Title: Big Data Analytics Code: CSCI 4030U
Instructor: Jarek Szlichta, jaroslaw [dot] szlichta [at] uoit [dot] ca
Office hours: Wednesdays 5-6pm (except reading week)
TA office hours in UA4029 (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
Marking Scheme: Labs and Project 30% (10% + 20%), Midterm I: 20%, Participation and Presentation: 10%, Final Midterm : 40%.
Late project submissions: 50% of the mark (within the first week).
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. Bringing Order to Big Data (slides on Blackboard)
4. Distributed Computing PDF
5. Finding Similar Items PDF
6. Large Scale Machine Learning PDF
7. Link Analysis PDF
8. Clustering PDF
9. Data Streams PDF
Labs will start in the week of 21st of January
Lab tasks will be posted on Blackboard
- 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, 27th of Feb, BRING YOUR LAPTOP.
- Final midterm, April 3rd, BRING YOUR LAPTOP.