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

Feb 11, 2023 117 likes •687 views

http://poloclub.gatech.edu/cse6242 CSE6242 / CX4242: Data & Visual Analytics Duen Horng (Polo) Chau Associate Professor, College of Computing Associate Director, MS Analytics Machine Learning Area Leader, College of

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http://poloclub.gatech.edu/cse6242


CSE6242 / CX4242: 


Data & Visual Analytics


Duen Horng (Polo) Chau


Associate Professor, College of Computing
 Associate Director, MS Analytics
 Machine Learning Area Leader, College of Computing
 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
  • CSE 6242 A
  • 186/202 seats filled
  • 81/250 waitlist slots taken
  • CX 4242 A
  • 50/68 seats filled
  • 4/100 waitlist slots taken
  • CSE 6242 Q (distance-learning): 6 students

This class room seats 300. 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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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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Or, to ride the AI wave…

ARTIFICIAL INTELLIGENCE + HUMAN INTELLIGENCE

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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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AI Interpretation & Protection Cyber Security Social Good & Health Large Graph Mining & Visualization

Polo Club of Data Science poloclub.github.io

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

Piazza: link available on canvas.gatech.edu

Assignment 
 Submission Canvas


(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/

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Join Piazza ASAP
 (via canvas.gatech.edu)

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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 breadth 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


On website; we go through them now


 Grading, plagiarism, collaboration, late submission, and the “warning” about the difficulty this course

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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!