This course has concluded. See 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
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
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.

Project (50% of grade)

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

Late Submissions Policy

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)

Dataset Ideas (may need API, or scraping)

Reading materials & Resources

Data Science



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
(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)
(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 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