Course DescriptionThis course is designed to give students an overview of big data, state of the practice in analytics, the role of the data scientist, big data analytics in industry verticals, and analytics life-cycle as an end-to-end process. It focuses on key roles for a successful analytic project, main phases of the life-cycle, developing core deliverables for stakeholders, team work skills, and problem solving skills.
What Will You Learn?
This course provides a technical foundation with skills in critical thinking, knowledge extension and transfer, research application, and report writing with respect to big data and data analytics.
Learn how to:
- Engage in machine learning using Weka and IBM Watson.
- Employ appropriate statistical tests to test hypotheses on data.
- Implement machine learning algorithms both in supervised and unsupervised learning.
- Utilize relational database management systems, NoSQL and Hadoop databases.
- Produce relevant visualizations from data.
Classroom sessions for this course are held in a computer lab and attendance is required to be successful in the course.
Open lab hours are also available outside of class time to complete the mandatory labs and assignments.
Course materials, video lectures and discussions are delivered and facilitated online within the D2L Learning Management System.
Throughout the semester, student questions related to course content may be answered either by the instructor on discussion board or by an online tutor via email. For more information, please email Anne-Marie Brinsmead, Program Director, at firstname.lastname@example.org
- Data Analytics, Big Data, and Predictive Analytics : Required Courses
- Health Informatics : Electives (select 4)