This course has concluded. See https://poloclub.github.io/#cse6242 for all past course offerings.

CSE6242 / CX4242, Spring 2016
Data and Visual Analytics

Georgia Tech, College of Computing

4:35 - 5:55pm, Clough 152, Tue & Thu
Prof. Duen Horng (Polo) Chau

This course will introduce you to broad classes of techniques and tools for analyzing and visualizing data at scale. It emphasizes on how to combine computation and visualization to perform effective analysis. We will cover methods from each side, and hybrid ones that combine the best of both worlds. Students will work in small teams to complete a research project exploring novel approaches for interactive data & visual analytics.

Office Hours

Polo Chau	Tue, 3:30-4:00pm (+ 30min after Tue's class at Clough Starbucks)	Klaus 1324
Gopi Krishnan Nambiar	Mon, 9-10 AM	common area between Klaus 3201 and 3217 near the stairwell
Nilaksh Das	Tue, 2-3 PM	CCB common area (1st floor)
Pradeep Vairamani Rajendran	Wed, 2-3PM	common area between Klaus 3201 and 3217 near the stairwell
Ajitesh Jain	Mon, 12.30 - 1.30pm	outside Klaus 1324
Vishakha Singh	Wed, 11.30-12.30 PM	CCB common area (1st floor)

Schedule & Lectures

Date		Topic	Tue	Thu	Events
Jan	12, 14	* Course introduction * Big data analytics building blocks	intro	building blocks
	19, 21	* Data Collection, and simple storage (SQLite) * Data cleaning	collection, cleaning	canceled	HW1 out
	26, 28	* Data integration * Project showcase: Wenwen Chang on Predicting Fire Risks in Atlanta * Project showcase: PASSAGE: A Travel Safety Assistant	integration	Fire Project slides, Passage Project slides
Feb	2, 4	* Data integration; similarity functions * Data Mining Concepts & Tasks * Heilmeier Questions * Visualization 101	concepts, heilmeier	vis101	HW1 due (Fri, 11:55pm)
	9, 11	* Data visualization for the web (D3) * Digital Advertising and Analytics by Dr. Sam Franklin, VP of data science at 360i	d3		Form project teams by Friday; HW2 out
	16, 18	* Fixing common visualization issues * Intro to classification: cross validation, k nearest neighbor	fix-vis	classification-intro, graph-basics
	23, 25	* Graph analytics how to build and store graphs basics; power laws; centrality (personalized) PageRank interactive applications evaluating apps	graph basics, centrality	graph algorithms, applications	HW2 due (Fri, 11:55pm)
Mar	1, 3	* Scaling up: Hadoop, Pig, Hive * Scaling up: Spark, Spark SQL * PANDAS	hadoop	pig, hive, spark, pandas	HW3 out
	8, 10	Project proposal presentations	Show time!	Show time!	Project proposal & slides due (Mon, 11:55pm)
	15, 17	* Analytics in practice II: Trey Grainger, CareerBuilder * Scaling up: SPARK stack * Scaling up: HBase		hbase
	22, 24	Spring Break	X	X
	29, 31	* Decision tree * Ensemble method, bagging, random forests * Classification (visualization & interaction)	tree, bagging, random forests, vis	Canceled	HW3 due (Fri, 11:55pm)
Apr	5, 7	* Clustering * Text analytics: concepts * Text analytics: algorithms (LSI=SVD)	clustering	text analytics	HW4 out Project progress report due (Fri, 11:55pm EST)
	12, 14	* Time series: algorithms, visualization, & applications	time series concepts, algorithms	time series algorithms, vis
	19, 21	Dimension reduction (PCA, MDS, LDA, IsoMap)	dimension reduction		HW4 due (Fri, 11:55pm)
	26	Project poster presentations	Poster presentation. 4:30pm to 6pm-ish. Klaus Atrium. Pizza + drinks served!	X	Proj final report due (Tue, 11:55pm EST)

Announcements and Discussion on Piazza

We use Piazza for discussion and all announcements.

Post your questions there. Our teaching staff and your fellow classmates will help answer them quickly. You can also use Pizza to find project teammates.

T-square will only be used for submission of assignments and projects.

While we welcome everyone to share their experiences in tackling issues and helping each other out, but please do not post your answers, as that may affect the learning experience of your fellow classmates.

Homework (50% of grade)

The fastest way to get help with homework assignments is to post your questions on Piazza. If you prefer that your question addresses to only our TAs and the instructor, you can use the private post feature (i.e., check the "Individual Students(s) / Instructors(s)" radio box).

While collaboration is allowed for homework assignments, each student must write up their own answers. All GT students must observe the honor code. Any suspected plagiarism and academic misconduct will be reported and directly handled by the Office of Student Integrity (OSI).

We plan to have 4 assignments in total.

[10%] HW1: Collecting & visualizing data, SQLite, D3 warmup, OpenRefine
[15%] HW2: D3 Graphs and Visualization
[15%] HW3: Hadoop, Spark, Pig and Pandas
[10%] HW4: Random Forest and Weka

Project (50% of grade)

See project description. See the schedule table above for deliverable due dates.

Late Submissions Policy

Homework: each student has 4 slip days total. No questions asked.
Project: each team has 3 slip days total. No questions asked. Slip days may not be used on in-class activities (e.g., proposal presentation, poster presentation, etc.).
To use slip days, specify the number of days you have used in the textbox on T-Square (when you submit your work).
Each slip day equals 24 hours. E.g., if a submission is late for 30 hours, that counts as 2 slip days
After all slip days are used up, 5% deduction for every 24 hours of delay. (e.g., 5 points for a 100-point homework)
We will not consider late submission of any missing parts of an homework assignment or project deliverable. To make sure you have submitted everything, download your submitted files to double check.
No penalties for medical reasons or emergencies. You must submit a doctor's note or an official letter explaining the emergency.

Distance Learning Sections (Q & Q3)

A standard 3-day lag applies to all homework and project deliverables. For project presentation, a group that has DL student member can choose to:

Present in class without 3-day lag; or
Submit a video presentation with 3-day lag (e.g., screen capture)

Dataset Ideas (may need API, or scraping)

Google public datasets. Thanks Revant!
NYC Taxi data for 2013 (FOILed by Chris Wong). 2013 Trip Data (11.0GB). 2013 Fare Data (7.7GB). Visualization for a days trip. Thanks Jitesh.
Large datasets publicly available. Thanks Gopi!
Georgia Tech's campus data (has APIs): bus info, directory, building, T-square, room reservation, building facilities usage (e.g., electricity, lights, A/C, etc.), Oscar/course info/registration, etc.
Yahoo WebScope
Data.gov: U.S. Government's open data
Freebase
Yelp
Numerous APIs from Google (e.g., Maps, Freebase, YouTube, etc.)
Trulia, Zillow: real estate listing sites
Numerous graph datasets (large and small): SNAP, Konect
Movies data: Rotten Tomatoes, IMDB
List of lists of datasets for recommendations.
Thanks Jon!
Million song dataset by Echo Nest.
It contains not only the basic information of songs (artist, genre, year, length etc), but also some musical features(like tempo, pitch, key, brightness).
Thanks Minwei!
Dataset about soccer games, players, clubs.
No API, but easy to scrape.
For a soccer player: transfer history, performance, nationality, birth date, etc.
For a soccer club: performance, squad, etc.
Thanks Ding!
The Free 'Big Data' Sources Everyone Should Know
Quandl - a dataset search engine for time-series data.
Thanks Henry!
A collection of links to various datasets.
Thanks Vignesh!
UCI also has a collection of links to various datasets sorted for various tasks (Classification, Regression, etc)
Thanks Vinodh!
Amazon AWS Public Data Sets (Thanks Jonathan!)
KDD Cup: annual competition in data mining, like Kaggle
Academic domain: Microsoft Academic Search, DBLP
Retrosheet: MLB statistics (Game/Play logs)
Classification datasets
Thanks Amish!
Various geophysical datasets for the oceans (magnetism, gravity, seismology, etc).
Thanks Ryan!
Social trends (Thanks Jonathan!)
Beer data (Thanks Jonathan!)
Academic torrents (terabytes) (Thanks Vaibhav!)
Article Search API from the New York Times (all the way back to 1851!) (Thanks Guido!)
(Kayak: flight, hotel, car, etc.)
Data Science Initiative - Microsoft Research has various datasets and access to tools that can aid in data science research

Reading materials & Resources

Textbook is NOT required.
Highly recommended good books:
- FREE probability book, by Prof. Guy Lebanon. (From Amazon.)
- A nice D3 tutorial
- Human Computation book by Edith Law and Luis von Ahn

Data Science

Data Science for Business by Foster Provost and Tom Fawcett

Visualization

The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures by Dona Wong
Information Dashboard Design: Displaying Data for At-a-Glance Monitoring by Stephen Few
Show Me the Numbers: Designing Tables and Graphs to Enlighten by Stephen Few

SQL

Video courses and lectures: (1) Coursera, (2) lynda.gatech.edu and search for SQL courses
Interactive SQL tutorials
Books: (1) SQL Cookbook (recipes to solve specific problems), Visual QuickStart Guide (succinct topic-by-topic), SQL Pocket Guide (covers syntax variations of MySQL, Oracle, etc.)
Introductory tutorial

Prerequisites & Expectation

For both CSE 6242 (grad) and CX 4242 (undergrad)

Students are expected to complete significant programming assignments (homework, project) that may involve higher-level languages or scripting (e.g., Java, R, Matlab, Python, C++, etc.).

Some assignments may involve web programming and D3 (e.g., Javascript, CSS).

You are expected to quickly learn many new things. For example, an assignment on Hadoop programming may require you to learn some basic Java and Scala quickly, which should not be too challenging if you already know another high-level language like Python or C++. Please make sure you are comfortable with this.

Please take a look at the assignments (homework and project) of the previous offerings of this course, which will give you some idea about the difficulty level of the assignments.

Basic linear algebra, probability knowledge is expected.

Additional formal prerequisites for CSE 6242

None, but you should have taken courses similar to those listed in the next section, at Georgia Tech or at another school.

Additional formal prerequisites for CX 4242

(Undergraduate Semester level MATH 2605 Minimum Grade of D or
Undergraduate Semester level MATH 2401 Minimum Grade of D or
Undergraduate Semester level MATH 24X1 Minimum Grade of D) or
and
(Undergraduate Semester level MATH 3215 Minimum Grade of D or
Undergraduate Semester level MATH 3225 Minimum Grade of D or
Undergraduate Semester level ECE 3077 Minimum Grade of D or
Undergraduate Semester level ISYE 2027 Minimum Grade of D)
and
(Undergraduate Semester level CS 1371 Minimum Grade of C or
Undergraduate Semester level CS 1372 Minimum Grade of C or
Undergraduate Semester level CX 4010 Minimum Grade of C or
Undergraduate Semester level CX 4240 Minimum Grade of C)

Auditing & Pass/Fail

Due to the class size, I am not offering auditing and pass/fail option this semester.

Previous offerings

See https://poloclub.github.io/#cse6242 for all past course offerings.

Acknowledgements & Related Classes

We thank Amazon's AWS in Education grant program for providing support for Amazon Web Services.
Tableau's data visualization software is provided through the Tableau for Teaching program.

Many thanks to my colleagues for sharing their course materials:

Prof. John Stasko - Information Visualization - Fall 2012
Prof. Jeff Heer - Research Topics in Interactive Data Analysis - Spring 2011
Prof. Christos Faloutsos - Multimedia Databases and Data Mining - Fall 2012

CSE6242 / CX4242, Spring 2016 Data and Visual Analytics