The fastest way to get help with homework assignments is to post your questions on Piazza. That way, only our TAs and instructor can help, your peers can too.
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 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.
For special cases such as failed submissions due to system errors, missing grades, failed file uploads, emergencies that prevent you from submitting, personal issues, you can contact the staff using a private Piazza post.
TAs will hold office hours starting week 2, except on Georgia Tech holidays (e.g., thanksgiving, MLK day, spring break). Each office hour session will be run by at least one TA, and is 1 hour long. See GT’s academic calendar for the full list of holidays (https://registrar.gatech.edu/calendar). We will spread the office hours across weekdays.
Please note that you are always welcome to ask questions on Piazza. Office hours supplement Piazza, and do not replace it.
|Polo Chau||Tue, 3:30PM-4PM
+ FREE after-class coffee, at Clough Starbucks
|Klaus 1324 (Polo's office)|
|Neetha Ravishankar||Mon, 12:30 - 1:30pm||All TA office hours are held in the open area outside Polo's office|
|Jennifer Ma||Tue, 11am - 12pm|
|Mansi Mathur||Tue, 11am - 12pm|
|Wed, 4 - 5pm|
|Vineet Vinayak Pasupulety||Wed, 4 - 5pm|
|Siddharth Gulati||Mon, 12:30 - 1:30pm|
|1||Aug||21,23||* Course Introduction
* Analytics Building Blocks
* Data Science Buzzwords
* Data Collection
|intro||building blocks, buzzwords, data collection|
* Data Cleaning
* Class Project Overview
* Code Back-up & Version Control
|SQLite, git||cleaning, project overview||HW1 out
Fri, Aug 31
* Example projects:
(1) Firebird: Predicting Fire Risks in Atlanta, by Shang-Tse Chen
(2) PASSAGE: A Travel Safety Assistant, by Nilaksh Das
* Data Integration
|Firebird, PASSAGE, project overview||data integration, vis 101|
|4||11,13||* Visualization 101
* Data Visualization for Web (D3)
|Form project teams by
Fri, Sept 14, 11:55pm
|5||18-20||* Fixing Common Visualization Issues
* Data Analytics, Concepts and Tasks
* Overview of project proposal and presentation
|fix vis||publication-fig; analytics tasks|
|6||25-27||* Scalable Computing: Hadoop
* Scalable Computing: Pig
* Scalable Computing: Hive
|hadoop; pig;||hive; spark|
|7||Oct||2-4||* Scalable Computing: Spark
* Scalable Computing: HBase
* Classification: concepts, cross-validation, k-NN, decision trees
|8||9-11||* Visualization for Classification: ROC, AUC, confusion matrix
* Introduction to Clustering: k-means, hierarchical clustering, DBSCAN, vis
|Fall recess||classification; clasification-vis||
|9||16-18||* Project proposal presentation||Show time!||Show time!||
Proposal document due
Proposal presentation slides due
|10||23-25||* Ensemble Method: bagging, random forests
||clustering; bagging, random forest||graph laws||
|11||Nov||30-1||* Graph Analytics:
centrality; algorithms-(personalized) PageRank; interactive applications
* Scaling up Algorithms with Virtual Memory
|centrality, pagerank||mmap||HW4 out
Fri, Nov 2
|Progress Report due
Fri, Nov 2, 11:55pm
|12||6-8||* Text Analytics: concepts, algorithms (LSI=SVD)||X||text algorithms|
|15||27-29||* Time series: algorithms, visualization, & applications
* Project poster presentation
|Poster presentation. 4:30pm to 5:45pm-ish. Klaus Atrium. Pizza + drinks served!||HW4 due
Mon, Nov 26, 11:55pm
|16||Dec||4||Lessons learned and closing words||X||
Final report due
Students have at least 2 weeks to complete each homework assignment. Some students waited until the last week, and could not finish. It is critical to plan ahead and prepare for the significant time needed.
Basic linear algebra, probability and statistics knowledge is also expected.
The Office of Disability Services offers accommodations for students with disabilities. Please contact the office should you need help.
None, but you should have taken courses similar to those listed in the next section, at Georgia Tech or at another school.
If you are an Analytics (OMS or campus) degree student, you should first take CSE 6040 and do very well in it; if necessary, please also first take CS 1301.
We thank Intel's support in curriculum development for the memory mapping module (scaling up algorithms with virtual memory).
We thank Amazon Educate for providing free cloud credit for Amazon Web Services. We are excited to be am AWS partner university and part of AWS Educate's private beta.
We thank Microsoft Azure's special grant for providing free cloud credit.
We thank Tableau for Teaching program's data visualization software.Many thanks to my colleagues for sharing their course materials: