Market Research Data Analysis and Governance with R

You will develop reproducible analytics practices using R, paired with governance controls that make research outputs auditable and reliable for stakeholders. The course begins with file management and naming conventions, metadata tagging, and data-quality KPI monitoring to ensure high data integrity standards. It then introduces core R skills for data import, tidy transformations, and pipe-based workflows to join, filter, and aggregate multi-source datasets using the Tidyverse ecosystem. You will learn to author parameterized R Markdown reports to automate regular reporting and to perform diagnostic tests—such as cross-validation and resampling—to evaluate the robustness of regression and predictive modeling techniques commonly used in market research. The curriculum embeds responsible LLM summarization of qualitative data and synthetic-data evaluation use-cases, teaching you how to detect and mitigate hallucination and bias in automated outputs. Labs focus on building end-to-end analytic pipelines that produce reproducible deliverables, paired with rigorous checks that validate metrics against source data to ensure trustworthy results. You will conclude the course by creating a portfolio-ready Data Pipeline and Model Validation Lab, demonstrating your ability to manage the entire data lifecycle from raw ingestion to predictive modeling and executive-ready automated reporting.