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Valuable Outputs from 'Women in Data Science' Event at Stanford

A follow-up report from a recent IT conference at Stanford University tips off Ed Tittel to a wealth of developments stemming from the blossoming involvement of women in the fast-growing data science field.

Women in Data ScienceLast week’s reportage on the Microsoft Learning Born to Learn blog included a simply stellar piece from Carolyn Lesser, Engineering Director for the Learning Experiences (LeX) Platforms team at Microsoft.

 

In a Feb. 15 post titled “Insights from Stanford’s Women in Data Science Conference,” Lesser gives an overview of what went down in Palo Alto (as well as at more than 70 satellite events around the globe). It sounds like a veritable treasure trove of information, strategy, and explanations of work in progress was tendered to a large world-wide audience.

 

I’ll cover some fascinating highlights here, but I urge everybody to consult the afore-linked blog post and visit the WIDS website. Anyone with an interest in data science will want to take full advantage of all the information to be mined there.

 

As Ms. Lesser observes, Data Science is a huge and growing field nowadays, with event attendees coming from backgrounds in engineering, product planning and sales, just at her virtual conference table. Others who participated represent software development, mathematics, and various technical fields as well.

 

As Lesser sees it, the impetus toward data science is simple: “Data is good business sense, and makes us smarter in our decisions.” Microsoft itself has invested heavily in data science over the past few years and, in fact, expanded its Microsoft Professional Program to include Data Science in 2016.

 

To summarize her take on things, Ms. Lesser sees cloud computing as lying at the center of the upswing in data sciences. This applies particularly in the areas of machine learning and artificial intelligence, because of the Cloud’s ability to deliver more and bigger data sets, as well as huge amounts of computing capability, to would-be consumers of such things anywhere, anytime.

 

She explains how this is affecting artificial intelligence, which used to rely on painstakingly analyzed and carefully crafted sets of rules to enable systems to interact with the real world. In an age of machine learning and insane amounts of data, however, along with automated learning algorithms, systems and machines can now teach themselves, and build their own sets of rules, algorithms, and guidelines.