This course is part of the Data Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. 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 in which students will put "theory into practice" offering students a hands-on approach to learning the material.

Is your organization eager to find and assess top talent? Students in McMaster University’s Human Resources Management program will provide actionable recommendations on talent acquisition, branding, marketing, operations, and planning to help you source candidates for challenging roles.

This course is part of the Big Data Programming and Analytics certificate program. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. This course is designed to present the fundamental concepts and theories in Data Analytics and promote the application to the workplace and professional practice. Students begin with an exploration of MongoDB which is a document database with scalability and flexibility for queries and indexing, and progress to the ELK stack – a technology stack used for logging with different components, such as Elasticsearch, Logstash, and Kibana. Course activities will include instructor presentations, required readings and experiential learning activities (i.e. case studies, group discussions, projects, etc.).

This course is part of the Big Data Programming and Analytics certificate programs. Students in the program are adult learners with a post-secondary degree/diploma in computer science, engineering, business, etc. This course builds on the fundamental principles of data analytics, this course advances to modern machine learning techniques such as neural network, deep learning, and reinforcement learning as well as NLP and text analysis. Application activities are structured to provide an introductory level of how machine learning techniques are applied to big data analytics.