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23 Apr, 3 tweets, 1 min read
I learned a lot from #DataFeminism, but one of my favorite take-aways is the term "Big D!ck Data." From Ch. 6: data-feminism.mitpress.mit.edu/pub/czq9dfs5/r…
BDD "is a formal, academic term ... to denote big data projects that are characterized by patriarchal, cis-masculinist, totalizing fantasies of world domination as enacted through data capture and analysis."
"Big D!ck Data projects ignore context, fetishize size, and inflate their technical and scientific capabilities."

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More from @WomenInStat

Women in Statistics and Data Science Profile picture
23 Apr
The #DataFeminism book also made me look inward and examine my own biases, which I am exceedingly grateful for.

Namely, it forced me to reckon with some of my fundamental operating assumptions as a statistician & data scientist.

Examples threaded below...
In chapter 3, the authors discuss the role of emotion in data visualization, specifically calling out giants in the field like Edward Tufte and Alberto Cairo (no snitch tagging, please) for what is presented as an anti-emotion stance.
On Tufte: "Any ink devoted to something other than the data themselves ... is a suspect and intruder to the graphic. Visual minimalism, according to this logic, appeals to reason first. ... Decorative elements ... are associated with messy feelings ... and emotional persuasion."
Read 12 tweets
Women in Statistics and Data Science Profile picture
23 Apr
There are 7 core principles of #DataFeminism:

1. Examine Power
2. Challenge Power
3. Elevate emotion and embodiment
4. Rethink binaries and hierarchies
5. Embrace Pluralism
6. Consider Context
7. Make labor visible
Principle 1: Examine Power

"#DataFeminism begins by analyzing how power operates in the world."

data-feminism.mitpress.mit.edu/pub/vi8obxh7/r…
Principle 2: Challenge Power

"#DataFeminism commits to challenging unequal power structures and working toward justice."

data-feminism.mitpress.mit.edu/pub/ei7cogfn/r…
Read 8 tweets
Women in Statistics and Data Science Profile picture
22 Apr
Good morning! Happy Thursday!

For #ThrowbackThursday I thought I'd highlight some of the amazing women who have been mentors (and friends) to me. Without support from an amazing community of women in mathematics & statistics I would not be where I am today! #WomenInSTEM
(These will be in chronological order)
.@lpudwell : Lara Pudwell

Lara was my advisor during my summer REU experience at @ValpoU in 2011.

Without her mentorship, I don't think I would have ever considered graduate school!
Read 7 tweets
Women in Statistics and Data Science Profile picture
24 Mar
Let's talk data visualizations today! Best practices, ideas, tools, resources or even some really neat visualizations - what are your recommendations?
I found this visualization of at-risk workers in COVID times very good at expressing key points, though I did not like the scroll feature too much!

nytimes.com/interactive/20…
Quite unlike the wealth disparity visualization where the scrolling was on point made all the difference:

mkorostoff.github.io/1-pixel-wealth/
Read 4 tweets
Women in Statistics and Data Science Profile picture
23 Mar
As we practice and teach Data Science, we continuously learn, unlearn and revise old and new concepts.
What are some freely available reading lists that give that help this or give a great intro to Data Science?

(1/n)
This one is from University of Washington and goes over some basic concepts: students.washington.edu/bxie/info370/

(2/n)
Another great one which details specific vital segments like clustering and dimensionality is this book/course from University of Utah: cs.utah.edu/~jeffp/teachin…

(3/n)
Read 8 tweets
Women in Statistics and Data Science Profile picture
22 Mar
For some #MondayMotivation, let's create a great resource of fellowships, workshops and communities in Data Science.

I'll start with some!
(1/n)
The Women in Data Science Conference (widsconference.org) is a great place to learn, network and grow.

2/n
The ACM SIGHPC Computational & Data Science Fellowships(sighpc.org/fellowships), with an upcoming deadline fosters diversity in Data Science and allied fields.

3/n
Read 9 tweets

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