View the article here: What’s Keeping Women Out of Data Science

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Analyzing the article What’s Keeping Women Out of Data Science

About the Article

Title Authors Publisher Date
What’s Keeping Women Out of Data Science Sylvain Duranton, Jörg Erlebach, Camille Brégé, Jane Danziger, Andrea Gallego, and Marc Pauly Boston Consulting Group March 06 2020

Problems within Data Science/STEM

The article discusses the growth of data science as a field as well as its impression to those not within the field. To most, data science can be perceived as unappealing due to the “nerdy” work culture stereotype associated with STEM careers. Besides it’s lack of appeal, the article highlights the scarcity of diversity. The article claims that women (on average across countries) make up 55% of university graduates but only account for approximately ⅓ of STEM degrees; within the ⅓, only ⅔ go into analytics or software development, and even less go into data science. The article suggests a few reasons why women may be less likely to go into STEM fields or even data science is because of the biases that women face. As few as 15-22% of data scientists are women.

Negative Perceptions of Data Science Broken Down by Countries

Clearing Misconceptions of Data Science

A solution to clearing many of the misconceptions of data science, as well as finding ways to involve women,can be done by doing the following: 1) Clearer communication of what a normal day as a data scientist looks like 2) More chances to connect with people within the field 3) Creating a better understanding of what qualifications are expected for the job 4) Communicate more effectively about data science with students, specifically female students The article has a mature, adult-focused approach with the solutions it provides. As a female, I believe that we should start implementing these solutions at a young age; in my opinion, STEM related fields should be emphasized and encouraged to females in early education (elementary to middle school). These are the years that girls discover many stereotypes, one being that guys are “better” than girls when it comes to STEM related material.

Perception of Data Science in Different Countries

According to the article, although data science has a lot of real-world application, data science is seen to be as mostly theoretical and abstract, with low impact and purpose. The article looks at different countries, specifically UK, US, France, Canada, Spain, Australia, Germany, India, China, Japan.The article does suggest that this may be made up of a plethora of reasons including misperceptions that result from university curricula and different perception of companies.

Negative Perceptions of Data Science Broken Down by Country

Important Quote from the Article

“As long as companies struggle to present data science as a field that is attractive to all students (not just to some), a large share of the female talent pool will continue to vote with their feet and avoid the field altogether, perpetuating the lack of diversity in this increasingly mission-critical part of companies’ workforces.”

About the Authors

There were multiple authors for this article. Surprisingly, not all of them were female (as I would have assumed to be true). It is nice to know that men also recognize the gender gap in data science/STEM as well as some of the factors causing this. In a more general context, this article was published under BCG’s website, which claims to be a “global team dedicated to applying artificial intelligence and advanced analytics to business at leading companies and organizations.” Their team is made up of 800+ data scientists and engineers.It is interesting to see that even a group as big as BCG recognizes that there is a huge gender gap.It’s refreshing to see companies that care about making their teams more inclusive and diverse.

What I Found Most Useful from the Article

This article emphasized a lot of points that I, as a girl in STEM, already was aware of; however, it had a lot of statistics that I was unaware of. I feel that my understanding of why there is a lack of diversity in STEM is more clear now as well as what we can do to make STEM a more inclusive field. I feel that this article gave me a lot to think about, and made me want to continue reading about this issue. It is such an important topic, especially for me since I am a year from graduating and looking for jobs as a data scientist.

Using a Dataset Inside the R Environment

summary(Orange)
##  Tree       age         circumference  
##  3:7   Min.   : 118.0   Min.   : 30.0  
##  1:7   1st Qu.: 484.0   1st Qu.: 65.5  
##  5:7   Median :1004.0   Median :115.0  
##  2:7   Mean   : 922.1   Mean   :115.9  
##  4:7   3rd Qu.:1372.0   3rd Qu.:161.5  
##        Max.   :1582.0   Max.   :214.0

Interactive Summary Table

datatable(Orange, options = list(pageLength = 10))

Including Plots

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