Title: Big Data Analytics Code: CSCI 4030U
Instructor: Jarek Szlichta, jarek [at] uoit [dot] ca
Office hours: Tuesdays 5pm (except reading week), on Jan 14th the office hours will be from 2pm-4pm, due to giving a talk at seminar at UofT.
TA office hours (upon request)
Description This course covers advanced topics in data process and analytics with special emphasis on Big Data. Topics of the course will include, but are not limited to, indexing structures for fast information retrieval, query processing algorithms, distributed storage and processing, scalable machine learning and statistical techniques, and trends of modern very large scale data systems. Students will gain understanding on the theoretical foundation and practical design principles of modern Big Data processing systems.
- Data Mining
- Finding Similar Items
- Mining Data Streams
- Link Analysis
- Frequent Itemsets
- Advertising on the Web
- Recommendation Systems
- Mining Social-Network Graphs
- Dimensionality Reduction
- Large-Scale Machine Learning
Policies: Refer to following link. Refer to UOIT Faculty of Science academic policies
Required readings: See Blackboard; Mining of Massive Datasets, Jure Leskovec, Anand Rajaraman, Jeff Ullman
Lecture Notes (always check newest version of the slides):
1. Introduction PDF
2. Association Rules Mining PDF
3. Finding Similar Items PDF
4. Clustering PDF
5. Data Curation and Analytics (Slides posted on Blackboard)
6. Large Scale Machine-Learning PDF
7. Link Analysis PDF
8. Data Streams PDF
9. Distributed Computing (online) PDF
Labs will start in the week of 20th of January
- Any student who misses an examination without a valid medical reason and documentation will receive zero for that examination/tutorial. Those with medical documentation will either be given a makeup exam/tutorial or will have the weight of the examination (final exam/midterm) added to the final exam.
- Midterm I, 11th of Feb (Tuesday), BRING YOUR LAPTOP.
- Midterm II (online), March 24th (Tuesday), USE YOUR LAPTOP.
- Student Presentations: March 31st (online).