In r4ds/bookclub-dsieur: Data Science in Education Using R Book Club

Teaching Data Science

Learning objectives:

• Pedagogical features embedded within the book
• Teaching data science
• Overcoming barriers to doing data science

Slides

r knitr::include_url("./R/2021-05-12/walkthrough_6.html")

Meeting Videos

Cohort 1

r knitr::include_url("https://www.youtube.com/embed/kvErHKVvQTI")

Meeting chat log

00:06:12    Joshua Rosenberg:   Hi everyone
00:06:16    Joshua Rosenberg:   Just getting setup here
00:39:06    Joshua Rosenberg:   Ty Ryan - glad to hear this.
00:39:27    Joshua Rosenberg:   (Also welcome critique/additional features suggestions, of course)
00:47:59    Isabella Velásquez: yes, and dplyr 1.0! and a bunc of other updates :D
00:55:23    Joshua Rosenberg:   If y'all want to take on just one of these two, that's fine - being aware of time here.
00:57:41    Joshua Rosenberg:   Great suggestions from you both, I think

Josh's Notes

# Discussion of Teaching Data Science

## ways to get started

- ID a problem/thing you want to do

- learn a bit about possible functionality

    - read a resource like r4ds

- work on a particular problem

- start with a narrow resource, like a chapter or a blog post

- solicit feedback

- find out what area/aspect of data science is most interesting to you, e.g. sports

- twitter (#rstats!) can be a great way to pick up on the conversation/follow people who do data science

- select a topic that can sustain motivation/interest early on

- building some understanding about where/what data is

- what is someone's vision/goals for becoming more data savvy

- what are ways you're using analytics in your life?

    - fitbit! (wearables/fitness trackers)

    - social media analytics?

    - tracking personal expenses?

    - following sports?

    - ...?

- send examples that are similar in purpose/contents/context to what someone is hoping to do

## pedagogical principles embedded in book

- walkthroughs helpful for intermediate R user

- Could pauses/chances for readers to engage in active thinking/application, instead of spoon feeding the next step be created?

    - could the bookdown for dsieur-bookclub be a place such walkthroughs could "live"?

- education is different/particular 

- seeing similar data to data that one encounters in one's work has some benefits (relative to e.g. business data)

- the topics are applicable to education

- something that could be mentioned - data stored in forms other than CSVs/flat text files

    - databases/AWS - through dplyr/dbplyr

    - RStudio connections tab (aside, I [Josh] could never get this to work, but was able to use dbplyr fine)

- so many things that we don't come back to in the text; there are so many features that RStudio is currently working on

    - for beginning R users

    - for continuity / expansion of the topic throughout the book

- walkthroughs focus on accessing, preparing, creating products from analyses

- what about the human aspects of data science?

## teaching strategies

- teaching PhD students who have mostly used SPSS, being very contextualized/focusing on a relevant data set can help

- visualization is a great place to start

- __instead of__ arguing for how access and R (or python or SPSS, etc.) are different, focus on showing how one can do things that one could do in other statistical software

    - make the transition less abrupt

- learn something, step away, and then forget! It would be good to have a systematic way to revisit concepts, like through flashcards - spaced practice, memory, other strategies; easy to let things to too long

## resources

- **meta-analysis** or review of when to do what

    - what strategies are better for teaching what coding/DS concepts and skills?

    - blocks work really well for certain skills/ideas, but not so well for others

- gamification or robotics are common contexts for learning coding/CS

    - but, there aren't resources like this for data science (and there aren't many examples of teaching data science outside of graduate-level courses)

        - reframe CS activities around data science; could support students moving into data science and machine learning roles (and data-intensive roles in a variety of occupations)

- data/data science can be accessible to anyone

    - personal

    - in various jobs - not just stem jobs

    - breaking down misconceptions about what data scientists do/who they are could be a step that could make progress 

r4ds/bookclub-dsieur documentation built on May 20, 2022, 6:24 p.m.