class: center, middle, inverse, title-slide # Teaching Data Science ##
Chapter 16 ### Joshua Rosenberg ### May 12, 2021 --- class: middle # Learning Objectives 1. Pedagogical features embedded within the book 1. Teaching data science 1. Overcoming barriers to doing data science --- class: middle # But, before that ## In response to the question, 'How should I get started doing data science'? ### What would you recommend they do first, second, and third (subsequently) to get started? --- # Pedagogical principles for the book - Problem-based learning - Supporting learning around real-world challenges - The _walkthroughs_ were designed with actual problems edu. data scientists face in mind - Differentiation - Chapters 5 and 6 for those completely new to R - Appendices as 'reaches' - Working in the open - [R View blog post](https://rviews.rstudio.com/2020/07/01/open-source-authorship-of-data-science-in-education-using-r/) - Building mental models - Foundational Skills Framework: Projects, functions, packages, data - Universal design - Inclusive and accessible to all individuals - Did not adequately address (based upon our aim) --- class: middle # What was helpful to you? # What additional (helpful features could be added or created)? --- # Strategies for teaching data science - Provide a home base - Reduce some of the work that learners have to do that is unrelated to reasoning about and using code - Write code early and often - Most everyone can learn to code; begin with reasoning about the output of code and writing code early - Don't touch that keyboard! - Supporting learners to fix typos and become familiar with syntax is well-worth the effort - Anticipate issues (and sacrifice accuracy for clarity early on) - The curse of knowledge is hard to overcome; be _generally_ accurate but emphasize being clear and pragmatic early on - Start with "early wins" - It is important to consider the end point; what will learners _want_ to do or make? - Consider representation and inclusion - Doing data science and working with data in education make these _even more_ important than in other domains in that we can make impressions upon learners about who does data science and what data we create - Draw on other resources - There are great resources in computing education and K-12 education to draw on --- class: middle # What are other helpful teaching strategies? # How do these strategies look different with learners of different ages and in different contexts? --- # What's next? - There is a lot of growth and interest in data science education - But, learning to do data science is still _really hard_! - **What are big gaps in resources?** --- # Thank you! Joshua Rosenberg jmrosenberg@utk.edu @jrosenberg6432