Diversity in computing

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Diversity_in_computing

Diversity in computing refers to the representation and inclusion of underrepresented groups, such as women, people of color, individuals with disabilities, and LGBTQ+ individuals, in the field of computing. The computing sector, like other STEM fields, lacks diversity in the United States.

Despite women constituting around half of the U.S. population they still are not properly represented in the computing sector. Racial minorities, such as African Americans, Hispanics, and American Indians or Alaska Natives, also remain significantly underrepresented in the computing sector.

Two issues that cause the lack of diversity are:

1. Pipeline: the lack of early access to resources
2. Culture: exclusivity and discrimination in the workplace

The lack of diversity can also be attributed to limited early exposure to resources, as students who do not already have computer skills upon entering college are at a disadvantage in computing majors. There is also the issue of discrimination and harassment faced in the workplace which affects all underrepresented groups. For example, studies have shown that 50% of women reported experiencing sexual harassment in tech companies.

As technology is becoming omnipresent, diversity in the tech field could help institutions reduce inequalities in society. To make the field more diverse, organizations need to address both issues. There are multiple organizations and initiatives which are working towards increasing diversity in computing by providing resources, mentorship, support, and fostering a sense of belonging for minority groups such as EarSketch, Black Girls Code, and ColorStack. Institutions are also implementing strategies such as Summer Bridge programs, tutoring, academic advising, financial support, and curriculum reform to support diversity in STEM. Along with Institutions Educators can help cultivate a sense of confidence in underrepresented students interested in pursuing computing, such as emphasizing a growth mindset, rejecting the idea that some individuals have innate talent, and establishing inclusive learning environments.

Statistics

In 2019, women represented 50.8% of the total population of the United States, but made up only 25.6% of computer and mathematical occupations and 27% of computer and information systems manager occupations. African Americans represented 13.4% of the population, but held 8.4% of computer and mathematical occupations. Hispanic or Latino people made up 18.3% of the population, but constituted only 7.5% of the people in these jobs. Meanwhile, white people, standing at 60.4%-76.5% of the population of the United States, represented 67% of computer and mathematical occupations and 77% of computer and information systems manager occupations. Asians, representing 5.9% of the population, held 22% of computer and mathematical jobs and were 14.3% of all computer and information systems managers.

In 2021, women made up 51% of the total population aged 18 to 74 years old, yet only accounted for 35% of STEM occupations. Additionally, while individuals with disabilities made up 9% of the population, they accounted for 3% of STEM occupations. Hispanics, Blacks, and American Indians or Alaska Natives collectively only accounted for 24% of STEM occupations in 2021 while making up 31% of the total population.

In addition to occupational disparities, there are differences in representation in postsecondary science and engineering education. Women earning associate's or bachelor's degrees in science and engineering accounted for approximately half of the total number of degrees in 2020, which was proportional to their share of the population for the age range of 18 – 34 years. In contrast, women only accounted for 46% of science and engineering master's degrees and 41% of science and engineering doctoral degrees. Hispanics, Blacks, and American Indians or Alaska Natives as a group face a similar gap between their share of the population and proportion of degrees earned, with them collectively making up 37% of the college age population in 2021, yet only 26% of bachelor's degrees in science and engineering, 24% of master's degrees in science and engineering, and 16% of doctoral degrees in science and engineering awarded in 2020. On top of the degree gap, data indicates that only 38% of women who major in computer science actually end up working in the computer science field, in contrast to 53% of men.

A 2021 report indicates that approximately 57% of women working in tech responded that have experienced gender discrimination in the workplace in contrast with men, where approximately only 10% reported experiencing gender discrimination. Additionally, 48% of women reported experiencing discrimination over their technical abilities in contrast with only 24% of men reporting the same discrimination. The report also found that 48% of Black respondents indicated that they experienced racial discrimination in the tech workplace. Hispanic respondents followed at 30%, Asian/Pacific Islanders responded at 25%, Asian Indians responded at 23%, and White respondents followed them at 9%.

In a 2022 survey available on Stack Overflow, approximately 2% of all respondents identified either "in their own words" or "transgender." On top of that, approximately 16% of all respondents identified using an option other than "Straight/Heterosexual." Additionally, 10.6% of respondents identified as having a concentration and/or memory disorder, 10.3% identified as having an anxiety disorder, and 9.7% as having a mood or emotional disorder.

When it comes to career mobility, a 2022 report found that there is a gap in promotions given in the tech industry to women in comparison to men. The report found that for every 100 men promoted to manager, only 52 women were given the same promotion.

Factors contributing to underrepresentation

There are two reported reasons for the lack of participation of women and minorities in the computing sector. The first reason is the lack of early exposure to resources like computers, internet connections and experiences such as computer courses. Research shows that the digital divide acts as a factor; students who do not already have computer skills upon entering college are at a disadvantage in computing majors, and access to computers is influenced by demographics, such as ethnic background. The problem of lack of resources is compounded with lack of exposure to courses and information that can lead to a successful computing career. A survey of students at University of Maryland Eastern Shore and Howard University, two historically black universities, found that the majority of students were not "counseled about computer related careers" either before or during college. The same study (this time only surveying UMES students) found that fewer women than men had learned about computers and programming in high school. The researchers have concluded that these factors could contribute to lower numbers of women and minorities choosing to pursue computing degrees.

Another reported issue that leads to the homogeneity of the computing sector is the cultural issue of discrimination at the workplace and how minorities are treated. For participants to excel in a tech-related course or career, their sense of belonging matters more than pre-gained knowledge. That was reflected in “The Great Resignation” that took place in the US during the COVID-19 pandemic. In a survey of 2,030 workers between the ages of 18 and 28 conducted in July 2021, the company found that 50% said they had left or wanted to their leave tech or IT job “because the company culture made them feel unwelcome or uncomfortable,” with a higher percentage of women and Asian, Black, and Hispanic respondents each saying they had such an experience. In most cases, the workplaces not only lack a sense of belonging but are also unsafe. Research conducted by Dice, a tech career hub, showed that more than 50% of women faced sexual harassment in tech companies. A pilot program that was done to understand different elements that affect minorities during a STEM course showed that increased mentorship and support was an important factor for the completion of the course.  

One of the biggest factors halting the increase of diversity in STEM education is awareness. Many experts feel that increasing awareness is a strong first step towards enacting change at a higher level. One of the most common outreach methods are on campus workshops at colleges. These workshops are effective because they instill awareness into people who are just coming into the field and learning about the field to foster inclusivity. Students leaving a workshop at a West Virginia university reported that they were unaware of the problems facing diverse people in STEM, particularly people with disabilities.

Effects on different groups

Black People

Gaming

Black gamers are put into unique positions when it comes to entering spaces of gaming, for when they are represented incorrectly whilst constantly at risk of being harassed for a wide variety of reasons. Whenever they are represented, which is not as often as is what occurs in the real world, it typically comes at the price of being stereotyped into typically two categories: being an athlete, a criminal, or both. If they decide to call out these issues, there is typically heavy backlash for their actions. One such example comes from The Sims community. When its black player base call out issues about various hair texture representations, enter Sims community spaces, or see storylines about black sims members, they typically faced racial attacks, microagressions, or see storylines of characters that looked like them that were based on prevalent stereotypes of black people. The solution to their issues did not come from the creators, but rather groups of black Sims players coming together to make their own spaces in order to have somewhere to go to. Moreover, Black content creators have a unique space within the gaming world: they need to maintain a level of being black that allows people to be comfortable with watching their content, but in creating who they are as creators, they are inherently creating spaces for racialized comments against them that fills their comment sections. Moreover, whenever they do ask for bigger changes, companies take on a race-blind approach to ignoring the problems within the communities they are allowing to exist. When black people are included, it’s mostly because the games being played are inherently included in African American culture, and often considered “diversity nights” for black creators.^[25]

Artificial Intelligence

The issues that lie dormant within the training data of large language models such as ChatGPT can be seen through how it sees black people. Former Google AI Ethicist Timnit Gebru had her time end at Google due to complications over a paper that described the issues of some AI Ethicists: its carbon impact is an issue that could create many issues very soon, greater datasets would lead to complications with currently insensitive vocabulary that was utilized in earlier days of the internet, and the amount of effort it takes to train the model again if something were to fail. There has already been clear evidence that AI models have latent biases that claim that white men are the best scientists. When this was discovered, OpenAI quickly created a block for questions that directly pertained to race, rather than fixing the issue at hand. Something else is the idea of beauty: when creating a supposedly unbiased judge for a beauty contest, BeautyAI asked for submissions from throughout the world, and within its 44 winners of the contest, 38 were white, and 1 finalist had an obvious darker skin tone. These submissions also were used in a manner of gleaning information about health factors affecting the users, and the fact that "healthy" people were put further to the front implies to the AI model that those who are darker skin toned are generally less healthy. Within both of these models, there exists training data that inherently has been given data that presents biases against people of color. A lack of representation within the spaces of developing these models creates an underlying issue of a lack of consideration for more people to be included. If the people that initial testing is done on are coworkers, it is possible that these models from the beginning are untested on all scenarios.

Surveillance

Black and Latinx communities have frequently been the targets of new surveillance and risk assessment technologies that have brought more arrest to these communities. The police have utilized tools to target communities of color for decades. One of the earliest examples of this occurring within the borders United States itself was directly after attacks on the Twin Towers. The New York Police Department used community leaders, taxi drivers, and extensive databases that managed to find ways of connecting people together in order to find more potential terrorists that lived within the United States. This has mostly been done through a program called CompStat, and many precincts have been encouraged to do the same because of its ability to find high crime areas and put more police in areas where they believe crime will happen, leading to even more arrests. In time, this has created systems in which entire states have attempted to create gang databases that have been based on risk assessments, but in turn created situations where children less than a year old were determined to be "self identified gang members". This creates a sense of both confusion and distrust amongst those within these communities, and in turn could lead to even more violence and arrests. These programs have been used throughout the United States such as Boston, Massachusetts, Salina, California, and, most clearly, Camden, New Jersey. Outside of specifically Boston, most of these places have not provided social services to those who are a part of these cycles of violence. Rather, they prefer to put them into prison. This cycle is a positive feedback loop for the computers, and does not help these communities.

Social Media

Africans throughout the world have a much higher risk of harassment through the internet:

1. The two countries with the highest levels of cyberbullying reports came from Kenya and Nigeria, with around 70% of all users claiming to have received hate throughout their time using the internet.
2. Tweets that have discriminatory ideals within them are linked to rates of hate crimes within the area that the Tweet was made.
3. Black People are more likely to report the attacks they received throughout the internet are mostly based on their race.

There is an inherent tie to being black within the internet and also receiving racially-charged hatred. Moreover, because of the lax nature of many popular social media sites (such as Twitter), there exists many ways in which white nationalists can come together to spread hatred through large hate waves that target people of color, and most especially black women.

Increasing diversity

Institutions working to improve diversity in the computing sector are focusing on increasing access to resources and building a sense of belonging for minorities. One organization working toward this goal is EarSketch, an educational coding program that allows users to produce music by coding in JavaScript and Python. Its aim is to spark interest in programming and computer science for a wider range of students and "to attract different demographics, especially girls." The nonprofit Black Girls Code is working to encourage and empower black girls and girls of color to enter the world of computing by teaching them how to code. The American nonprofit for minority students in computer science, ColorStack, also works towards a similar goals, using mentorship-based operations and hosting multiple in-person and virtual support programs as a means of doing so. Another way to widen access to resources is by increasing equality in access to computers. Students who use computers in school settings are more likely to use them outside the classroom, so bringing computers into the classroom improves students' computer literacy.

Those who work in the field of education, primarily educators, have a significant impact on how students perceive the fields of engineering and computing, as well as their own capabilities within these fields. According to the American Association of University Women (AAUW), there are several things that teachers can do to cultivate a sense of confidence in underrepresented individuals interested in pursuing an education or career in the field of computing. Some of these things that educators can do are:

1. Emphasize that engineering skills and abilities can be acquired through learning. In other words, emphasize the idea of a growth mindset.
2. Portray obstacles and challenges as universal experiences, rather than indicators of unsuitability for engineering or computing.
3. Increase accessibility to computing for people from diverse backgrounds and reject the notion that some individuals are inherently better suited to the field.
4. Highlight the varied and extensive applications of engineering and computing.
5. Establish inclusive environments for girls in math, science, engineering, and computing where they're encouraged to tinker with technology and develop confidence in their programming and design skills.

Another way for educators to affect change and help to resolve the problem is through certain intervention methods that have shown to have a positive impact on the issue. These can be implemented by institutions rather than individuals and have shown a lot of promise. Of these there are ten that have been heavily researched and are as follows:

1. Summer Bridge: Summer bridge programs are meant to help students from low income families transition to college life and take place between the end of a prospective student's senior year of high school and freshman year of college. Summer bridge programs are meant to help students adjust and get ahead in their college lives.
2. Mentoring: In this program each student must take a mentor that they can trust to help them when they find themselves struggling while also promoting individual successes.
3. Research Experience: Students participate in research on or off campus during their time as an undergraduate. This has been found to greatly increase a student's likelihood of pursuing a graduate degree compared to students who do not participate in research.
4. Tutoring: One of the most common academic intervention methods a student seeks out a knowledgeable individual to provide extra instruction and practice.
5. Career Counseling and Awareness: Having a connection to someone in the field that a student is trying to join is extremely important. If an institution can help to connect students with someone in their prospective career it causes a higher likelihood of that student staying in that field.
6. Learning Center: An on campus learning center is a place where students can go to learn skills that will help them succeed in school in general. Topics like study skills and note taking skills are taught free of charge.
7. Workshops and Seminars: Short Classes and meetings on campus that focus on skills or research work from professors at other universities who are visiting. Workshops can be used to learn knowledge that is outside of the curriculum.
8. Academic Advising: Higher Quality academic advising is a large factor in increasing student retention. If students feel adequately supported and are paced correctly throughout their experience they are much more likely to finish their degree.
9. Financial Support: Giving financial aid to students through merit scholarships or other outside scholarship opportunities has been found to increase retention rates among Students.
10. Curriculum and Instructional Reform: Find and isolate areas of the program that are meant to “weed out” students and refactor them to be challenging but rewarding.

These methods on their own are not enough to adequately increase the diversity of the talent pool but have shown promise as potential solutions. They can be most effective when used in an integrated manner, meaning the more that are studied and utilized the closer to a solution STEM educators will be.

Since workplace discrimination causes lack of diversity in STEM, changing that would increase diversity in the sector. Big tech companies like Microsoft and Facebook are publishing diversity reports and investing in programs to make their companies more diverse.

Additionally, while companies dedicating resources to initiatives designed to promote diversity within their workplaces is a great start, there is more that tech companies can do. The AAUW published a set of proposals for STEM employers to adopt, aimed at enhancing diversity within their organizations:

1. Sustain effective management practices that are equitable, consistent, and promote a healthy work environment.
2. Administer and advocate for diversity and affirmative action policies.
3. Minimize the detrimental effects of gender bias.
4. Foster a sense of inclusion and belonging.
5. Allow employees the opportunity to work on projects or initiatives that have social significance.

Women in engineering

From Wikipedia, the free encyclopedia
https://en.wikipedia.org/wiki/Women_in_engineering

A female engineer working on an optical communications system test.

Women are often under-represented in the academic and professional fields of engineering; however, many women have contributed to the diverse fields of engineering historically and currently. A number of organizations and programs have been created to understand and overcome this tradition of gender disparity. Some have decried this gender gap, saying that it indicates the absence of potential talent. Though the gender gap as a whole is narrowing, there is still a growing gap with minority women compared to their white counterparts. Gender stereotypes, low rates of female engineering students, and engineering culture are factors that contribute to the current situation where men dominate in fields relating to engineering sciences.

History

Main article: History of women in engineering

The history of women as designers and builders of machines and structures predates the development of engineering as a profession. Prior to the creation of the term "engineer" in the 14th century, women had contributed to the technological advancement of societies around the globe. By the 19th century, women who participated in engineering work often had academic training in mathematics or science. Ada Lovelace was privately schooled in mathematics before beginning her collaboration with Charles Babbage on his analytical engine that would earn her the designation of the "first computer programmer." In the early years of the 20th century, greater numbers of women began to be admitted to engineering programs, but they were generally looked upon as anomalies by the men in their departments.

A 1953 Society of Women Engineers board meeting.

The first University to award an engineering's bachelor's degree for women was University of California, Berkeley. Elizabeth Bragg was the recipient of a bachelor's degree in civil engineering in 1876, becoming the first female engineer in the United States. Prior to the 19th century, it was very rare for women to earn bachelor's degree in any field because they did not have the opportunity to enroll in universities due to gender disparities. Some universities started to admit women to their colleges by the early 1800s and by the mid-1800s they started to admit them into all academic programs including engineering. 

In the United States, the entry into World War II created a serious shortage of engineering talent, as men were drafted into the armed forces. To address the shortage, initiatives like General Electric on-the-job engineering training for women with degrees in mathematics and physics and the Curtiss-Wright Engineering Program among others created new opportunities for women in engineering. Curtiss-Wright partnered with Cornell, Penn State, Purdue, the University of Minnesota, the University of Texas, Rensselaer Polytechnic Institute and Iowa State University to create an engineering curriculum that lasted ten months and focused primarily on aircraft design and production.

During this time, there were few public attacks on female engineers. Chiefly, these attacks were kept quiet inside institutions due to the fact that women did not pressure aggressively to shift the gender gap between men and women in the engineering field. Another reason why these “attacks” were kept private is due to how men believed that it was impossible for engineering to stop being a male-dominated field.

Women's roles in the workforce, specifically in engineering fields, changed greatly during the Post–World War II period. As women started to marry at later ages, have fewer children, divorce more frequently and stopped depending on male breadwinners for economic support, they started to become even more active in the engineering labor force despite the fact that their salaries were less than men's.

Women also played a crucial role in programming the ENIAC from its construction during the World War II period through the next several decades. Originally recruited by the Army in 1943, female ENIAC programmers made considerable advancements in programming techniques, such as the invention of breakpoints, now a standard debugging tool.

In addition to the wartime shortage of engineers, the number of women in engineering fields grew due to the gradual increase of public universities admitting female students. For example, Georgia Tech began to admit women engineering students in 1952, while the École Polytechnique in Paris, a premier French engineering institution, began to admit female students in 1972.

As a result, gender stereotypical roles have changed due to industrialization resolution.

Factors contributing to lower female participation

Gender stereotypes

Stereotype threat may contribute to the under-representation of women in engineering. Because engineering is a traditionally male-dominated field, women may be less confident about their abilities, even when performing equally. At a young age, girls typically do not express the same level of interest in engineering as boys, possibly due in part to gender stereotypes. There is also significant evidence of the remaining presence of implicit bias against female engineers, due to the belief that men are mathematically superior and better suited to engineering jobs. The Implicit Association Test (IAT) shows that people subconsciously connect men with science and women with art, according to the results from over half a million people around the world between 1998 and 2010. This unconscious stereotype also has negative impact on the performance for women. Women who persist are able to overcome these difficulties, enabling them to find fulfilling and rewarding experiences in the engineering profession.

Due to this gender bias, women's choice in entering an engineering field for college is also highly correlated to the background and exposure they have had with mathematics and other science courses during high school. Most women that do choose to study engineering regard themselves as better at these types of courses and as a result, they are capable of studying in a male-dominated field.

Women's self-efficacy is also a contributor to the gender stereotype that plays a role in the underrepresentation of women in engineering. Women's ability to think that they can be successful and perform well is correlated to the choices they make when choosing a college career. Women that show high self-efficacy personalities are more likely to choose to study in the engineering field. Self-efficacy is also correlated to gender roles because men often present higher self-efficacy than women, which can also be why when choosing a major most women opt to not choose the engineering major.

Lower rates of female students in engineering degree programs

Over the past few years, 40% of women have left the engineering field. There are many factors leading to this, such as being judged about going into a difficult major such as engineering, or working in difficult workplace conditions. According to the Society of Women Engineers one in four females leave the field after a certain age.

Women are under-represented in engineering education programs as in the workforce (see Statistics). Enrollment and graduation rates of women in post-secondary engineering programs are very important determinants of how many women go on to become engineers. Because undergraduate degrees are acknowledged as the "latest point of standard entry into scientific fields", the under-representation of women in undergraduate programs contributes directly to under-representation in scientific fields. Additionally, in the United States, women who hold degrees in science, technology, and engineering fields are less likely than their male counterparts to have jobs in those fields.

This degree disparity varies across engineering disciplines. Women tend to be more interested in the engineering disciplines that have societal and humane developments, such as agricultural and environmental engineering. They are therefore well-represented in environmental and biomedical engineering degree programs, receiving 40-50% of awarded degrees in the U.S. (2017–18), and are far less likely to receive degrees in fields like mechanical, electrical and computer engineering.

A study by the Harvard Business Review discussed the reasons why the rate of women in the engineering field is still low. The study discovered that rates of female students in engineering programs are continuous because of the collaboration aspects in the field. The results of the study chiefly determined how women are treated differently in group works in which there are more male than female members and how male members “excluded women from the real engineering work”. Aside from this, women in this study also described how professors treated female students differently “just because they were women”.^[18]

Despite the fact that fewer women enroll in engineering programs across the nation, the representation of women in STEM-based careers can increase when college and university administrators work on implementing mentoring programs and work-life policies for women. Research shows that these rates are difficult to increase since women are judged as less competent than men to perform supposedly “masculine jobs”.

Engineering culture

Autodidact computer chip designer and inventor, Jeri Ellsworth, at the Bay Area "Maker Faire" in 2009.

Another possible reason for lower female participation in engineering fields is the prevalence of values associated with the male gender role in workplace culture. For example, some women in engineering have found it difficult to re-enter the workforce after a period of absence. Because men are less likely to take time off to raise a family, this disproportionately affects women.

Men are also associated with taking leadership roles in the workplace. By holding a position of power over women, they may create an uncomfortable environment for them. For example, women may receive lower pay, more responsibilities, or less appreciation as compared to men. However, women may have more potential to become good leaders: studies have indicated that women have more key leadership skills; for example, the ability to motivate employees, build relationships, and take initiative.

Communication is also a contributing factor to the divide between men and women in the workplace. Male-to-male communication is said to be more direct, but when men explain a task to a women, they tend to talk down, or “dumb down” terms. This comes from the stereotype that men are more qualified than women, and can cause men to treat women as inferiors instead of equals. Other typically masculine traits, such as workplace sexual harassment and creating a hostile work environment also certainly contribute to this atmosphere of domineering attitudes towards women.

Part of the male dominance in the engineering field is explained by their perception towards engineering itself. A study in 1964 found that both women and men believed that engineering was masculine in nature.

Over the past several decades, women's representation in the workforce in STEM fields, specifically engineering, has slowly improved. In 1960, women made up around 1% of all engineers, and by the year 2000, women made up 11% of all engineers, for an increase of 0.25 percentage points per year. At this rate, one would not expect 50-50 gender parity in engineering to occur until the year 2156.

Several colleges and universities nationwide are attempting to decrease the gender gap between men and women in the engineering field by recruiting more women into their programs. Their strategies include increasing women's exposure to STEM courses during high school, planting the idea of a positive outlook on female participation from the engineering culture, and producing a more female-friendly environment inside and outside the classroom. These strategies have helped institutions encourage more women to enroll in engineering programs as well as other STEM-based majors. For universities to encourage women to enroll in their graduate programs, institutions have to emphasize the importance of recruiting women, emphasize the importance of STEM education at the undergraduate level, offer financial aid, and develop more efficient methods for recruiting women to their programs.

Statistics

Percentage of female undergraduate students with engineering degree in India, Australia, Canada, the UK, and US

Country	% of women	year
Australia	14%	2010
Canada	21.8%	2017
India	29.7%	2018
United Kingdom	17.57%	2016-2017
United States	19.7%	2015-2016

United States

Main article: Women in engineering in the United States

In 2014, there were 7.9% female freshmen among all first-year students planning to study in STEM (science, technology, engineering, and mathematics) related majors. In comparison, 26.9% male freshmen intended to major in STEM. For female students who chose engineering, over 32% decided to switch to a different major.

Since 1997, the percentage of Asian females enrolling in engineering majors has risen from about 30% to 34% but somehow also dropped in 2002. African American females have increased their representation in engineering from 21% to 33% in the same time frame. Mexican American and Puerto Rican females have had an increase in their representation from 25% to 31%. Even if ethnicities are included in these statistics, men from all ethnicities still outnumber the proportion of women who enroll in engineering bachelor programs.

The percentage of master's degrees awarded to women has not changed much from 2003 (22.3%) to 2012 (23.1%). The percentage of doctoral degrees awarded to women in engineering increased from 11.6% in 1995, to 17.4% in 2004, to 21.1% in 2008, then to 22.2% in 2012.

There is a significant drop-off rate regarding the number of women who earn a bachelor's degree and the women who afterward enroll in graduate school. Over the last 35 years, women have been more likely than men to enroll in graduate school right after receiving their bachelor's degree. Women who do not enroll in a graduate program right after earning their bachelor's degree tend to be caregivers who face work-family conflicts in the context of family women. The workforce remains the area of lowest representation for women. There were 13% female engineers in 2016. Usually, the salary of female engineers is 10% less than male engineers. The retention of female engineers is also disproportionally low; in 2006, 62.6% of qualified male engineers were employed in engineering professions, as opposed to 47.1% of qualified female engineers.

Female engineering students in class

Canada

Though women tend to make up more than half of the undergraduate population in Canada, the number of women in engineering is disproportionately low. In 2017, 21.8% of undergraduate engineering students were women, and 20.6% of undergraduate engineering degrees were awarded to women. The enrollment of women in engineering climbed from 16% in 1991 to over 20% in 2001, but by 2009 this number had fallen to 17%. One commentator attributed this drop to a number of factors, such as the failure of higher education programs to explain how engineering can improve others' lives, a lack of awareness of what engineers do, lack of networking opportunities and discomfort of being in a male-dominated environment and the perception that women must adapt to fit in.

In the 1990s, undergraduate enrollment of women in engineering fluctuated from 17% to 18%, while in 2001, it rose to 20.6%. In 2010, 17.7% of students in undergraduate engineering were women.

2016 percentage of women enrolled in tertiary education programs in Canada

Province	Undergraduate	Graduate	Doctoral
Alberta	22%	23.3%	23.3%
British Columbia	16.5%	27.5%	27.5%
Manitoba	16%	22.9%	22.9%
New Brunswick	15.9%	19.3%	19.3%
Newfoundland and Labrador	20.9%	20.6%	20.6%
Northwest Territories	—	—	—
Nova Scotia	18.7%	15.8%	15.8%
Nunavut	—	—	—
Ontario	17.7%	21.4%	21.4%
Prince Edward Island	—	—	—
Quebec	16.3%	20.4%	20.4%
Saskatchewan	19%	27.9%	27.9%
Yukon Territory	—	—	—
Canada	17.7%	21.9%	21.9%

In 2017, the disciplines with the highest proportion of undergraduates who are women were environmental, biosystems, and geological engineering. Four out of the five disciplines with the largest percentages of undergraduate who are women were also the disciplines with the fewest overall undergraduate students enrolled. The lowest proportion of women were found in mechanical (14.2%), software (14.6%), and computer engineering (14.8%).

The number of women enrolled in undergraduate, graduate, and doctoral engineering programs tends to vary by province, with the proportion in Newfoundland and Labrador, Prince Edward Island, and Alberta.

The percentage of engineering faculty who are women increased from 13.4% in 2013 to 15.5% in 2017. The University of Toronto has the highest number of female professors in Canada (21) and École Polytechnique de Montréal (18), University of Waterloo (17) and the University of British Columbia (16).

CCWE1992 goals for 1997 and actual 2009 percentage of women involved in engineering in Canada

Women in...	1997	2009
1st year undergraduate	25-25%
Undergraduate programs		17.4%
Master's studies	20%	24.1%
Doctoral studies	10%	22%
Faculty members: professors	5%	Full: 7% Associate: 11% Assistant: 18%
Eng. degree graduates	18%	17.6%
Profession		10.4%

In 2011, the INWES (International Network of Women Engineers and Scientists) Education and Research Institute (ERI) held a national workshop, Canadian Committee of Women in Engineering (CCWE+20), to determine ways of increasing the number of women in the engineering field in Canada. CCWE+20 identified a goal of increasing women's interest in engineering by 2.6% by 2016 to a total of 25% through more incentives such as through collaboration and special projects. The workshop identifies early education as one of the main barriers in addition to other factors, such as: "the popular culture of their generation, the guidance they receive on course selection in high school and the extent to which their parents, teachers, and counsellors recognize engineering as an appropriate and legitimate career choice for women." The workshop report compares enrollment, teaching, and professional statistics from the goals identified in 1997 compared to the actual data from 2009, outlining areas of improvement (see table, right).

United Kingdom

According to the Women's Engineering Society's statistics document, 12.37% of engineers in the UK are female in 2018. 25.4% of females from 16 to 18 years old plan to have a career in the engineering field, compared to 51.9% of males from the same age group.

The Royal Academy of Engineering reported in 2020 that the gender pay gap in the engineering profession is smaller than the average for all UK employment. The mean (10.8%) and median (11.4%) pay gap for engineers in the sample analysed is around two thirds the national average. In 2017, the average salary for female engineers across all engineering fields was £38,109, whereas the average salary for male engineers across all fields was £48,866. The industry average salary is £48,000

Portrayal in popular culture

The 2016 Hollywood film Hidden Figures follows three African American women engineers' work at NASA in 1960. The film was nominated for the 89th Academy Award for Best Picture. In 2019, Mary Robinette Kowal published SF novel The Calculating Stars, which also tells the story of women engineers working in NASA around the same period. The novel received Nebula Award for Best Novel and Hugo Award for Best Novel.

Data

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Data

These are some of the different types of data: Geographical, Cultural, Scientific, Financial, Statistical, Meteorological, Natural, Transport

Data (/ˈdeɪtə/ DAY-tə, US also /ˈdætə/ DAT-ə) are a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data are usually organized into structures such as tables that provide additional context and meaning, and may themselves be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements. Data are commonly used in scientific research, economics, and virtually every other form of human organizational activity. Examples of data sets include price indices (such as the consumer price index), unemployment rates, literacy rates, and census data. In this context, data represent the raw facts and figures from which useful information can be extracted.

Data are collected using techniques such as measurement, observation, query, or analysis, and are typically represented as numbers or characters that may be further processed. Field data are data that are collected in an uncontrolled, in-situ environment. Experimental data are data that are generated in the course of a controlled scientific experiment. Data are analyzed using techniques such as calculation, reasoning, discussion, presentation, visualization, or other forms of post-analysis. Prior to analysis, raw data (or unprocessed data) is typically cleaned: Outliers are removed, and obvious instrument or data entry errors are corrected.

Data can be seen as the smallest units of factual information that can be used as a basis for calculation, reasoning, or discussion. Data can range from abstract ideas to concrete measurements, including, but not limited to, statistics. Thematically connected data presented in some relevant context can be viewed as information. Contextually connected pieces of information can then be described as data insights or intelligence. The stock of insights and intelligence that accumulate over time resulting from the synthesis of data into information, can then be described as knowledge. Data has been described as "the new oil of the digital economy". Data, as a general concept, refers to the fact that some existing information or knowledge is represented or coded in some form suitable for better usage or processing.

Advances in computing technologies have led to the advent of big data, which usually refers to very large quantities of data, usually at the petabyte scale. Using traditional data analysis methods and computing, working with such large (and growing) datasets is difficult, even impossible. (Theoretically speaking, infinite data would yield infinite information, which would render extracting insights or intelligence impossible.) In response, the relatively new field of data science uses machine learning (and other artificial intelligence) methods that allow for efficient applications of analytic methods to big data.

Etymology and terminology

Further information: Data (word)

The Latin word data is the plural of datum, "(thing) given," and the neuter past participle of dare, "to give". The first English use of the word "data" is from the 1640s. The word "data" was first used to mean "transmissible and storable computer information" in 1946. The expression "data processing" was first used in 1954.

When "data" is used more generally as a synonym for "information", it is treated as a mass noun in singular form. This usage is common in everyday language and in technical and scientific fields such as software development and computer science. One example of this usage is the term "big data". When used more specifically to refer to the processing and analysis of sets of data, the term retains its plural form. This usage is common in the natural sciences, life sciences, social sciences, software development and computer science, and grew in popularity in the 20th and 21st centuries. Some style guides do not recognize the different meanings of the term and simply recommend the form that best suits the target audience of the guide. For example, APA style as of the 7th edition requires "data" to be treated as a plural form.

Meaning

Adrien Auzout's "A TABLE of the Apertures of Object-Glasses" from a 1665 article in Philosophical Transactions

See also: DIKW pyramid

Data, information, knowledge, and wisdom are closely related concepts, but each has its role concerning the other, and each term has its meaning. According to a common view, data is collected and analyzed; data only becomes information suitable for making decisions once it has been analyzed in some fashion. One can say that the extent to which a set of data is informative to someone depends on the extent to which it is unexpected by that person. The amount of information contained in a data stream may be characterized by its Shannon entropy.

Knowledge is the awareness of its environment that some entity possesses, whereas data merely communicates that knowledge. For example, the entry in a database specifying the height of Mount Everest is a datum that communicates a precisely measured value. This measurement may be included in a book along with other data on Mount Everest to describe the mountain in a manner useful for those who wish to decide on the best method to climb it. Awareness of the characteristics represented by this data is knowledge.

Data are often assumed to be the least abstract concept, information the next least, and knowledge the most abstract. In this view, data becomes information by interpretation; e.g., the height of Mount Everest is generally considered "data", a book on Mount Everest geological characteristics may be considered "information", and a climber's guidebook containing practical information on the best way to reach Mount Everest's peak may be considered "knowledge". "Information" bears a diversity of meanings that range from everyday usage to technical use. This view, however, has also been argued to reverse how data emerges from information, and information from knowledge. Generally speaking, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, and representation. Beynon-Davies uses the concept of a sign to differentiate between data and information; data is a series of symbols, while information occurs when the symbols are used to refer to something.

Before the development of computing devices and machines, people had to manually collect data and impose patterns on it. With the development of computing devices and machines, these devices can also collect data. In the 2010s, computers were widely used in many fields to collect data and sort or process it, in disciplines ranging from marketing, analysis of social service usage by citizens to scientific research. These patterns in the data are seen as information that can be used to enhance knowledge. These patterns may be interpreted as "truth" (though "truth" can be a subjective concept) and may be authorized as aesthetic and ethical criteria in some disciplines or cultures. Events that leave behind perceivable physical or virtual remains can be traced back through data. Marks are no longer considered data once the link between the mark and observation is broken.

Mechanical computing devices are classified according to how they represent data. An analog computer represents a datum as a voltage, distance, position, or other physical quantity. A digital computer represents a piece of data as a sequence of symbols drawn from a fixed alphabet. The most common digital computers use a binary alphabet, that is, an alphabet of two characters typically denoted "0" and "1". More familiar representations, such as numbers or letters, are then constructed from the binary alphabet. Some special forms of data are distinguished. A computer program is a collection of data, that can be interpreted as instructions. Most computer languages make a distinction between programs and the other data on which programs operate, but in some languages, notably Lisp and similar languages, programs are essentially indistinguishable from other data. It is also useful to distinguish metadata, that is, a description of other data. A similar yet earlier term for metadata is "ancillary data." The prototypical example of metadata is the library catalog, which is a description of the contents of books.

Data sources

With respect to ownership of data collected in the course of marketing or other corporate collection, data has been characterized according to "party" depending on how close the data is to the source or if it has been generated through additional processing. "Zero-party data" refers to data that customers "intentionally and proactively shares". This kind of data can come from a variety of sources, including: subscriptions, preference centers, quizzes, surveys, pop-up forms, and interactive digital experiences. "First-party data" may be collected by a company directly from its customers. The secure exchange of first-party data among companies can be done using data clean rooms. "Second-party data" refers to data obtained from other organizations or partners, through purchase or other means and has been described as "another organization's first-party data". "Third-party data" is data collected by other organizations and subsequently aggregated from different sources, websites, and platforms.

Summary of data sources

Data source	Owned by	Accuracy	Use case	Privacy risk
First-party	The business	High	Personalization, retargeting	Low
Second-party	Partner	Moderate	Partnership campaigns	Moderate
Third-party	External entity	Low	Broad targeting	High

"No-party" data can sometimes refer to synthetic data that is generated based on patterns from original data.

Data documents

Whenever data needs to be registered, data exists in the form of a data document. Kinds of data documents include:

• data repository
• data study
• data set
• software
• data paper
• database
• data handbook
• data journal

Some of these data documents (data repositories, data studies, data sets, and software) are indexed in Data Citation Indexes, while data papers are indexed in traditional bibliographic databases, e.g., Science Citation Index.

Data collection

Gathering data can be accomplished through a primary source (the researcher is the first person to obtain the data) or a secondary source (the researcher obtains the data that has already been collected by other sources, such as data disseminated in a scientific journal). Data analysis methodologies vary and include data triangulation and data percolation. The latter offers an articulate method of collecting, classifying, and analyzing data using five possible angles of analysis (at least three) to maximize the research's objectivity and permit an understanding of the phenomena under investigation as complete as possible: qualitative and quantitative methods, literature reviews (including scholarly articles), interviews with experts, and computer simulation. The data is thereafter "percolated" using a series of pre-determined steps so as to extract the most relevant information.

Data longevity and accessibility

An important field in computer science, technology, and library science is the longevity of data. Scientific research generates huge amounts of data, especially in genomics and astronomy, but also in the medical sciences, e.g. in medical imaging. In the past, scientific data has been published in papers and books, stored in libraries, but more recently practically all data is stored on hard drives or optical discs. However, in contrast to paper, these storage devices may become unreadable after a few decades. Scientific publishers and libraries have been struggling with this problem for a few decades, and there is still no satisfactory solution for the long-term storage of data over centuries or even for eternity.

Data accessibility. Another problem is that much scientific data is never published or deposited in data repositories such as databases. In a recent survey, data was requested from 516 studies that were published between 2 and 22 years earlier, but less than one out of five of these studies were able or willing to provide the requested data. Overall, the likelihood of retrieving data dropped by 17% each year after publication. Similarly, a survey of 100 datasets in Dryad found that more than half lacked the details to reproduce the research results from these studies. This shows the dire situation of access to scientific data that is not published or does not have enough details to be reproduced.

A solution to the problem of reproducibility is the attempt to require FAIR data, that is, data that is Findable, Accessible, Interoperable, and Reusable. Data that fulfills these requirements can be used in subsequent research and thus advances science and technology.

In other fields

Although data is also increasingly used in other fields, it has been suggested that their highly interpretive nature might be at odds with the ethos of data as "given". Peter Checkland introduced the term capta (from the Latin capere, "to take") to distinguish between an immense number of possible data and a sub-set of them, to which attention is oriented. Johanna Drucker has argued that since the humanities affirm knowledge production as "situated, partial, and constitutive," using data may introduce assumptions that are counterproductive, for example, that phenomena are discrete or are observer-independent. The term capta, which emphasizes the act of observation as constitutive, is offered as an alternative to data for visual representations in the humanities.

The term data-driven is a neologism applied to an activity which is primarily compelled by data over all other factors. Data-driven applications include data-driven programming and data-driven journalism.

Data engineering

From Wikipedia, the free encyclopedia

https://en.wikipedia.org/wiki/Data_engineering

Data engineering is a software engineering approach to the building of data systems, to enable the collection and usage of data. This data is usually used to enable subsequent analysis and data science, which often involves machine learning. Making the data usable usually involves substantial compute and storage, as well as data processing.

History

Around the 1970s/1980s the term information engineering methodology (IEM) was created to describe database design and the use of software for data analysis and processing. These techniques were intended to be used by database administrators (DBAs) and by systems analysts based upon an understanding of the operational processing needs of organizations for the 1980s. In particular, these techniques were meant to help bridge the gap between strategic business planning and information systems. A key early contributor (often called the "father" of information engineering methodology) was the Australian Clive Finkelstein, who wrote several articles about it between 1976 and 1980, and also co-authored an influential Savant Institute report on it with James Martin. Over the next few years, Finkelstein continued work in a more business-driven direction, which was intended to address a rapidly changing business environment; Martin continued work in a more data processing-driven direction. From 1983 to 1987, Charles M. Richter, guided by Clive Finkelstein, played a significant role in revamping IEM as well as helping to design the IEM software product (user data), which helped automate IEM.

In the early 2000s, the data and data tooling was generally held by the information technology (IT) teams in most companies. Other teams then used data for their work (e.g. reporting), and there was usually little overlap in data skillset between these parts of the business.

In the early 2010s, with the rise of the internet, the massive increase in data volumes, velocity, and variety led to the term big data to describe the data itself, and data-driven tech companies like Facebook and Airbnb started using the phrase data engineer. Due to the new scale of the data, major firms like Google, Facebook, Amazon, Apple, Microsoft, and Netflix started to move away from traditional ETL and storage techniques. They started creating data engineering, a type of software engineering focused on data, and in particular infrastructure, warehousing, data protection, cybersecurity, mining, modelling, processing, and metadata management. This change in approach was particularly focused on cloud computing. Data started to be handled and used by many parts of the business, such as sales and marketing, and not just IT.

Tools

Compute

High-performance computing is critical for the processing and analysis of data. One particularly widespread approach to computing for data engineering is dataflow programming, in which the computation is represented as a directed graph (dataflow graph); nodes are the operations, and edges represent the flow of data. Popular implementations include Apache Spark, and the deep learning specific TensorFlow. More recent implementations, such as Differential/Timely Dataflow, have used incremental computing for much more efficient data processing.

Storage

Data is stored in a variety of ways, one of the key deciding factors is in how the data will be used. Data engineers optimize data storage and processing systems to reduce costs. They use data compression, partitioning, and archiving.

Databases

If the data is structured and some form of online transaction processing is required, then databases are generally used. Originally mostly relational databases were used, with strong ACID transaction correctness guarantees; most relational databases use SQL for their queries. However, with the growth of data in the 2010s, NoSQL databases have also become popular since they horizontally scaled more easily than relational databases by giving up the ACID transaction guarantees, as well as reducing the object-relational impedance mismatch. More recently, NewSQL databases — which attempt to allow horizontal scaling while retaining ACID guarantees — have become popular.

Data warehouses

Main article: Data warehouse

If the data is structured and online analytical processing is required (but not online transaction processing), then data warehouses are a main choice. They enable data analysis, mining, and artificial intelligence on a much larger scale than databases can allow, and indeed data often flow from databases into data warehouses. Business analysts, data engineers, and data scientists can access data warehouses using tools such as SQL or business intelligence software.

Data lakes

A data lake is a centralized repository for storing, processing, and securing large volumes of data. A data lake can contain structured data from relational databases, semi-structured data, unstructured data, and binary data. A data lake can be created on premises or in a cloud-based environment using the services from public cloud vendors such as Amazon, Microsoft, or Google.

Files

If the data is less structured, then often they are just stored as files. There are several options:

• File systems represent data hierarchically in nested folders.
• Block storage splits data into regularly sized chunks; this often matches up with (virtual) hard drives or solid state drives.
• Object storage manages data using metadata; often each file is assigned a key such as a UUID.

Management

The number and variety of different data processes and storage locations can become overwhelming for users. This inspired the usage of a workflow management system (e.g. Airflow) to allow the data tasks to be specified, created, and monitored. The tasks are often specified as a directed acyclic graph (DAG).

Lifecycle

Business planning

Business objectives that executives set for what's to come are characterized in key business plans, with their more noteworthy definition in tactical business plans and implementation in operational business plans. Most businesses today recognize the fundamental need to grow a business plan that follows this strategy. It is often difficult to implement these plans because of the lack of transparency at the tactical and operational degrees of organizations. This kind of planning requires feedback to allow for early correction of problems that are due to miscommunication and misinterpretation of the business plan.

Systems design

The design of data systems involves several components such as architecting data platforms, and designing data stores.

Data modeling

Main article: Data modelling

This is the process of producing a data model, an abstract model to describe the data and relationships between different parts of the data.

Roles

Data engineer

A data engineer is a type of software engineer who creates big data ETL pipelines to manage the flow of data through the organization. This makes it possible to take huge amounts of data and translate it into insights. They are focused on the production readiness of data and things like formats, resilience, scaling, and security. Data engineers usually hail from a software engineering background and are proficient in programming languages like Java, Python, Scala, and Rust. They will be more familiar with databases, architecture, cloud computing, and Agile software development.

Data scientist

Main article: Data science

Data scientists are more focused on the analysis of the data, they will be more familiar with mathematics, algorithms, statistics, and machine learning.