Course DescriptionThis course builds on the previous Basic Methods course and covers more advanced concepts including classification and clustering algorithms, decision trees, linear and logistic regression, time series analysis, and text analytics. The course will provide applied knowledge on how to analyze large scale network data produced through social media. In this context topics include network community detection, techniques for link analysis, information propagation on the web and information analysis of social media.
What Will You Learn?
This course provides skills in the application of knowledge transfer, advanced critical thinking, research application, and report writing.
Learn how to:
- Manage advanced data cleansing to prepare the data for analysis.
- Perform statistical comparisons by hypothesis testing.
- Explore how to practically analyze large scale network data with the use of models for network structure and evolution.
- Optimize the structures and dynamics of self-organizing networks such as the Web.
- Differentiate between parametric and non-parametric statistical testing.
- Recognize feature selection techniques for use in modeling.
- Contextualize and leverage small world phenomena.
- Know how to build statistical learning models for clustering, classification, and regression to represent domains under study using R and Python.
- Identify available problem-solving approaches and methods.
- Develop an overall understanding of experimental design in data science projects.
- Recognize the dimensionality reduction techniques for big datasets.
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 email@example.com
RequisitesDepartment Consent Required
- Data Analytics, Big Data, and Predictive Analytics : Required Courses
- Science, Technology, Engineering, and Mathematics (STEM) : Electives (select 2)