Empowering Women in Data Science

Saturday, April 4, 2020

two female programmers working on a project

Inequality in the workforce creates both ethical and economic ramifications. Women have fewer career opportunities, earn less and complete more unpaid work than men, creating inequity that shouldn’t exist in the modern world. Studies have shown that when women are underrepresented in the workforce, economic growth suffers, creativity and innovation become stagnant, and companies can feel the impact on both their productivity and their bottom line.

Underrepresentation of women has been a challenge for technology fields such as data science. According to Forbes, only 26% of data jobs in the United States are held by women. Fortunately, businesses are recognizing the benefits of gender equality and the ethical reasons for diverse labor forces and are taking steps to empower women to enter the field of data science.

History of Women in Data Science

Despite current underrepresentation of women in data science, women throughout history have made many significant contributions to the field:

  • Ada Lovelace: Born in 1815, Ada Lovelace has been dubbed “the first computer programmer” for her work introducing numerous concepts for Charles Babbage’s proposed analytical engine, which was intended to perform complex mathematical calculations.

  • Jean Jennings Bartik: One of the programmers for the Electronic Numerical Integrator and Computer (ENIAC), Jean Jennings Bartik helped develop the world’s first all-electronic digital computer. The University of Pennsylvania developed the ENIAC for the U.S. Army in 1945. Bartik went on to work on several other significant computers.

  • Katherine Johnson: Katherine Johnson was a NASA mathematician whose orbital mechanics calculations helped launch the first human space flights. She helped to pioneer the use of computers to perform complex calculations and was one of the first African American female NASA scientists.

  • Grace Hopper: A computer scientist and U.S. Navy admiral, Grace Hopper is known for cowriting the COBOL computer language in 1959. Today, companies and governments use COBOL in business, finance and administrative systems.

  • Reshama Shaikh: A strong advocate for women in data science, Reshama Shaikh worked as a biostatistician for pharmaceutical companies and founded the Women in Machine Learning and Data Science and PyLadies groups, which promote gender equality in technology fields.

  • Adele Goldberg: Computer scientist Adele Goldberg has made many advancements in the data science field. She helped develop the Smalltalk-80 programming language and other object-oriented programming concepts.

  • Vivian Shangxuan Zhang: Vivian Shangxuan Zhang is the chief technology officer and founder of the NYC Data Science Academy, which provides analytics training. She also founded iCamp, which teaches science, technology, engineering and math (STEM) concepts to children.

Why Data Science Needs More Women 

The percentage of women in data science is extremely low. This employment gap points to social and ethical unfairness that reflects poorly on modern societies. Women need to be equally represented in technology fields to break down gender biases and stereotypes and build confidence in younger generations. The existing contributions made by women in the field illustrates the importance of their continued participation.

Due to the low number of women in data science, the field isn’t benefiting from the valuable perspectives of a significant portion of the population. With a diverse workforce, innovation and creativity are fueled by input from individuals with a wide range of experiences and world views. Workplace culture is enhanced through balanced social interactions.

Diversity also creates economic benefits.  Companies with strong gender diversity rates are 15% more likely to outperform those with low representation of women, according to a McKinsey study. In addition, the Organization for Economic Cooperation and Development predicts a 6% gain in gross domestic product if the global gender workforce gap is reduced 50% by 2030.

Why Are So Few Women in Data Science?

Why are women so scarce in the data science field? Feelings of isolation are a major concern, as is the lack of available mentors resulting from so few women holding computing positions. In addition, the gender pay gap is a serious issue, with women in all careers making $0.79 for every dollar that men make, according to a 2019 PayScale report. Lack of STEM-based education can also be a contributing factor, as can gender biases among hiring staff and corporate human resources policies that lack gender balance rules.

The first step a company must take to improve workplace equality is to establish a gender diversity hiring policy. Companies must identify the departments where women are underrepresented and target hiring accordingly. Additionally, companies must ensure that women are proportionally represented in leadership roles, including top executive positions. Universities can also play a role in encouraging more women to enroll in advanced data science courses and accommodating their needs.

Pursue a Career in Data Science

Data science is one of the fastest-growing occupational roles in the United States, according to LinkedIn. As an increasing number of companies realize that gender diversity improves talent and innovation, more opportunities for women are becoming available. In fact, many big-name tech firms like Microsoft and Dell have launched programs to encourage diversity in their workforces.

Women interested in pursuing data science careers typically earn a bachelor’s degree in a STEM-related field before moving on to a master’s program in computer science, data analytics, business analytics or a similar field of study. While earning an advanced degree, students gain the necessary skills for a career in data science, including programming, data and software engineering, statistics, communication, mathematics and problem-solving.

Earn a Master’s in Business Analytics

Women interested in data science may wish to explore a Master’s in Business Analytics from the University of San Diego. USD’s MSBA program gives students the knowledge they need to become leaders in data science and business analytics. A robust curriculum provides a strong foundation in data analytics tools such as Tableau, Python and SQL, as well as communication, problem-solving, statistics, accounting, data mining and data management. Learn more about how the program can help you pursue your professional goals in the field of data science.

 

Recommended Readings

What Is Business Analytics? An Inside Look at the Merger of Business and STEM

Effective Data Presentation Techniques to Guide Data Strategy

Step-by-Step Guide to Becoming a Health Data Analyst

 

Sources

American Association of University Women, “Before Gates and Jobs, There Was Admiral Grace Murray Hopper”

American Association of University Women, “Solving the Equation: The Variables for Women’s Success in Engineering and Computing”

Better Buys, Why We Need Women in Data Science 

Biography, Ada Lovelace 

DataCamp, “Reshama Shaikh Discusses Women in Machine Learning and Data Science”

Dataquest, “The Gender Gap in Data Science (and What You Can Do About It)” 

Encyclopaedia Britannica, Jean Bartik

Forbes, “Breaking Down the Gender Gap in Data Science”

Forbes, “The Data Science Diversity Gap”

McKinsey & Company, Why Diversity Matters

NASA, Katherine Johnson

Organization for Economic Cooperation and Development, “Why a Push for Gender Equality Makes Sound Economic Sense”

LinkedIn, “5 of the Fastest Growing Jobs in the USA And How to Get Them”

PayScale, The State of the Gender Pay Gap 2019

Contact:

Renata Ramirez
renataramirez@sandiego.edu
(619) 260-4658