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

CSE6242A,Q / CX4242A, Spring 2018
Data and Visual Analytics

Georgia Tech, College of Computing

4:30 - 5:45pm, 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 complement 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 project exploring novel approaches for interactive data & visual analytics.

Office Hours

Polo Chau	Tue, 3:30PM-4PM + FREE after-class coffee, at Clough Starbucks	Klaus 1324 (Polo's office)
Neetha Ravishankar	Tue, 12-1pm	All TA office hours are held in the open area outside Polo's office
Jennifer Ma Head TA	Mon, 1-2pm
Mansi Mathur	Mon, 1-2pm
Arathi Arivayutham	Wed, 11am-12pm
Vineet Vinayak Pasupulety	Wed, 11am-12pm
Siddharth Gulati	Tue, 12-1pm

Announcements and Discussion

We use Piazza for announcements and discussion.

Everyone must join this class's Piazza, at https://piazza.com/gatech/spring2018/cse6242aqcx4242a.

Double check that you are joining the right Piazza!

When you have questions about class, homework, project, etc., post your questions there. Our teaching staff and your fellow classmates will help answer them quickly. You can also use Piazza 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, please do not post your answers, as that may affect the learning experience of your fellow classmates.

Class Schedule and Lecture Slides !!! Evolving !!!

* Publication-quality figures

Wk	Dates		Topics	Tue	Thu	Events (eastern time)
1	Jan	9, 11	* Course introduction * Big data analytics building blocks * Data Collection	intro	building blocks, buzz words, data collection
2		16, 18	* simple storage (SQLite) * Data cleaning * Class Project overview; Heilmeier questions * GT Github; one drive	SQLite, Data cleaning	project overview; Github	HW1 out
3		23, 25	* Example projects: (1) Firebird: Predicting Fire Risks in Atlanta, by Shang-Tse Chen (2) PASSAGE: A Travel Safety Assistant, by Nilaksh Das * Data integration: knowledge graph; data reconciliation/de-duplication; similarity functions	Firebird, PASSAGE	Data integration
4	Feb	30, 1	* Visualization 101 * Fixing common visualization issues	vis101	vis fix	HW1 due (Fri, 2/2,11:55pm)
5		6, 8	* Fixing presentation issues * Data visualization for the web (D3)	D3	Dr. Kevin Roundy, Symantec Research Labs pub-quality figures	Form project teams by Fri, 2/9; HW2 out
6		13, 15	* Data analytics concepts & tasks * Overview of project proposal and presentation * Scaling up: Hadoop, Pig, Hive	analytics concepts, project proposal and presentation	Hadoop, Pig, Hive
7		20, 22	* Scaling up: Spark, Spark SQL * Scaling up: HBase	spark	hbase
8	Mar	27, 1	* Classification key concepts, k-NN, cross validation	classification	cont'd	HW2 due (Wed, 2/28, 11:55pm); HW3 out
9		6, 8	Project proposal presentations	Show time!	Show time!	Project proposal & slides due (Mon, 3/5, 11:55pm)
10		13, 15	* Ensemble method, bagging, random forests * Classification: decision tree, vis (ROC, AUC, confusion matrix) * Clustering: k-means, hierarchical clustering, DBSCAN * Clustering vis * Graph analytics How to build and store graphs Basics; power laws	classification vis, clustering vis, random forests	graph basics
11		20, 22	Spring break	X	X
12		27, 29	* Graph analytics Centrality Algorithms; (personalized) PageRank Interactive applications; Apolo Evaluating apps (user eval) * Memory-mapping/virtual memory to scale up algorithms	graph centrality & algorithms	mmap	HW3 due (Fri, 3/30, 11:55pm) HW4 out
13	Apr	3, 5	* Text analytics: concepts	text analytics	canceled	Proj progress report due (Fri, 4/6, 11:55pm)
14		10, 12	* Text analytics: algorithms (LSI=SVD) * Time series: algorithms, visualization, & applications	cont'd	time series: basics, linear forecast
15		17, 19	* Closing words * Lessons learned	time series: non-linear forecast, vis	course review; 10 lessons learned	HW4 due (Fri, 4/20, 11:55pm)
16		24	* Project poster presentations	Poster presentation. 4:30pm to 5:45pm-ish. Klaus Atrium. Pizza + drinks served!	X	Proj final report due (Tue, 4/24, 11:55pm)

Homework (50% of grade)

The fastest way to get help with homework assignments is to post your questions on Piazza. If you prefer to address your questions 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 have 4 assignments in total. Tentative

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.
We will not accept any submission that is more than 7 days late.
No penalties for medical reasons or emergencies. And should they arise, you must contact the Dean of Students office. Doctor's notes, medical documentation, explanation of emergencies, etc. should be submitted to the Dean’s office. After their office receives the information, we will notify me on your behalf.

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
IPEDS data: Postsecondary education data from National Centre for Education Statistics
Bureau of Labor Statistics data
Uber data: Anonymized data from over 2 billion trips
Freebase
Yelp
Microsoft Academic Graph
Numerous APIs from Google (e.g., Maps, Freebase, YouTube, etc.)
Zillow: real estate listing site
Numerous graph datasets (large and small): SNAP, Konect
Movies data: 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!
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!). Website offline :( . Older version at web.archive.org
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

This course does NOT have any required textbook. We recommend the following books and resources.

Data science, machine learning, data mining

Data Science for Business by Foster Provost and Tom Fawcett
The Elements of Statistical Learning: Data Mining, Inference, and Prediction by Trevor Hastie, Robert Tibshirani, and Jerome Friedman

Visualization

A nice D3 tutorial
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

Probability

FREE probability book, by Prof. Guy Lebanon. (From Amazon.)

Human Computation

Human Computation book by Edith Law and Luis von Ahn

Office of Disability Services

The Office of Disability Services offers accommodations for students with disabilities. Please contact the office should you need help.

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.

Acknowledgement & 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

CSE6242A,Q / CX4242A, Spring 2018 Data and Visual Analytics