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.

In today’s era, many individuals and businesses benefit from having an online presence that helps with identity branding and/or to promote their services and products. In this project, students who are on their way to become professional front-end developers will design and develop a variety of web applications that help you satisfy your clients’ business needs. This course is part of the Frontend Development certificate program.

Attracting new customers and retaining existing ones are the key to a successful business but often not easy to do in this hyper-competitive business world. Marketing plays a key role in the customer management process to help a business achieve its growth objectives. In this project, students from McMaster University’s Centre for Continuing Education will analyze your organization’s current business to business marketing strategy and make recommendations on how to improve your results.

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.