Integration Program

The Data Science Integration Program is an innovative professional development initiative designed for instructors in higher education in the Santa Cruz and Monterey Bay area. This comprehensive program empowers educators to seamlessly integrate data science elements into their courses while enhancing their pedagogical skills through evidence-based teaching practices.

Data Science Integration

Participants will develop expertise in incorporating essential data science concepts into their curriculum, including:

Data Exploration & Analysis: Basic summary statistics, exploratory data analysis, and data visualization techniques
Statistical Methods: Inference, modeling, and prediction using real-world datasets
Computing Skills: Working with Jupyter notebooks and various data types
Practical Applications: Connecting data science concepts to diverse academic disciplines

Pedagogical Excellence

The program emphasizes modern, research-backed teaching methodologies:

Active Learning Strategies: Engaging students through hands-on activities and collaborative learning
Backwards Design: Developing courses that start with clear learning outcomes and work backward to create meaningful assessments and activities
Experiential Learning: Creating authentic, real-world learning experiences that connect classroom concepts to practical applications
Authentic Assessment: Moving beyond traditional testing to evaluate student learning through meaningful, real-world tasks
Rubric Development: Creating clear, equitable assessment criteria that support student success

Duration & Format

This program is designed as a 5-week intensive experience featuring weekly 2-hour virtual meetings conducted via Zoom. This flexible online format accommodates the busy schedules of academic professionals while maintaining the benefits of real-time collaboration and discussion. The program utilizes a cohort-based approach that brings together 10 diverse instructors from various disciplines, fostering rich cross-disciplinary exchanges and building a supportive community of practice that extends beyond the program duration.

Weekly Components

Each week combines multiple learning modalities to maximize engagement and practical application. Participants attend interactive Zoom sessions that feature collaborative workshops and peer discussions, allowing for real-time problem-solving and knowledge sharing. These live sessions are complemented by comprehensive Canvas learning modules that provide in-depth coverage of both data science concepts and pedagogical strategies at participants' own pace. Throughout the week, instructors engage in hands-on activities designed to reinforce key concepts through practical application, while simultaneously working on Jupyter notebook development projects that allow them to create customized, discipline-specific materials they can immediately implement in their own courses.

Program Outcomes

Upon completion, participants will:

Have developed custom Jupyter notebooks tailored to their courses
Possess a toolkit of active learning strategies to increase student engagement
Understand how to design courses using backwards design principles
Be equipped with authentic assessment techniques that measure meaningful learning
Have joined a supportive community of educators committed to innovative teaching

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