Asking For Directions
  • 29th August, 2017
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Asking For Directions

The month of August is National Women’s Month in South Africa. Despite the immense gains that women have made in education and the workforce since the 1950’s, there are still very few women in STEM fields, and that includes Data Science. 

Science, technology, engineering and mathematics (STEM) are fundamental to the economy and the ability of a country or business to be, and remain, competitive. Job studies point to a future career landscape where most jobs will require a STEM education, with scientists and engineers working hard to solve some of the biggest challenges on the planet.

At the moment, less than 10% of young women worldwide show an interest in STEM, and this figure is likely much lower across Africa. The percentage of women in STEM jobs globally sits at 14%, but is just 7% in South Africa. South Africa ranked lowest (148th out of 148 countries) in a recent World Economic Forum report on Education assessing the quality of Maths and Science at school. With 8 out of 10 jobs with skills scarcity in South Africa being STEM related, the country clearly has a supply problem. There are several social and environmental factors for the continued gender disparity including learning environment, negative stereotypes about girls’ abilities, and unconscious bias regarding fields of study.

The skills shortage in Data Science and Analytics is well documented. With an estimated shortage of 140 000 - 190 000 jobs by 2017 in the US alone and a demand 60% greater than supply, it’s evident that we need more women Data Scientists. It may seem contradictory, knowing that poor gender stereotypes lead to fewer women in STEM fields, but some of the stereotypical female traits are the same qualities of thriving, successful data scientists:

1. Communication
Results have to make the journey from the data science team out into the company for them to be applied and used. Great data scientists understand both the problem and solution within their business context and render results useful to business decision-makers. Exceptional communicators translate complex technical solutions and results in ways that are simple, jargon-free and that anybody could quickly grasp.

2. Teamwork
Collaboration, and working with different people across multiple departments is essential to data science projects. Women tend to be skilled collaborators, with the ability to manage various people across the data science value chain.

3. The Ability to Ask for Directions 
Men not asking for directions is probably the most famous example of the great gender divide - a quick Google search brought up 31.5 million items on this subject. A good data scientist will ask hundreds of questions (even asking for directions) to fully understand the problem landscape. Adept data scientists don’t assume that experts see or know everything they do and don’t fear asking a multitude of weird and wonderful questions.

The extraordinary, and often challenging, part of data science is the variety of competencies associated with the field. It’s crucial to remember that it’s not solely scientific and technical and we need men and women with a diverse skill set to meet the severe shortage we face.

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Ps: In keeping with our drive to support exceptional women to develop careers in data science, Angela Lum-neh a 2nd year student at the African Leadership University in Mauritius, starts her internship with Ixio Analytics in Cape Town next month.
 

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About Author

Megan founded Ixio in 2012 after seeing a need for strong, data-led modelling and analytics in business. As Chief Scientist at Ixio Analytics, Megan leads our advanced modelling programs and coordinates the technical requirements for our clients. Her background in Evolutionary Biology allows her to bring a rigorous scientific approach to analytical problem solving. Megan is highly regarded in the industry and contributes regularly to data and analytics conferences, both locally and internationally. She takes a keen interest in environmental issues and is an avid surfer.

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