Data science and analytics! What’s the future of women in data science?
A 2020 report from the National Center for Women and Information Technology (NCWIT) states that although women employees make up 57% of the workforce overall, they only make up 26 % of jobs in computers and math. This article is the first of a new series that the University of Wisconsin’s Master of Science in Data Science is publishing to raise awareness of the value and necessity of women in the data science and analytics field.
Even while there is still more to be done to ensure the inclusion of women in data science, there are some figures that are worth highlighting. In this article, we examine hiring patterns, the skills that women contribute to the data science industry, and ways that female data scientists can continue to be competitive in the field.
Let’s start by breaking down the proportion of women working in the computing industry. According to a 2022 Burtch Works survey, there are now 24% more women working as data scientists than there were in 2018 when there were just 15% of women in this field. The same analysis discovered that the category of entry-level individual contributor positions has the highest proportion of female employees working in data science.
These figures suggest the beginning of women-led leadership as well as a potential increase in the number of women working in the area. But there is still a lot of room for advancement in the computing industry for women and other underrepresented groups, particularly in the mid-to senior-level leadership positions traditionally filled by men.
The 2020 NCWIT research also revealed that women are more racially and culturally diverse than men in the computing workforce. The graph below demonstrates that more women than men have positions in computing and are of African American, Black, or Asian heritage.
Diversity in the workplace is important for decreasing bias, which is crucial in the computing industry. A study by the Boston Consulting Group (BCG) found that people and machine learning algorithms both occasionally “see” patterns that lead to false, skewed, or even harmful conclusions when evaluating causal links and correlations in huge data sets.
Diversity in the workplace also raises the standard of work. In a study conducted by Bo Cowgill and Fabrizio Dell’Acqua of Columbia University, it was discovered that prediction errors were connected among demographic groupings, particularly by gender and race. More diverse teams will decrease the possibility of biases compounding, leading to fewer mistakes.
Diversity lessens bias while increasing innovation revenue. The likelihood that one of the answers will be financial success rises because people from various backgrounds and experiences frequently approach challenges in unique ways to generate a diversity of solutions.
All businesses, from public safety to healthcare to renewable energy, use data science. The work of data scientists within these industries frequently has a noticeable influence on the technology that will define our future. Companies can emphasize the specific issues that data scientists resolve in their business to demonstrate how data science is at the core of good decision-making and attract women to data science professions. Bottom line: All industries must strive to improve communication with women in data science if they are to benefit from the experiences, viewpoints, and talents that these individuals have to offer.
The gender pay gap in leadership and data science occupations is still a serious problem. Prioritizing diversity, equity, and inclusion activities in internal culture and hiring procedures can be beneficial for every organization. This calls for dialogue at all levels of the workforce, from academic institutions awarding degrees to CEOs debating promotions to leadership roles.
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