This course offers an introduction to data science and machine learning paving the way for students to learn data analytics principles. In particular, this course begins with a brief history of data analytics and data science, followed by regression analysis, regression and classification trees, and ends with introductions to K-means clustering, principal component analysis (PCA). Each lecture has associated with it a practical lab session which students will put theory into practice offering students a hands-on approach to learning the material.
Saturday, May 10 - August 9: 9:00 a.m. - 12:00 p.m. (Virtual)
Location:
ONLINE
Instructor:
Pedram Habibi
Notes:
This course is offered as a virtual classroom with classes held, as per the scheduled day/time. Classes will be held online, using a combination of synchronous, real-time, instructor led sessions, supplemented with asynchronous activities. You should expect to attend class at the same time, each week, although not every session will be a full 3 hours of live instruction. Rather, classes will provide learners the opportunity to apply what has been covered by your course facilitator. Laptop computer required; No class May 17, and August 2. Please note registering after the course start date may delay course access and does not permit exceptions to coursework due dates, late textbook purchases, or the refund policy
Instructors
Pedram Habibi
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Pedram Habibi has a BSc in Physics, a Bachelor of Applied Science in Computer and Communication Networking, and a Master of Engineering in Electrical and Computer Engineering. Pedram has worked for many years in telecommunication and other IT domains. He has taught Data Analytics and Technology topics at McMaster University and Ontario Colleges. His interests are Big Data Analytics, AI, ML, Cloud computing, IoT, Cyber Security, DevOps, and Networking.
