This course has concluded. See for all past course offerings.

CSE6242 / CX4242, Spring 2017
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 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 research project exploring novel approaches for interactive data & visual analytics.

Office Hours

Picture Polo Chau Tue, 3:30-4:00pm
+ FREE after-class coffee, at Clough Starbucks
Klaus 1324
Picture Meghna Natraj
Head TA
Mon, 1-2PM Klaus- Open area next to Polo’s office
Picture Fred Hohman Mon, 1-2PM Klaus- Open area next to Polo’s office
Picture Bhanu Verma Wed, 1-2PM CULC (3rd floor), Common area near 325, take right from stairs, walk few steps, common area is on the left
Picture Chirag Tailor Wed, 1-2PM CULC (3rd floor), Common area near 325, take right from stairs, walk few steps, common area is on the left
Picture Kiran Sudhir Thu, 12-1PM Klaus- Open area next to Polo’s office
Picture Varun Bezzam Thu, 12-1PM Klaus- Open area next to Polo’s office

Announcements and Discussion

In-class announcement slides

We use Piazza for announcements and discussion.

Everyone must join this class's Piazza, at

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, but please do not post your answers, as that may affect the learning experience of your fellow classmates.

Class Schedule and Lecture Slides

Date Topic Tue Thu Events
Jan 10, 12 * Course introduction
* Big data analytics building blocks
intro analytics building blocks  
17, 19 * Data Collection, and simple storage (SQLite)
* Data cleaning
data collection cleaning, GT Github HW1 out
24, 26 * Class Project overview; Heilmeier questions
* Example projects:
(1) Firebird: Predicting Fire Risks in Atlanta, presented by Wenwen Chang
(2) PASSAGE: A Travel Safety Assistant, presented by Nilaksh Das, Meghna Natraj
* Data integration: knowledge graph; data reconciliation/de-duplication; similarity functions
Firebird, PASSAGE, project integration
Feb 31, 2 * Visualization 101
* Fixing common visualization issues
(* Fixing presentation issues)
vis101 fix vis HW1 due (Fri, 11:55pm)
7, 9 * Data visualization for the web (D3)
* Data analytics concepts & tasks
d3 concepts Form project teams by Friday;
HW2 out
14, 16 * Scaling up: Hadoop, Pig, Hive hadoop pig, hive, spark
21, 23 * Industry Talk: Kristin Ottofy on Microsoft Azure
* Scaling up: Spark, Spark SQL
Microsoft Azure talk spark, project proposal and presentation
Mar 28, 2 * Classification key concepts, k-NN, decision tree, cross validation

classification key concepts continue HW2 due (Wed, 11:55pm);
HW3 out
7, 9 Project proposal presentations Show time! Show time! Project proposal & slides due (Mon, 11:55pm)
14, 16 * Classification vis (ROC, AUC, confusion matrix)
* Scaling up: HBase
* Intro to clustering; DBSCAN
Classification Vis, hbase clustering
21, 23 Spring Break X X
28, 30 * Graph analytics
  • How to build and store graphs
  • Basics; power laws
  • Centrality
  • Algorithms; (personalized) PageRank
  • Interactive applications
  • Evaluating apps
graph basics & laws graph centrality HW3 due (Fri, 11:55pm)
Apr 4, 6 * Ensemble method, bagging, random forests
* Memory-mapping/virtual memory to scale up algorithms
pagerank, apolo, user eval random forests, MMap HW4 out
Project progress report due (Fri, 11:55pm EST)
11, 13 * Text analytics: concepts
* Text analytics: algorithms (LSI=SVD)
text, LSI/SVD cont'd
18, 20 * Time series: algorithms, visualization, & applications
(* Dimension reduction: PCA, MDS, LDA, IsoMap)
* Project poster presentations
time series, non-linear forecasting Poster presentation. 4:30pm to 6pm-ish. Klaus Atrium. Pizza + drinks served! HW4 due (Sun, 11:55pm)
25 * Closing words and course overview
Review \ lessons learned X Proj final report due (Tue, 11:55pm EST)

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 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

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