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.
The deadline to enroll in CMTH642 for Fall term is September 12, 2022.
The deadline to enroll in CMTH642 for Winter term is January 16, 2023.
Students will also not be allowed to swap between sections of the Data Analytics courses after the above dates.
You must download the Microsoft Remote Desktop in order to access the software needed to complete the requirements for this course. Prior to your first class, you are strongly advised to test the computer you plan to use, as machines operated using a third-party administrator (such as laptops provided by a workplace) may not allow access to the required software/download(s).
International students should use their own virtual private network (VPN) software to connect to University resources.
RequisitesPrerequisite: (CIND 119, CIND 123 AND CIND 830) or (CMTH 304 or CMTH 380) AND (CCPS 109 or CPS 118)
- Data Analytics, Big Data, and Predictive Analytics : Required Courses
- Science, Technology, Engineering, and Mathematics (STEM) : Electives (select 2)