This document summarizes adopting the Data8 introductory data science course at two-year colleges. Data8 was designed to be accessible to a broad range of students without typical prerequisites. It combines inferential thinking, computational thinking, and a focus on social issues. The goals are diversity, equity, pedagogical clarity, scalability, and depth without computational barriers. Core concepts include critical thinking, experimentation, and understanding data limitations and uncertainty. The course uses Jupyter notebooks and Python and focuses on hands-on work and understanding concepts rather than package details.

1. Adopting Data8 at a Two-year
College
Presented by: Ava Meredith, Seattle Central College

2. What is Data 8?
● Data 8 is a popular introductory Data Science class at UC Berkeley
● Designed to be accessible to a broad range of students without
the typical prerequisites for a data science class
● Data 8's unique model combines inferential thinking, computatianl
thinking, and focus on social issues into a single, introductory
course
● All materials for the course are available for free online under a CC
license.

3. Data 8 Goals
● Diversity
● Equity
● Pedagogical Clarity
● Scalability
● Depth
● No computational barrier to entry

4. Core Concepts
● Critical thinking
● Don't take your data for granted
● Use the combination of CS + Stats as a feature, not a bug
● Focus on hands on work
● Determine if your inference is sound
● Experiment
● Know the right statistical tools for the job

5. ● Learn about data limitations
● Quantify and understand uncertainty in data
● Turn your data analysis into a decision
● Think of ways that you could be wrong
● Consider edge-cases

6. ● Focus on main ideas (shield the students from non essential
topics)
● Use the data science module rather than many package APIs
● Use JupyterHub (no need for students to setup environment)

7. Observation and Visualization

8. ● Abstract cleaning data by providing pre-collected/cleaned data
● Provide further resources
● Aim the course for anybody, not just statistics or CS majors.

9. Intersections of Topics
● Intersectionality is a feature, not a bug
● Connect CS and statistics concepts
● Use interactivity to let people explore

10. Topics covered
● Programming fundamentals
● Statistics, sampling, and hypothesis testing
● Inference, prediction, and models
● Comparing distributions

11. Connector courses
Connector courses offer the ways in which data science is applied in a
domain knowledge field

12. Tech Stack
● Managing course content - Jupyter notebooks
● Programming language - Python 3
● Primary data object and functions - Use of data analytics
packages in Python (Data 8 wraps several)
● Handling the Python environment - Python dev environment
managed with miniconda

13. Next Steps
View the course online http://data8.org/
Free online textbook: https://www.inferentialthinking.com/chapters/intro
Data Science Academic Resource Kit: https://data.berkeley.edu/education/ark