CMPT 733: Big Data Programming II
Objectives
This course is designed for students who have completed CMPT 726 and CMPS 732, and want to further their knowledge and skills in data science and big data. It aims to bridge the gap between theoretical concepts and practical applications of machine learning and data engineering, by exposing them to current trends and challenges in data science and big data.
The course will cover essential topics such as data wrangling, data visualization, data storytelling, and machine learning workflows, and introduce students to cutting-edge techniques and tools for dealing with large-scale and complex data. By the end of this course, students should be able to tackle real-world data problems, ask meaningful questions about data, design effective data-processing pipelines, and communicate their findings.
Topics
- Introduction to Data Science
- Data Preparation
- Visualization
- Statistics
- Deep Learning
- Practical Machine Learning (AutoML, Explainable AI, Feature Engineering)
- Anomaly Detection
- Cloud Computing
- Responsible Data Science
- Communication
Logistics
Instructor
- Steven Bergner | Webpage
TAs
- Gaurav Bhagchandani
- Jialiang Guo
- Nidhi Kantekar
Lectures
- Time: Tue 10:30 PM - 12:20 PM
- Location: BLU9660
Labs
Lab G101:
- Time: Wed 11:30 AM to 1:20 PM (Instructor + TA) and Fri 1:30 PM to 3:20 PM (TAs)
- Location: SECB1010
Lab G102:
- Time: Wed 1:30 PM to 3:20 PM (Instructor + TA) and Fri 11:30 AM to 1:20 PM (TA)
- Location: SECB1010
Grading
- Assignments: 11 × 4% = 44%
- In-lab team exercise: 9%
- Final Project: 47% (2% proposal + 15% milestone + 15% final presentation + 15% code & report & video)
Schedule
| Week | Date | Event Type | Description | Course Materials |
|---|---|---|---|---|
| wk 1 | Tue Jan 9 | Lecture 1 | Course Introduction | slides |
| Wednesday January 17 2024 | A1-1 Due | Assignment #1-1 Due | A1-1 | |
| Wednesday February 07 2024 | A1-2 Due | Assignment #1-2 Due | A1-2 | |
| wk 2 | Tue Jan 16 | Lecture 2 | Data Preparation | slides |
| Friday January 26 2024 | A2 Due | Assignment #2 Due | A2 | |
| wk 3 | Tue Jan 23 | Lecture 3 | Statistics (Part I) | slides |
| Monday February 05 2024 | A3 Due | Assignment #3 Due | A3 | |
| wk 4 | Tue Jan 30 | Lecture 4 | Data Visualization (Part I) | slides |
| Monday February 12 2024 | A4 Due | Assignment #4 Due | A4 | |
| wk 5 | Tue Feb 6 | Lecture 5 | Practical Machine Learning (Part I) | slides |
| Friday February 16 2024 | A5 Due | Assignment #5 Due | A5 | |
| wk 6 | Tue Feb 13 | Lecture 6 | Deep Learning (Part I) | slides |
| Tuesday February 20 2024 | Proposal Due | Course Project Proposal Due | ||
| Tue Feb 20 | Reading Break | No Classes | ||
| wk 7 | Tue Feb 27 | Lecture 7 | Data Visualization (Part II) | slides |
| Monday March 18 2024 | A7 Due | Assignment #7 Due | A7 | |
| wk 8 | Tue Mar 5 | Lecture 8 | Practical Machine Learning (Part II) | slides |
| Fri Mar 8 | Milestone Presentation | |||
| Tue Mar 12 | Friday March 22 2024 | Assignment #8 Due | A8-1, A8-2 | |
| wk 9 | Tue Mar 12 | Lecture 9 | Statistics (Part II) | slides |
| Friday March 29 2024 | A9 Due | Assignment #9 Due | A9-1, A9-2 | |
| wk 10 | Tue Mar 19 | Lecture 10 | Deep learning (Part II), Natural Language Processing | slides |
| Wednesday April 10 2024 | A10 Due | Assignment #10 Due | A10 lec10-nlp-ds | |
| wk 11 | Tue Mar 26 | Lecture 11 | Responsible Data Science | slides |
| Monday April 15 2024 | A11 Due | Assignment #11 Due | A11-1, A11-2 | |
| wk 12 | Tue Apr 2 | NO class - Final project prep | ||
| Fri Apr 5 | ||||
| wk 13 | Tue Apr 9 | Final Project Presentation & Code | Course Project Presentation | |
| Fri Apr 12 | Report & Video Due | Course Project Report Due |