[1/2] Celebrating #InternationalWomensDay with great #WomenInSTEM who touched my career:

Vera Rubin: my postdoctoral mentor at DTM @CarnegiePlanets

Neta Bahcall: hired me and guided me at Hubble @stsci

Pat Fraher: my boss's boss, head of Hubble Data Systems Division

[1/2]
[2/2] #InternationalWomensDay — great #WomenInSTEM who touched my career:

Chi Yang: my Department Chair at @GeorgeMasonU

Peggy Agouris: my @GMU_COS College of Science Dean

@AngelaZutavern: VP who hired me at @BoozAllen

@Strategy_Gal: #DataScience Strategic Thinker & Leader
1984 @CarnegieScience-DTM photo
Standing: me, Linda Stryker, and François Schweizer
Seated: Jan Oort en.wikipedia.org/wiki/Jan_Oort and Vera Rubin en.wikipedia.org/wiki/Vera_Rubin

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More from @KirkDBorne

28 Aug 19
Love the simplicity of #Serverless #FaaS (Function-as-a-Service) but hate the setup process? Look to these 7 open source projects to ease #AWS Lambda deployments: dy.si/M3VJd
———
#BigData #StreamingAnalytics #Cloud #DataScience #MachineLearning #IFTTT #EventDriven #AI
13 free tools for #API design, development, & testing — for example, Amazon API Gateway allows you to build front-end APIs for applications built on Amazon EC2, #AWS Lambda, or any web application:
dy.si/1VTf942
————
#microservices #cloud #serverless #FaaS #coding #IoT
#IFTTT alternatives for developers of #EventDriven workflows: dy.si/ZorG2N
————
#IoT #EdgeAnalytics #EdgeComputing #Microservices #DataScience #BigData #FaaS #ML
————
+See the book “AWS Lambda in Action: Event-driven #Serverless Applications” at amzn.to/2YFDSO8
Read 4 tweets
18 Aug 19
Bayesian #MachineLearning Part 1 — Understanding Bayes Theorem from an Analytics Perspective: bit.ly/2KAJaHS #abdsc
—————
#BigData #DataScience #Statistics #StatisticalLiteracy #DataLiteracy #Algorithms
Bayesian #MachineLearning Part 3 — building Bayesian models & inference networks: bit.ly/2O2afGr #abdsc
—————
#BigData #DataScience #Statistics #StatisticalLiteracy #DataLiteracy #Algorithms
—————
+See also these books: amzn.to/30jbwuk
Read 5 tweets

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