Women have carved a niche for themselves in almost every field one could possibly think of. Business, finance, marketing, IT, law – you name it and you’ll find women doing a great job without any difficulty. However, data science is that one field that’s still a little unexplored by women. And considering the potential that women have, this field can see remarkable changes in the days ahead.

What’s been observed so far is that the data science industry is men-dominated and the number of females who’ve made a career in this field is relatively less. Almost all the data scientists out there are men and this itself raises questions as to why is that so when the skills required to become a data scientist are gender-neutral. Be it critical thinking, structured approach, creativity, intuition, analytical ability, problem-solving techniques, or any similar skill for that matter – all these have nothing to do with whether the person is a male or a female. Women too stand the potential to inculcate all of this and emerge out to be extremely credible data scientists. Then, why aren’t the number of women data scientists shooting up?

The fact that data science as a career isn’t something that many people are aware of which is why the number of data scientists across the globe is very less. Making people aware of the same will not only open the door of opportunities for people all around but also bring in more women to the field.

No wonder, there’s a staggering difference between the number of men and women in the field. A number of factors have contributed to this like the workplace culture, the level of confidence, lack of interest, no early exposure and inherent bias. Now is the time that women are made to understand what are the opportunities available for them, what will those opportunities involve, and what the quality of life looks like for someone in this role. Women have the potential to cater to business needs and with adequate knowledge of the same can help them achieve results like never before. Additionally, being exposed to the required skills well in advance makes it easy for them to achieve the desired objective.

Yes, there are many qualified women out there who fit the eligibility criteria just right but the hurdles don’t seem to stop. Gender diversity could be unbalanced based on the way an organization recruits for a position. Also, the fact that most of the recruitments rely on referrals to determine the top candidates cannot be overlooked. No wonder, it has been observed that teams with more or less equal number of men and women are more likely to be creative, share knowledge and finish tasks faster and efficiently when compared to teams of unequal genders.

However, a point worth noting is that closing the gender gap in this field of data science shouldn’t only be about reaching a certain ratio of women to men within the workplace. Even though recruitment is still an issue, the focus should be more about empowering women. It is now time that we change how the system functions with fewer women followed by steps such as informing women interested in or entering this field that skill proficiency isn’t based on gender.

With that being said, need of the hour is to bring awareness about this current situation, highlight the growth opportunities available, and bring the benefits of gender diversity to light. Well, better late than never!!

The post THE IMPORTANCE OF WOMEN IN DATA SCIENCE appeared first on Artificial Intelligence.

For the fifth year in a row, Harvard, MIT, Microsoft Research New England, and now the Broad Institute, are proud to collaborate with Stanford University to bring the Women in Data Science (WiDS) conference to Cambridge, Massachusetts.

This virtual, one-day technical conference will feature an all-female lineup of speakers from academia and industry, to talk about the latest data science-related research in a number of domains, to learn how leading-edge companies are leveraging data science for success, and to connect with potential mentors, collaborators, and others in the field.

WiDS Cambridge is an independent event that is organized by Harvard, MIT, Microsoft Research New England, and the Broad Institute, as part of the annual WiDS Worldwide conference organized by Stanford University and an estimated 150+ locations worldwide, which features outstanding women doing outstanding work in the field of data science. All genders are invited to attend all WiDS Worldwide conference events.

The post Women in Data Science Conference – 3/11 appeared first on Artificial Intelligence.

In celebration of International Women’s Day, women data scientists from around the world will gather together virtually on March 7 and 8, 2021, for presentations, workshops and networking at the Women in Data Science (WiDS) Worldwide Conference. The event offers women in data science, a chance to recognize their social, economic, cultural and political achievements.

“Data science is the new gold or the new oil,” WiDS co-founder Dr. Margot Gerritsen said. “Data-driven decision making is in all aspects of our lives. It’s important that all sorts of genders, cultures and backgrounds are represented in this development.”

Like many women working in fields still dominated by men, Dr. Margot Gerritsen remembers the tipping point that inspired her to co-found WiDS with Karen Matthys. Invited to attend a conference by a male colleague, she noted that the event featured no women keynote speakers. Her colleague offered a familiar explanation: There were so few women in the field that he couldn’t find one to present at the event.

Less than a year later, the first WiDS Worldwide Conference proved him wrong: Over 6,000 people from around the world joined the conference livestream. By 2020, over 30,000 participants in more than 50 countries tuned in.

According to conference organizer Judy Logan, the WiDS Worldwide Conference has been global since its start. The initial conference livestream’s popularity showed co-directors Gerritsen, Logan and Matthys the international demand for collaboration and networking among women data scientists around the world.

The virtual nature of the conference allowed women to connect with one another across borders without the elitist registration and travel costs of other conventions. The WiDS Worldwide Conference encourages women in data science to establish their own national and regional networks while participating in a broader global conversation. As advocates recognize, data science needs diverse perspectives to determine the future of data-driven decision making in a world where data collection and analysis are increasingly (and unevenly) penetrating every aspect of peoples’ lives.

The opening presentations for this year’s conference include a fireside chat with Nobel Laureate and physicist Andrea Ghez—only the fourth woman to win the Nobel Prize for Physics—and a presentation by Emily Fox, distinguished engineer (Apple) and professor (University of Washington) on health and machine learning. Additional presentations center the work and research of data scientists working around the world.

For example, Dr. Kalinda Griffiths, a scientist at the Centre for Big Data Research in Health at the University of New South Wales, will discuss her research addressing health disparities among indigenous peoples, including how they have been defined and how identifications are operationalized in government data collections.

Dr. Fatima Abu Salem, associate professor of computer science at the American University of Beirut (AUB), will discuss “Doing Data Science in Data Deserts,” a presentation that looks at how grassroots initiatives are facilitating data collection efforts in countries where data is less abundant and accessible.

This year’s conference features the fourth incarnation of WiDS datathon, an event in which participants are given a dataset and a problem for which they must use their ingenuity and data science skills to explore solutions. This year’s datathon reflects WIDS’ commitment to solving problems with global ramifications: Datathon participants have been asked to create models to determine whether patients have been diagnosed with a certain type of diabetes that might improve ICU treatment.

The WiDS Worldwide Conference is open to all interested in ensuring that the future of data science is diverse and inclusive. Information about registration fees and platform can be found on the WIDS website.

The post Women in Data Science Initiative Holds Global Conference to Celebrate International Women’s Day appeared first on Artificial Intelligence.

Women in Data Science (WiDS) will host its fourth WiDS Livermore regional event “to encourage our community of women in computing.” Attendees will watch WiDS Stanford Livestream as well as feature Lab-focused technical talks, mentoring breakout sessions and a career panel.  The program for this one-day virtual technical conference is an opportunity to hear about the latest data science-related research and applications in a number of domains, and to connect with others in the field. 

WiDS Livermore is an independent event organized by Lawrence Livermore National Laboratory (LLNL) Ambassadors as part of the annual Women in Data Science (WiDS) Worldwide conference organized by Stanford University at an estimated 150+ locations worldwideWiDS said ll genders are invited to attend all WiDS Worldwide events.

The Women in HPC (WHPC) Mentoring Programme connects women from around the world with mentorship and support that will help them to achieve their full potential as HPC professionals. This will primarily be a professionals’ network. However, where appropriate the mentoring program will be open to students as well (for example when the host organization does not provide an appropriate scheme or HPC related mentor).

The WHPC Mentoring Programme is accepting applications for the next cohort, running during 1st March–30th June 2021. See below for more information and the application form.

The WHPC mentoring program will be open three times a year and will last four months. During the prescribed mentoring program, both mentors and mentees will receive training on how to make the most of mentoring, as well as the opportunity to participate in webinars on key skills. Mentoring will be provided one-to-one with mentors and mentees from different organizations and career-levels. Each formal mentor-mentee engagement session lasts four months. WiDS accepts mentor/mentee applications year long and we will pool you into the next cohort.

The post Women in Data Science to Host March 8 Virtual Regional Event at Livermore appeared first on Artificial Intelligence.

Walmart can tell you how many of anything are in a given store or warehouse at any moment. Apps track your heartrate and your phone tracks your location at any moment. C-suite executives monitor everything in their organizations daily. Big Data dominates our economy. Yet, we don’t have consistent, standardized and real-time data on the jobs driving that 21^st century-Big Data economy: science, technology, engineering and math (STEM). Especially for women.  In the labyrinth of sources, the government’s Bureau of Labor Statistics (BLS) data seems to be the most detailed, but it’s relative; it’s not even clear exactly which jobs they include.

“Where data comes in is to put greater pressure on educational institutions and on employers to monitor what they’re doing and be held accountable if they lose women, if they keep losing women, or keep not getting women in the first place,” Ariane Hegewisch, Ph.D., Program Director Employment & Earnings at the Institute for Women’s Policy Research, told me. She added that it’s important to see the racial data as well, because, “it does impact women of different racial and ethnic backgrounds very differently.”

The devil’s in the definitions: “There is no standard definition of a STEM occupation.”

A big part of the problem is defining these jobs. The BLS lists all occupations and you need to mine their breakdown to find what you want. The BLS defines STEM jobs as: “Science, technology, engineering, and math (STEM) occupations include computer and mathematical, architecture and engineering, and life and physical science occupations, as well as managerial and postsecondary teaching occupations related to these functional areas and sales occupations requiring scientific or technical knowledge at the postsecondary level.”

Using this definition, women made slight gains in computer science jobs in 2020, from 24.9% overall in Q1-2020 to 25.1% by Q4-2020, based on unpublished data the BLS sent me. A BLS spokesperson wrote me that unpublished data, “may contain estimates that do not meet CPS publication standards,” for quantity of people in a given job. Roberta Rincon, Ph.D, Senior Manager of Research at the Society of Women Engineers (SWE), told me it could also be because the BLS uses sampling surveys, so it’s not counting every job.

Rincon explained that different sources use different definitions. For example, she said, “are you going to count engineers as people who graduated with engineering degrees, or are you counting them because their job title is ‘engineer’?” Then there are people who work as engineers but don’t have either a degree or such a title.

The largest organization for women in technology, Anita B.org, did their own study on “women technologists” in late March/early April 2020, before the pandemic really hit, and got different data than the BLS for comparable jobs in Q1-2020. Anita B.org defines “women technologists” as: “technical jobs in computing and information technology, including technical management and technical leadership. We do not include scientific positions in this that are unrelated to computing and information technology (ex: civil engineer, biologists, etc.).”

Catalyst, the 59-year-old nonprofit focused on advancing women in the workforce that compiles a wide range of data from around the globe, defines STEM this way: “’STEM’ refers to the fields of science, technology, engineering, and mathematics. There is no standard definition of a STEM occupation… [for them] STEM incorporates professional and technical support occupations in the areas of life and physical sciences, computer science and mathematics, and engineering. Less agreement has been made on the inclusion of educators, healthcare professionals, and social scientists in STEM; therefore, these occupations are not covered here.⁵³

STEM drives the 21^st century economy, so here’s what we need to measure these jobs:

Any business leader will tell you that investment and accountability follow measurement.

Jobs data is reported every month, including demographic data, which is how we know that all the jobs lost in December 2020 were held by women and all the job gains then were made by men.  Many companies collect diversity and jobs data and review it internally, but don’t want to release it publicly, “because it doesn’t always make them look very good,” as Rincon put it. But, she added, different sources collect and report data on gender differently, and others lack the resources or incentives to either collect or report it.

“So, unless you have an organization like the government who is telling them, ‘this is what we want you to report and this is how we want you to report it and this is how we’re defining it,’ then you have apples and oranges in some cases,” SWE’s Rincon explained.

Therefore, since STEM jobs drive the 21^st century economy, and leaders need to be accountable for diversity, we need:

(a) A standard definition of STEM jobs, to include educators, healthcare professionals, and people who work in related roles, such as Chief Sustainability Officers, or in communications and finance roles in STEM fields, such as in electric vehicles, energy companies, or vaccine development;

(b) Tracking systems befitting the 21^st economy that provide accurate, consistent data for these roles on a timely basis; and

(c) An organization like the government demanding this data, and providing standards for it and for reporting it.

These systems also need to be adaptable as the economy evolves. For example, new roles are being created across the economy as climate change, diversity and related initiatives, such as environment-social-governance (ESG) investing, take center stage in the 21^st century economy.

If we want to hold business leaders and policymakers accountable for advancing women in STEM jobs – and to train our workforce according to market needs – we need consistent, timely data. Big data needs to step up.

The post Forbes: Why Big Data Is Failing Women In STEM And How To Fix It appeared first on Artificial Intelligence.

Indian FinTech ZestMoney, an AI-driven EMI financing platform, has witnessed some interesting trends during the three-month lockdown triggered by the COVID19 pandemic. The Bengaluru-based company said that it has seen a spike in Education loans on its platform. Founded by Lizzie Chapman, Priya Sharma and Ashish Anantharaman in 2015, ZestMoney is one of the fastest-growing consumer lending fintech companies in India.

While online learning has been gaining popularity even otherwise, the nationwide lockdown due to COVID-19 has accelerated the demand like never before. With more free time available, people are increasingly embracing professional courses to upskill and stay relevant in their careers, especially at a time when the uncertain economic outlook has impacted jobs across sectors.  Edtech companies have come to their rescue, making available a plethora of options accessible to consumers from the comfort of their homes, the company said in a release.

Courses in Data Science, Machine Learning, Software Engineering, Management, Business Intelligence, and Visualization emerged as popular options. Full Stack development and Backend Engineering were also highly sought after programs on the platform.

Lizzie Chapman, Co-founder, and CEO of ZestMoney said, “Affordability now being a critical factor when it comes to credit, the demand for EMI financing options is at an all-time high. Even in these uncertain times, we have been able to cater to the demand with our early bets on Artificial Intelligence and data mining giving us a definite edge. Our specialized model has been able to predict consumer shift, making it a low-risk business for us. The 100% increase in Edtech players on our platform is a validation that our partners also see that value in us.  We expect this demand to continue.”

Chapman further added that ZestMoney foresees an increased adoption of digital financing in the coming months as the fintech industry continues to play a crucial role in reviving the economy by offering reliable and transparent access to credit. “The early signs look quite encouraging,” Chapman said.

Besides, ZestMoney also saw people with high CIBIL scores (650+) applying for EMI loans, signaling a demand even from premium customers. Demand from smaller towns was also high with over 57% of the applicants for upskilling programs were from Tier-3 markets. Cities like Wayanad, Vellore, Udaipur, Tawang, Uttarkashi especially witnessed an increase in applications. There was a 30% surge in female applicants.

As consumers look for reliable access to credit, ZestMoney has seen a 100% increase in the number of Edtech players on their platform. The company has been able to emerge as the preferred choice for partners by offering fully automated approvals which are some of the fastest and highest in the industry for this category.

The post ZestMoney sees huge spike in Edtech loans, women applicants during lockdown appeared first on Artificial Intelligence.

It’s no secret that the representation of women in the technology workforce is lower than it should be.

For predictive analytics professionals, a Burch Works study showed that women comprised 26% of the workforce in 2019, an increase over the previous year of only 2%.

Microsoft Corp. is taking its own steps to change that. Through active participation in major industry gatherings, such as WiDS 2020, and paying close attention to the hiring process, the company is looking to change workforce percentages.

“We make sure that we have women on every set of interviews,” said John Hoegger (pictured), principal data science manager at Microsoft. “What’s it like to be a woman on this team? If it’s all men, you can’t answer that question. I’ve now got a team of 30 data scientists and half of them are women.”

Hoegger spoke with Sonia Tagare, host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, during the Women in Data Science conference in Stanford, California. They discussed the growth of WiDS over the past four years and advice for women seeking to join companies as data scientists.

From conference to a movement

When the Microsoft manager discovered that the WiDS event had only one sponsor — WalMart Labs — in its inaugural year, he quickly decided that his company should become a supporter too. The organization has since expanded its portfolio of global events to include “datathons” and the development of role models for women data scientists.

“It’s amazing to see how this event has grown over the four years,” Hoegger said. “There’s all of these new regional events that have been set up every year. It’s turned from just a conference into a movement.”

Microsoft has hosted various WiDS events at its headquarters in Redmond, Washington, along with New York and Boston, according to Hoegger. Asked about what advice he would offer for women seeking positions in the data science field, Hoegger encouraged an open approach that would provide candidates with useful insight into the company culture.

“Go to those interviews and ask what it’s like to be a woman on the team,” Hoegger said. “You want to ensure that the team you join and company you join are inclusive and really value diversity in the workforce.”

Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s coverage of the Women in Data Science conference.

The post Diversity in hiring process helps Microsoft fuel opportunity for women data scientists appeared first on Artificial Intelligence.

Accenture has announced the return of its award-winning Women in Data Science Accelerator for the second year. The six-week evening programme is free to attend and combines industry-led lectures, hands-on workshops and career guidance to help women become successful leaders in data science.

The aim of this programme is to build and develop the data science capabilities of women who already hold applied data skills or have broader numerical experience and are interested in the field. Participants will get the opportunity to network with other women pursuing data science careers and learn from Accenture experts in the field. Accenture STEM research published in December 2019 highlighted that 85% of girls in school are very inspired by role models, reinforcing the importance of women working in STEM careers, such as data science, that younger girls can look up to.

What’s Involved?

30 women will be selected to participate in the six-week programme hosted at The Dock, Accenture’s flagship R&D and Global Innovation Centre in Dublin’s Silicon Docks. The curriculum consists of a blend of modules that will help participants build their data science skillsets and further their professional development. There will also be a collaborative practical project throughout the six weeks.

Sessions will run for two hours, one evening per week in a classroom environment, with an additional 2-4 hours weekly focused on the practical project in the participant’s own time. The programme will provide a unique insight into the day-to-day work of in the field of data science while at the same time offering great networking opportunities with Accenture’s experienced data scientists and digital leaders.

Commenting on the launch of the 2020 programme, Oonagh O’Shea, Women in Data Science Programme MD Sponsor, said: “The accelerator is a great opportunity for participants to upskill, collaborate and network with fellow and aspiring data scientists to help further their careers. I am deeply proud of the success of the programme last year. The commitment of our people to develop this resulted in Accenture winning the Analytics Institute’s 2019 ‘Diversity in Analytics Award’. The feedback from last year’s participants has been really overwhelming so I am delighted that we are running the programme again this year.”

Following the inaugural six-week accelerator in 2019, 82% of participants felt more equipped to make a career move into data science after the programme, while 100% of participants would recommend it to other women.

To find out more and to register interest, visit www.accenture.com/WomenInData. The closing date for expressions of interest is Friday, 31st January 2020. Participants will be notified of their acceptance into the programme by 12th February 2020. It will begin on Wednesday 19th February 2020 and run for six weeks, until Wednesday 25th March 2020.

In 2019, Accenture in Ireland was awarded ‘Diverse Company of The Year’ at the annual Women in Tech Awards and ‘Employer of the Year’ at the Women in IT Awards.

The post APPLICATIONS OPEN FOR ACCENTURE’S WOMEN IN DATA SCIENCE ACCELERATOR PROGRAMME appeared first on Artificial Intelligence.

June 12, 2019 – Using deep learning technology, researchers from Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital (MGH) were able to predict women’s future risk of breast cancer development more accurately than when they used traditional methods, according to a study published in Radiology.

Current models use factors like genetics and family history to predict risk, but these tools often fall short. Breast density is an independent risk factor for predicting breast cancer risk, but it’s based on subjective assessment that can vary among radiologists.

Researchers developed a deep learning model that could standardize and automate breast density measurements.

“There’s much more information in a mammogram than just the four categories of breast density,” said study lead author Adam Yala, PhD candidate at MIT in Cambridge, Mass. “By using the deep learning model, we learn subtle cues that are indicative of future cancer.”

The team compared three different risk assessment models. The first used traditional risk factors, and the second used deep learning that evaluated the mammogram alone. The third was a hybrid method that used both the mammogram and traditional risk factors into the deep learning model.

Researchers trained and tested the models on nearly 90,000 screening mammograms from about 40,000 women and found that both deep learning models performed with greater accuracy than the traditional model.

When using the deep learning models to predict women’s risk based on breast density, the team found that patients with non-dense breasts and model-assessed high risk had 3.9 times the cancer incidence of patients with dense breasts and model-assessed low risk. These advantages held across different subgroups of women.

“Unlike traditional models, our deep learning model performs equally well across diverse races, ages and family histories,” said Regina Barzilay, PhD, an AI expert and professor at MIT. “Until now, African-American women were at a distinct disadvantage in having accurate risk assessment of future breast cancer. Our AI model has changed that.”

At MGH, clinicians are already using artificial intelligence to assist with breast density measurements. Researchers are tracking its performance in the clinic and working to refine how they communicate risk information to women and their primary care physicians.

“A missing element to support more effective, more personalized screening programs has been risk assessment tools that are easy to implement and that work across the full diversity of women whom we serve,” said Constance Lehman, MD, PhD, chief of breast imaging at MGH and professor of radiology at Harvard Medical School.

“We are thrilled with our results and eager to work closely with our health care systems, our providers and, most importantly, our patients to incorporate this discovery into improved outcomes for all women.”

Deep learning has proven itself to be a reliable support tool for cancer care. In 2018, a team at Google developed a deep learning tool that could detect metastasized breast cancer with 99 percent accuracy.

Researchers at Case Western Reserve University also built a model that achieved 100 percent accuracy when identifying invasive forms of breast cancer in pathology images.

“If the network can tell which patients have cancer and which do not, this technology can serve as triage for the pathologist, freeing their time to concentrate on the cancer patients,” Anant Madabushi, a biomedical engineering professor at Case Western Reserve and co-author of the study, said at the time.

“To put this in perspective, the machine could do the analysis during ‘off hours,’ possibly running the analysis during the night and providing the results ready for review by the pathologist when she/he were to come into the office in the morning.”

The research from MGH and MIT builds on these efforts, and further shows the potential for deep learning to transform cancer care and diagnosis.

“There’s a very large amount of information in a full-resolution mammogram that breast cancer risk models have not been able to use until recently,” Yala said. “Using deep learning, we can learn to leverage that information directly from the data and create models that are significantly more accurate across diverse populations.”

The post Deep Learning Predicts Women’s Future Risk of Breast Cancer appeared first on Artificial Intelligence.