreas Weigend , former chief scientist at Amazon.

Teaser

There is a 5-minute video about what this course is about. Let me know what you think!

Course Description

Leading companies in the digital network economy, such as Amazon, Facebook, and Google, are using big data to revolutionize the way individuals interface with the world. This course examines the impact of our digital footprints, what they can be used for, and how they reveal surprising details about human behavior.
The course starts with an overview of the principles of ubiquitous data. We’ll then discuss how data science is revolutionizing commerce, education, health, wealth, and work. Students will understand the opportunities and risks of the irreversible shift in business and human interaction created by the social data revolution. Some industry leaders will join us, similar to past years (2013, 2012, 2011, 2010, 2009, 2008)
Students taking INFO-290A are invited to participate in the Social Data Lab’s activities and events.

Class Dates

In Fall 2014, we can get into the classroom (202 South Hall) at 3:30PM. We start sharp at 3:40pm. There will be a 10 minute break (or in some classes a 30 minute breakout session instead). Class ends at 6:30pm.

Session
Topic
30 Sep
Social Data
In this class, we will examine the birth of the Social Data Revolution in the cradle of e-commerce and online advertising, and consider its present and future. We also look at sensors and new ways of collecting data.
Greg Tanaka (Bay Sensors)
07 Oct
Social Graph
By studying human behavior on digital networks like LinkedIn, we acquire insights and techniques to understand and to influence people.
Simon Zheng (LinkedIn)
07 Oct and
21 Oct
Finance
How can we apply new techniques and ideas to these existing stockpiles of data to help people make better decisions, whether in combating fraud or determining retail trends? We look into unleashing the hidden potential of existing information silos by transforming them into data warehouses.
Max Levchin (Affirm) [07 Oct]
Gary Kearns (MasterCard Advisors) [21 Oct]
28 Oct
(Part 1)
Work
The Internet is creating new opportunities and redefining work in the post-industrial economy.At the same time, availability of new forms of data is transforming how workers are evaluated and how they evaluate their employers. We consider the balance of power between the two sides and how that is shifting in ways that are enabled by data.
28 Oct
(Part 2)
Sociometrics
Modern companies are learning that creating a work environment in which collaboration between individuals can happen is a key component of innovation and success. As they invest in designing better work places to foster creativity and collaboration, how can these investments be measured? How do we quantify social interactions? That's the question that sociometrics seek to answer.
28 Oct (Part 3)
Learning
When the goal of making all course materials freely available online has finally been realized, what comes after? With all the personalized data we have, how can we make education more effective? Is it possible to objectively measure the potential of a student, and if it were possible, should we do it?

04 Nov
Fitness
Our well-being as humans is being augmented by data. As wearable sensors provide a deeper look into our daily routines, will understanding data better help us live healthier lives? How do we tell the difference between data that are easy to collect and data that actually help us understand the state of our health better?
18 Nov
Data Ownership and the Future of Data
At the heart of the future direction of the Social Data Revolution lies the critical concept of data ownership. In what sense can we exercise meaningful control over what we produce, both intentionally and incidentally, online? In asking these questions, we look towards the future of data.
Before each class, I am holding office hours from 2-3pm in South Hall, Room 302.
After each class, we will go out for dinner with ~6-8 students (and guest speaker, if there is one).

Syllabus

I will briefly discuss the syllabus in the first class.Do come prepared with any questions you might have!

Grading

There is a total of 100 points.
40 for your wiki contribution (20 for the week you are responsible, 20 total for the other weeks)
60 for the assignments (5 assignments, 10 points for HW1 and HW3-5), and 20 for HW2.

Initial survey

To hit the ground running in the first class, I’ve put up a short survey at bit.ly/ischool2014survey. The goals are to get you to think about the topics beforehand, and to give me a feeling of what is most exiting to you ☺ So, please, take the time and let me know your thoughts about what you want to get out of the class. Also, if you have any ideas or questions, please email me as soon as possible. We'll discuss some options in the first class and nail down the final syllabus down before the second class.

Recordings

Videos are at youtube.com/socialdatarevolution
Audio recordings (mp3) and corresponding transcripts (docx) are at weigend.com/files/teaching/ischool/2014/recordings
And if you want access right after class, eg because you are responsible for the wiki page for that class, check the dropbox folder bit.ly/ischool2014raw

Instructor

I am excited to teach this class! It builds on my experiences as the chief scientist at Amazon. Like Google and Facebook, Amazon at its heart is a data company. I worked with Jeff Bezos on topics ranging from data strategy (how do we get customers to write reviews and add photos when useful?) and customer behavior (what experiments can we run to understand how people make purchasing decisions?), to discussing with each team their "fitness functions" (including, of course, the question of how a recommendation algorithm should be evaluated).

I left Amazon in 2004 and came back to the Bay Area to work with some startups and teach, in the fall this course at Berkeley and in the spring at Stanford (where I also got my PhD in physics). My thesis was actually more in the area of what now is called Data Science: I developed neural networks for time series analysis and prediction. Between my postdoc at Xerox PARC and joining Amazon, I was full time faculty, first an assistant professor in Computer Science and Cognitive Science at CU Boulder, and then an associate professor in Information Systems at NYU's Stern School of Business.

I am very fortunate to be an advisor to some great startups (including RocketFuel, see the panel Sketchy Data with its CEO before their IPO last year), and to work as an independent consultant with innovative companies including Alibaba, Lufthansa, and MasterCard. A few years ago, I founded the Social Data Lab: If you take this course you will learn about some of the exciting problems we are working on. With the lab and two former TAs, Claudia Perlich (NYU, now Dstillery) and Jeremy Carr (Stanford, now Palatir), we developed a three-day master class that we are teaching around the world.

In 2014, every company seems to want to have their Big Data Day: I spoke at ATT's in Dallas, Walmart's in Bentonville, and Tencent’s in China. At top international conferences, such as the World Business Forum in Sydney in May, Big Data has become the topic of highest interest to most delegates. If want to know what I talk about, view the slides of a keynote Transforming Big Data into Decisions at an IBM event this June. And if you are interested how I talk about it, you can listen to the audio recording of my talk Data is the New Oil.

Location

The class meets in 202 South Hall (School of Information)
https://www.google.com/maps/place/South+Hall,+University+of+California,+Berkeley
If you are early, spend some time at Caffe Strada, 2680 Bancroft Way, Berkeley, CA 94704.
It takes about 10 min from the café to the classroom (walk towards the Campanile).

Info Session

Date: April 15, 2014, 3:30-6:30pm

Shaun Tai, our videographer, came to the session, filmed it and and uploaded the recording of the info session to the youtube.com/socialdatarevolution. channel. So if you missed it: no worries --- just watch the video and email me any questions you might have, ok?

And to bring you up to speed, here is the email that went out in April to prospective students. This course is not just for students in the I School. In the past years we always had some great undergrads from EECS and engineering, as well as students from Haas and the Law School who provided great perspectives and also learned quite a bit. And nothing beats showing up to the first class on Tuesday September 30 at 3pm at 205 South Hall (the School of Information), and seeing whether you get excited by the material and/or the other students!

Ever taken a shoe selfie?
You’re not alone.
Billions of people socialize “their” data with friends, companies, and the world. This irreversible cultural shift, the Social Data Revolution, has transformed how we view our friends and ourselves (as well as our shoes). Social data influence our purchasing and lifestyle decisions. Companies and governments now observe our decisions and how we got there. So, your shoe selfie is also a clue for today’s data detectives as they piece together your digital footprint.
This fall, I’ll again teach the course “Social Data Revolution” (INFO 290A-03). We’ll meet on Tuesday afternoons (Sep 30–Nov 25, 3:30-6:30PM).
To help you decide whether you want to take this course, join me for the info session on April 15, 2014. I’ll give an overview of what we’ll do in the fall and answer your questions.
To get you started, here are some links:
If you are interested in taking this course, please attend the info session on Tuesday, 4/15. It starts at 3:30PM in Room 202, South Hall, with an overview lecture, followed by Q&A watch the recording of the info session and email me with any questions or suggestions you might have. Thanks!