How to address Polo? Grammatically correct Prof. Chau Dr. Chau - - PowerPoint PPT Presentation

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How to address Polo? Grammatically correct Prof. Chau Dr. Chau - - PowerPoint PPT Presentation

Nov 24, 2022 616 likes •1.37k views

http://poloclub.gatech.edu/cse6242 CSE6242 / CX4242: Data & Visual Analytics Duen Horng (Polo) Chau Assistant Professor Associate Director, MS Analytics Georgia Tech Google Polo Chau (only one in the world) How to

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http://poloclub.gatech.edu/cse6242


CSE6242 / CX4242: 


Data & Visual Analytics


Duen Horng (Polo) Chau


Assistant Professor
 Associate Director, MS Analytics
 Georgia Tech

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Google “Polo Chau” (only one in the world)

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How to address Polo?

Grammatically correct

  • Prof. Chau
  • Dr. Chau

Grammatically incorrect, but popular

  • Prof. Polo
  • Dr. Polo
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Course Registration

  • As of 3pm today (Jan 9, 2018)
  • CSE 6242 A
  • 217/220 seats filled
  • 2/65 waitlist slots taken
  • CX 4242 A
  • 78/80 seats filled
  • 0/50 waitlist slots taken
  • CSE 6242 Q (distance-learning): 9 students

This class room seats 300. Almost all physical seats have been filled. If you are on the waitlist, please wait for seats to released (some students typically “drop” after today).

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Course TAs Be very very nice to them!

Office hours and locations (TBD) on course homepage


poloclub.gatech.edu/cse6242

Neetha Ravishankar Jennifer Ma Mansi Mathur Arathi Arivayutham Vineet Vinayak Pasupulety Siddharth Gulati

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Acar

@Symantec

Robert Brian Chad


@Southwestern Univ

Shang Srishti

@Apple

Florian


@Facebook

Shan


@Oracle

Aakash


@Google

Samuel


CMU Masters

Jerry


Stanford PhD

Paras


Berkeley PhD

Victor

@Facebook

Peter


UCLA PhD

Meera


@Microsoft

Fred Andy Nilaksh Madhuri Matthew Bob

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poloclub.gatech.edu

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poloclub.gatech.edu

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We work with (really) large data.

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Internet

50 Billion Web Pages

www.worldwidewebsize.com www.opte.org
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Facebook

2 Billion Users

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Citation Network

www.scirus.com/press/html/feb_2006.html#2 Modified from well-formed.eigenfactor.org

250 Million Articles

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Twitter

Who-follows-whom (500 million users) Who-buys-what (120 million users)

cellphone network

Who-calls-whom (100 million users)

Protein-protein interactions

200 million possible interactions in human genome

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Many More

Sources: www.selectscience.net www.phonedog.com www.mediabistro.com www.practicalecommerce.com/
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“Big Data” Analyzed

DATA INSIGH

Graph Nodes Edges

YahooWeb 1.4 Billion 6 Billion Symantec Machine-File Graph 1 Billion 37 Billion Twitter 104 Million 3.7 Billion Phone call network 30 Million 260 Million

We also work with small data. 
 Small data also needs love.

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7

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7

Number of items an average human holds in working memory

±2

George Miller, 1956

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7

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Data Insights

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How to do that?

COMPUTATION + HUMAN INTUITION

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Both develop methods for making sense of network data

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How to do that?

COMPUTATION INTERACTIVE VIS

Automatic User-driven; iterative Summarization, 
 clustering, classification Interaction, visualization >Millions of nodes Thousands of nodes

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How to do that?

COMPUTATION INTERACTIVE VIS

Automatic User-driven; iterative Summarization, 
 clustering, classification Interaction, visualization >Millions of nodes Thousands of nodes

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How to do that?

COMPUTATION INTERACTIVE VIS

Automatic User-driven; iterative Summarization, 
 clustering, classification Interaction, visualization >Millions of nodes Thousands of nodes

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How to do that?

COMPUTATION INTERACTIVE VIS

Automatic User-driven; iterative Summarization, 
 clustering, classification Interaction, visualization >Millions of nodes Thousands of nodes

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How to do that?

COMPUTATION INTERACTIVE VIS

Automatic User-driven; iterative Summarization, 
 clustering, classification Interaction, visualization >Millions of nodes Thousands of nodes

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How to do that?

COMPUTATION INTERACTIVE VIS

Automatic User-driven; iterative Summarization, 
 clustering, classification Interaction, visualization >Millions of nodes Thousands of nodes

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Our research combines the 
 Best of Both Worlds

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Our Approach for Big Data Analytics

DATA MINING HCI

Automatic User-driven; iterative Summarization, 
 clustering, classification Interaction, visualization >Millions of items Thousands of items

Human-Computer Interaction

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Our mission & vision:

Scalable, interactive, usable
 tools for big data analytics

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“Computers are incredibly fast, accurate, and stupid. Human beings are incredibly slow, inaccurate, and brilliant. Together they are powerful beyond imagination.”

(Einstein might or might not have said this.)

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SLIDE 30 22 Apolo: Making Sense of Large Network Data by Combining Rich User Interaction and Machine
  • Learning. CHI 2011.

Machine Learning + Visualization

http://www.scs.gatech.edu/news/522401/12m-nsf-award-helps-consumers-enter-age-big-data

Recently received $1.2 Million NSF award

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SLIDE 31 23 Carina: Interactive Million-Node Graph Visualization using Web Browser Technologies. 
 Dezhi (Andy) Fang, Mahew Keezer, Jacob Williams, Kshitij Kulkarni, Robert Pienta, Duen Horng (Polo) Chau. 
 WWW’17 Poster

Carina: Million-node Graph Exploration in Web Browser [www’17]

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Find co-directors who made at least two films together, starring the same actor.

VISAGE: Interactive Visual Graph Querying

VISAGE: Interactive Visual Graph Querying. 
 Robert Pienta, Acar Tamersoy, Sham Navathe, Hanghang Tong, Alex Endert, Duen Horng Chau. 
 International Working Conference on Advanced Visual Interfaces (AVI 2016).

SIGMOD’17 Best Demo, honorable mention

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ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models. 
 Minsuk Kahng, Pierre Andrews, Aditya Kalro, Duen Horng (Polo) Chau. 
 IEEE Transactions on Visualization and Computer Graphics (Proc. VAST'17), Jan 2018.

Visualization & Interpretation of Deep Learning Models

ActiVis

Deployed on ML platform of

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Polo’s primary application area:


Cyber Security

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Patented with Symantec Finds malware from 37 billion file relationships Serving 120 million users worldwide Published at SDM’11, KDD’14

Polonium & AESOP

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Text

NetProbe


Auction Fraud Detection on eBay

$$$

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 Best papers of SDM 2014 


(top data mining conference)

MARCO


Detecting Fake Yelp Reviews

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Insider Trading Detection


with Securities and Exchange Commission (SEC)

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Course homepage
 All assignments, slides posted here poloclub.gatech.edu/cse6242/ Discussion, Q&A, 
 find teammates

Piazza: goo.gl/cGvHeE

  • r piazza.com/gatech/spring2018/cse6242aqcx4242a

Assignment 
 Submission T-Square


(Use Piazza for discussion)

Logistics

Make sure you’re at the right Piazza!
 (CSE-6242-O01, CSE-6242-OAN have their Piazza forums too)

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Course Homepage

For syllabus, HWs, projects, datasets, etc.

Google “cse6242”


poloclub.gatech.edu/cse6242/2018spring

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Join Piazza ASAP

goo.gl/cGvHeE

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Important to join Piazza because…

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  • Polo will announce events related to this class and

data science in general

  • Distinguished lectures
  • Seminars
  • Hackathons (free food, prizes)
  • Company recruitment events (free food, swag)

Important to join Piazza because…

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Course Goals

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What is Data & Visual Analytics?

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What is Data & Visual Analytics?

No formal definition!

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Polo’s definition: 
 the interdisciplinary science of combining 
 computation techniques and 
 interactive visualization 
 to transform and model data to aid 
 discovery, decision making, etc.

What is Data & Visual Analytics?

No formal definition!

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What are the “ingredients”?

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What are the “ingredients”?

Need to worry (a lot) about: storage, complex system design, scalability of algorithms, visualization techniques, interaction techniques, statistical tests, etc. Wasn’t this complex before this big data era. Why?

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http://spanning.com/blog/choosing-between-storage-based-and-unlimited-storage-for-cloud-data-backup/

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What is big data? Why care?

Many businesses are based on big data.

Search engines: rank webpages, predict what you’re going to type Advertisement: infer what you like, based on what your friends like; show relevant ads E-commerce: recommends movies/products (e.g., Netflix, Amazon) Health IT: patient records (EMR) Finance

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Good news! Many jobs!

Most companies are looking for “data scientists” The data scientist role is critical for organizations looking to extract insight from information assets for ‘big data’ initiatives and requires a broad combination of skills that may be fulfilled better as a team


  • Gartner (http://www.gartner.com/it-glossary/data-scientist)

Breadth of knowledge is important.
 This course helps you learn some important skills.

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Collection Cleaning Integration Visualization Analysis Presentation Dissemination

Course Schedule


(Analytics Building Blocks)

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Building blocks. Not Rigid “Steps”

Can skip some Can go back (two-way street)

  • Data types inform visualization design
  • Data size informs choice of algorithms
  • Visualization motivates more data cleaning
  • Visualization challenges algorithm

assumptions
 e.g., user finds that results don’t make sense

Collection Cleaning Integration Visualization Analysis Presentation Dissemination

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  • Learn visual and computation techniques

and use them in complementary ways

  • Gain a breath of knowledge
  • Learn practical know-how by working on 


real data & problems

Course Goals

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  • [50%] 4 homework assignments
  • End-to-end analysis
  • Techniques (computation and vis)
  • “Big data” tools, e.g., Hadoop, Spark, etc.
  • [50%] Group project -- 4 to 6 people
  • [Bonus points] In-class pop quizzes
  • Each quiz is worth 1% course grade
  • No exams

Grading

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Policies

Collaborating on homework
 Late submission policy

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Working on Homework

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WARNING

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You’ll be writing a lot of code

Q: Is it OK to copy and use code found on the web?
 A: No 
 Q: Why?
 A: Here’s why…

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WARNING: Do not plagiarize!

  • Using code as reference does not mean copying and

modifying it.

  • To use example code as reference, you should go over it,

understand what it is doing, and then try to accomplish what it is trying to do using your own code. And it’s a good practice to cite the the sources (e.g., as part of your code comments).

  • The analogy is like how you would write an essay or a
  • speech. You can get inspirations from others, but you should

use your own words, otherwise it will be considered

  • plagiarism. Plagiarism can lead to severe consequences.
  • http://www.plagiarism.org/plagiarism-101/what-is-plagiarism/
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Are You Ready to Take this Course?

  • Require a lot of programming
  • You need to learn many new things in

short amount of time

  • HW2 (D3 data vis) is most demanding:

Javascript + CSS + HTML

  • Very common in industry
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The best way to find out is to check out previous semester’s homework assignments

  • poloclub.gatech.edu/cse6242/2017fall/
  • poloclub.gatech.edu/cse6242/2017spring/
  • poloclub.gatech.edu/cse6242/2016fall/
  • poloclub.gatech.edu/cse6242/2016spring/

Are You Ready to Take this Course?

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e.g., http://poloclub.gatech.edu/cse6242/2017fall/

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From Previous Classes…

  • Class projects turned into papers at top

conferences (KDD, IUI, etc.)

  • Projects as portfolio pieces on CV
  • Increased job and internship opportunities
  • Former students sent me “thank you” notes
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IUI Full conference paper

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KDD Workshop paper

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IUI Poster paper

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“I feel like the concepts from your class are like a rite of passage for an aspiring data scientist. Assignments lead to a feelings of accomplishment and truly progressing in my area of passion.” “I really get more intuition about how to deal with data with some powerful tools in HW3 [uses AWS]. That feeling is beyond description for me.” “I would like to say thank you for your class! Thanks to the skills I got from the class and the project, I got the offer.”

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What Polo expects from you

  • Actively participate throughout the course!
  • Ask questions during class and on Piazza
  • Help out whenever you can, e.g., help

answer questions on Piazza

  • Polo reserves last few minutes of every

class for Q&A

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FREE After-class Coffee ☕

  • After class, Polo randomly selects 5 students

(+2 volunteers) for FREE after-class coffee

  • Polo’s treat. You can order coffee, tea,

pastries — whatever you want

  • Very casual — you can ask me ANYTHING
  • Will try doing this at least once a week,

starting next week!