Introduction to Advancement Analytics Using R-Introduction to Advancement Analytics Using R Slides

Introduction to Advancement Analytics Using R Slides

Pdf Summary

The document outlines a session on "Introduction to Advancement Analytics Using R" organized by the Council for Advancement and Support of Education (CASE) in 2022. Led by Nick Campisi, Director of Data Science at CASE, and Stefan Byrd-Krueger, CEO of PTKO, the session's objective is to guide participants in deploying data effectively across teams and systems. The course covers the contexts, strategies, and technologies pertinent to data analytics, focusing on learning and exploring the R programming language.<br /><br />Participants are encouraged to explore how data impacts workflows within organizations, addressing strategy definition, data tracking, reporting and analysis, and the adoption and optimization of analytics. The session aims to shift the focus from data-driven to decision-driven analytics – emphasizing that understanding the decisions beforehand makes analysis more effective.<br /><br />Advanced data analysis helps participants derive new metrics, interpret complex data, and enhance their understanding. The session covers strategic data integration, identifying existing resources, gaps, and required improvements in initial datasets. <br /><br />As AI plays an increasingly larger role in analytics, participants are advised to use AI tools that are institutional approved, recognize the evolution of AI-powered analysis, and employ Gen AI to support softer aspects of analytics. They are also reminded to stay cautious with AI-generated analyses and only use it as far as they can understand and debug the outcomes.<br /><br />Tools like Microsoft Visual Studio Code, with capabilities for multiple programming languages and extensions like Copilot, are recommended for learning and collaborative analytics. The session concludes by highlighting the democratizing effect of AI: lowering the entry barriers for novice users while amplifying the capabilities of expert analysts. Lastly, R is favored for its cost-effectiveness, robust capabilities, and supportive community, making it well-suited for advancement analytics.

Keywords

Advancement Analytics

R Programming

Data Science

AI in Analytics

Strategic Data Integration

Decision-driven Analytics

Microsoft Visual Studio Code

Data-driven Workflows

AI Tools

Nick Campisi