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

CSE6242 / CX4242, Fall 2017
Data and Visual Analytics

Georgia Tech, College of Computing

4:35 - 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

Picture Polo Chau Tue, 3:30PM-4:00PM
+ FREE after-class coffee, at Clough Starbucks
Klaus 1324
Picture Kiran Sudhir
Head TA
Thu, 3:05PM-4:05PM Klaus- Open area next to Polo’s office
Picture Varun Bezzam Wed, 12:30PM-1:30PM Klaus- Open area next to Polo’s office
Picture Yuyu Zhang Wed, 12:30PM-1:30PM Klaus- Open area next to Polo’s office
Picture Akanksha Bindal Mon, 11:00AM-12:00PM Klaus- Open area next to Polo’s office
Picture Vishal Bhatnagar Mon, 11:00AM-12:00PM Klaus- Open area next to Polo’s office
Picture Vivek Iyer Thu, 3:05PM-4:05PM Klaus- Open area next to Polo’s office

Announcements and Discussion

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

Wk Dates Topics Tue Thu Events (eastern time; EST)
1 Aug 22, 24 * Course introduction
* Big data analytics building blocks
intro building blocks, data collection  
2 29, 31 * Data Collection, and simple storage (SQLite)
* Data cleaning
* Class Project overview; Heilmeier questions
* Data Cleaning: Starting with the End in Mind (VantagePoint) by Prof. Alan Porter, Denise Chiavetta, and Stephen Carley
SQLite, Data cleaning VantagePoint lecture, project overview HW1 out
3 Sept 5, 7 * Example projects:
(1) Firebird: Predicting Fire Risks in Atlanta, by Shang-Tse Chen
(2) PASSAGE: A Travel Safety Assistant, by Nilaksh Das
* GT Github; one drive
* Data integration: knowledge graph; data reconciliation/de-duplication; similarity functions
Firebird, PASSAGE Github, data integration, analytics concepts
4 12, 14 * Visualization 101
Irma vis101 HW1 due (Fri, 9/15,11:55pm)
5 19, 21 * Data visualization for the web (D3)
* Fixing common visualization issues
(* Fixing presentation issues)
vis fix D3 Form project teams by Fri, 9/22;
HW2 out
6 26, 28 * Scaling up: Hadoop, Pig, Hive
* Data analytics concepts & tasks
* Overview of project proposal and presentation
Hadoop, Pig, Hive analytics tasks, project proposal and presentation
7 Oct 3, 5 * Scaling up: Spark, Spark SQL
* Scaling up: HBase
spark hbase
8 10, 12 * Classification key concepts, k-NN, cross validation
* Clustering: k-means, hierarchical clustering, DBSCAN
(student recess)
classification, clustering HW2 due (Wed, 10/11, 11:55pm);
HW3 out
9 17, 19 Project proposal presentations Show time! Show time! Project proposal & slides due (Mon, 10/16, 11:55pm)
10 24, 26 * Classification: decision tree, vis (ROC, AUC, confusion matrix)
* Clustering vis
* Ensemble method, bagging, random forests
* Graph analytics
  • How to build and store graphs
  • Basics; power laws
classification vis, clustering vis, ensemble, random forests graph basics
11 Nov 31, 2 * 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, 11/3, 11:55pm)
HW4 out
12 7, 9 * Text analytics: concepts
* Text analytics: algorithms (LSI=SVD)
text analytics cont'd Proj progress report due (Fri, 11/10, 11:55pm)
13 14, 16 * Time series: algorithms, visualization, & applications
time series: basics, linear forecast time series: non-linear forecast, vis
14 21, 23 Thanksgiving X X
15 28, 30 * Publication-quality figures
* Project poster presentations
pub-quality figures, lessons learned Poster presentation. 4:30pm to 6pm-ish. Klaus Atrium. Pizza + drinks served! HW4 due (Fri, 12/1, 11:55pm)
16 Dec 5 * Closing words and course review
lessons learned X Proj final report due (Tue, 12/5, 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 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.

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

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

Data science, machine learning, data mining




Human Computation

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.

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: