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.
+ ocassional FREE after-class coffee, at Clough Starbucks
|Outside Clough 152|
|Monday, 10:00AM - 11:00AM||All TA office hours are held on the second floor of CODA in the space near room 234|
|Sushanto Praharaj||Tuesday, 10:00AM - 11:00AM|
|Saifil Nizarali Momin||Tuesday, 11:30AM - 12:30PM|
|Apurv Priyam||Wednesday, 1:00PM - 2:00PM|
|Shrishti||Thursday, 12:00PM - 1:00PM|
|Aastha Agrawal||Friday, 9:00AM - 10:00AM|
|1||Jan||7,9||* 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, Jan 17
* Data Integration
* Visualization 101
|data integration, vis 101||vis 101|
* Data Visualization for Web (D3)
* Data Analytics, Concepts and Tasks
|D3||D3, analytics tasks||
* Example projects:
(1) Firebird: Predicting Fire Risks in Atlanta
(2) PASSAGE: A Travel Safety Assistant, by Nilaksh Das
* Fixing Common Visualization Issues
|Firebird, PASSAGE; fix vis;||fix vis; clustering||
Form project teams by
Fri, Feb 7
* Introduction to Clustering: k-means, hierarchical clustering, DBSCAN, vis
* Overview of project proposal and presentation
* Scalable Computing: Hadoop
|clustering; project; hadoop||hadoop|
* Scalable Computing: Pig
* Scalable Computing: Hive
* Scalable Computing: Spark
* Scalable Computing: HBase
* Classification: concepts, cross-validation, k-NN, decision trees
|pig, hive, spark||hbase; classification||HW2 due
Fri, Feb 21
Fri, Feb 21
|8||25,27||* Project proposal presentation||Show time!||Show time!||Proposal document due
Mon, Feb 24
Proposal presentation slides due
Mon, Feb 24
* Visualization for Classification: ROC, AUC, confusion matrix
* Ensemble Method: bagging, random forests
|clasification-vis; bagging, random forest||(cont'd)|
* Graph analytics: basics & power laws
* Graph analytics: centrality & personalized PageRank
* Graph analytics: interactive applications
* Graph analytics: scaling up with virtual memory
* publication-quality figures
This March 10 class (this class only) meets in Klaus 1443.
graph basics, laws, centrality, pagerank,
|(cont'd), mmap, publication-quailty figures||HW3 due
Fri, Mar 13
Fri, Mar 13
|12||24,26||TBD||Progress Report due
Fri, Mar 27
|13||31,2||* Text Analytics: concepts, algorithms (LSI=SVD)||text algorithms||HW4 due
Fri, Apr 3
* Time series: basics and linear forecasting
* Time series: non-linear forecasting, visualization
basics and linear forecasting
nonlinear forecasting and vis
|15||14,16||* [tentative] Advanced topics: interpretable AI * Lessons learned and closing words||
|16||21||* Project poster presentation||Poster presentation. 4:30pm to 5:45pm-ish. Klaus Atrium. Pizza + drinks served!||
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.
Academic support, and personal support: Office of the Dean of Students, Counseling Center, Health Serivces, Women's Resource Center, LGBTQIA Resource Center, Veteran's Resource Center, Georgia Tech Police.
All content and course materials can be accessed online. There is no textbook for this course.
All Georgia Tech students have FREE access to https://www.safaribooksonline.com, where you can find a huge number of highly rated and classic books (e.g., the "animal" books) from O'Reilly and Pearson covering a wide variety of computer science topics, including some of those listed below. Just log in with your official GT email address, e.g., email@example.com.
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: