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Data 8: Foundations of Data Science

Explore UC Berkeley's popular Data 8 course covering inferential thinking, computational thinking, and social issues in data analysis. Emphasizes hands-on work and critical thinking skills for all skill levels. No prerequisites required.

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Data 8: Foundations of Data Science

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  1. UC Berkeley’s Data 8 The Foundations of Data Science: Inferential Thinking, Computational Thinking, and Real-World Relevance 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: inferential thinking, computational thinking, and real-world relevance • Focus on social issues in data analysis • All materials for the course are available for free online under a CC license.

  3. The Data 8 Teaching Philosophy • Represents a shift from traditional teaching each of the individual concepts in a course. • Introductory courses in statistics, computer science, writing, and ethics (among others) combined into a single introductory course.

  4. Data 8 Goals • Diversity • Equity • Pedagogical Clarity • Scalability • Depth • No computational barrier to entry

  5. 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

  6. 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

  7. 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)

  8. Observation and Visualization

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

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

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

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

  13. 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

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

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