Summary of Data Feminism
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Qualities
- Analytical
- Eye Opening
- Concrete Examples
While the rating tells you how good a book is according to our two core criteria, it says nothing about its particular defining features. Therefore, we use a set of 20 qualities to characterize each book by its strengths:
Applicable – You’ll get advice that can be directly applied in the workplace or in everyday situations.
Analytical – You’ll understand the inner workings of the subject matter.
Background – You’ll get contextual knowledge as a frame for informed action or analysis.
Bold – You’ll find arguments that may break with predominant views.
Comprehensive – You’ll find every aspect of the subject matter covered.
Concrete Examples – You’ll get practical advice illustrated with examples of real-world applications or anecdotes.
Controversial – You’ll be confronted with strongly debated opinions.
Eloquent – You’ll enjoy a masterfully written or presented text.
Engaging – You’ll read or watch this all the way through the end.
Eye opening – You’ll be offered highly surprising insights.
For beginners – You’ll find this to be a good primer if you’re a learner with little or no prior experience/knowledge.
For experts – You’ll get the higher-level knowledge/instructions you need as an expert.
Hot Topic – You’ll find yourself in the middle of a highly debated issue.
Innovative – You can expect some truly fresh ideas and insights on brand-new products or trends.
Insider’s take – You’ll have the privilege of learning from someone who knows her or his topic inside-out.
Inspiring – You’ll want to put into practice what you’ve read immediately.
Overview – You’ll get a broad treatment of the subject matter, mentioning all its major aspects.
Scientific – You’ll get facts and figures grounded in scientific research.
Visionary – You’ll get a glimpse of the future and what it might mean for you.
Well structured – You’ll find this to be particularly well organized to support its reception or application.
Recommendation
What gets left out of datasets can be interesting and as telling as what gets left in. Catherine D’Ignazio and Lauren F. Klein ask: Who decides? They illustrate their “seven principles of data feminism” through campaigns and creative projects. Though aimed at data scientists, “data visualizers” and anyone relying on data to make their arguments – which means everyone – will find much to ponder. Considering the source has never mattered more.
About the Authors
Catherine D’Ignazio teaches Urban Science and Planning at MIT. Co-author Lauren F. Klein teaches Quantitative Theory and Methods at Emory University.
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