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.

Piazza Discussion Forum

We will use Piazza for discussion (e.g., homework, project) and for ALL announcements. Post your questions there, and the teaching staff and your fellow classmates will be able to 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.

Office Hours

Instructor	Polo Chau	Tue, 11am-12pm, Klaus 1324
TA	Brian Kahng	Tue, 1-2pm, Klaus 1212
TA	Seungyeon Kim	Wed, 3-4pm, Klaus 1315
TA	Alan Zhang	Wed, 1:30-2:30pm, Klaus 2126
TA	Drew Wei	Wed, 11am-12pm, Klaus 2126

Schedule (tentative)

Video recordings of the lectures are available at http://gtcourses.gatech.edu.

13

Date		Topic	Tue	Thu	Events
Aug	19, 21	* Course introduction * Big data analytics building blocks, data Collection, and simple storage (SQLite)	slides	slides
	26, 28	* Data cleaning & integration * Toward Coupling Cognition and Computation through Interactive Interfaces by Prof. Alex Endert	Guest lecture by Prof. Alex Endert	slides	HW1 out (Tue)
Sept	2, 4	* Visualization fundamentals * Data visualization for the web (D3)	Slides	Slides	lectures by Chad Stolper
	9, 11	* Data Mining Concepts & Tasks * Visualization DOs and DON'Ts; Heilmeier Questions	Slides	Slides	HW1 due (Mon)
	16, 18	* Deep learning * Graph analytics how to build and store graphs basics; power laws; centrality graph statistics and how to compute them	Guest lecture by Josh Patterson. Slides deck #1, deck #2	Slides	HW2 out (Thu)
	23, 25	* Graph analytics graph algorithms interactive tools applications	Slides	Same as Tue
Oct	30, 2	* Scaling up (Hadoop, Pig, HBase, Hive)	Slides	Slides	HW2 due (Wed). Form proj team (Fri)
	7, 9	* Scaling up (Spark, Spark SQL, etc) * Dimensionality Reduction: techniques, visualization, practitioner's guide	Slides	Slides	Proj proposal due (Fri)
	14, 16	* Classification (techniques, visualization & interaction)	No class (Fall student recess)	Slides
	21, 23	Project proposal presentations			HW3 out (Mon)
	28, 30	* Ensemble Methods	Guest lecture by Prof. Jimeng Sun	Slides
Nov	4, 6	* Clustering * Text analytics: concepts	Slides	Slides	HW3 due (Wed)
	11, 13	* Text analytics: algorithms (LSI=SVD) * Time series: algorithms	Slides	Slides	Proj progress report due (Mon), HW4 out (Tue)
	18, 20	* Time series: algorithms, visualization, & applications * Human Computation	Slides	Slides
	25, 27	Thanksgiving week	Canceled	TG	HW4 due (Mon)
Dec	2, 4	* Closing words and course overview * Project poster presentations	Overview	Poster session: 9:30-12	Proj final report due (Fri)

Grading

• 40% Homework
• 50% Project
• 10% Class and Piazza participation

Late Submissions Policy

• Strictly enforced.
• For homework assignments (applies to HW2 and onwards): 10% deduction for every 24 hours of delay. (e.g., deduct 9 points per 24 hours, for a 90-point homework.)
• For project deliverables (excluding presentations), 5% deduction for every 24 hours of delay.
• 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:

1. present in class without 3-day lag or 
2. submit a video presentation with 3-day lag (e.g., screen capture)

Homework

We plan to have 4 assignments in total.

All assignments should be submitted via T-Square.

If you have questions the about assignments, the fastest way for us to help is if you 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, each student *must* write up their own answers. All GT students must observe the honor code.

• [5%] HW1: Collecting and Visualizing Data; SQLite; D3 Warmup
• [10%] HW2: D3 Graphs and Visualization
• [15%] HW3: Hadoop, Spark, and Pig
• [10%] HW4: Decision Trees and Weka

Project

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

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!
• 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.)

Auditors

Auditors must first obtain instructor's permission of the instructor, then enroll in the course. The auditor must attend all lectures, and optionally complete the assignments.

Textbooks and reading materials

• None required.
• Highly recommended good reads:
  • Data Science for Business by Foster Provost and Tom Fawcett
  • FREE probability book, by Prof. Guy Lebanon. (From Amazon.)
  • A nice D3 tutorial
  • Human Computation book by Edith Law and Luis von Ahn

Prerequisites

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)

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

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