📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Advanced Data Science Techniques for Academicians, from foundations to a certified capstone project.
Research Design
• Aligning analysis plan with research question before data collection
• Effect size, confidence intervals and moving beyond p-value reporting
• Preregistration and analytic flexibility as a source of false findings
Modelling
• Multilevel and mixed-effects models for nested data
• Structural equation modelling and its assumptions
• Bayesian estimation and reporting posterior uncertainty
Text & Networks
• Text mining and topic modelling for corpus-scale research
• Bibliometric and citation network analysis
• Content analysis with inter-coder reliability
Reproducibility
• Reproducible pipelines and computational environment capture
• Data deposition, FAIR principles and licensing
• Responding to replication requests and sharing code responsibly
Dissemination
• Reporting statistical methods to journal standards
• Reading and reviewing quantitative papers critically
• Grant applications: justifying method and sample size
Earn government-registered certification in Advanced Data Science Techniques for Academicians
e-Certificate and e-Marksheet issued on successful completion.
