With the continuous evolution of technology and new developments arising from the need to integrate technology to efficiently deliver an excellent digital consumer experience, more women are taking charge by being part of this evolution through their involvement in local communities tailored to effectively share resources and current trends in data science and Machine Learning.
The WiMLDS community which comprises of data scientist and machine learners aims at increasing representation of women data scientist into the tech space, the luck of therefore presented an opportunity to build up this local community where the majority are self taught and hence are able to keep up with the ever highly advancing technology.
“We are all largely self taught so we found each other while looking for data science and machine learning communities to aid our learning journeys. There was no such community in existence and the opportunity presented itself to start a local chapter of Women in Machine Learning and Data Science. So we jumped at it and now it has been almost 2 years,” says Kathleen Siminyu Head of data science at Africa’s Talking.
Kathleen who studied Mathematics and computer science as her major at Jomo Kenyatta University of Agriculture and Technology (JKUAT), ended up breaking into data science space and acknowledges that there is need to continue the conversation of increasing the female data scientist into the space and one way of doing so is through community engagement which assists them build confidence and start showcasing their capabilities.
One of the main differences between men and women that we noted is that the men are challenged to come back and learn more
“One of the main differences between men and women that we noted is that the men are challenged to come back and learn more when they find content that is beyond their level whereas the women would not come back for a couple of sessions and instead be trying to catch up on their own or just not come back at all. I am interested to see if in the next couple of years these efforts will be reflected in the job market since our efforts are mainly aimed at beginners who are trying to learn”, she remarked.
However, its evidently notable that the gender divide in the local Machine Learning and data science community is close to 50/50 courtesy of the space the community creates but if zoomed out, the wider Africa Data science community still has a lot left to be desired. If the technology ecosystem is zoomed out further its still is a field where the higher you go, the lesser women you find.
The key elements to venture into this career, however, is the desire and commitment to continuously learn, given that in this internet age, world class learning materials are freely available online. An added advantage would be a background in mathematics and/or computer science that will help boost hacking skills and statistical knowledge.
THE FUTURE OF DATA SCIENCE AND MACHINE LEARNING?
The trajectory of Data science and machine learning in Africa will help redefine our continent in the next couple of days, the involvement of individuals across Africa doing similar activities from the grassroots communities contributes to building networks where people connect and share the resources, ideas, current trends and the future progressions. It is paramount to focus on building a technical capacity by having online courses and competitions that use African data-sets to solve African challenges and use that as learning materials.
At the moment, naturally, there is a skew towards the western context since they are the curators and main consumers of the content. Fortunately, We now have individuals and movements contemplating these things and writing books on the same. “Realistically, we still have a long way to go. We cannot hope to democratize machine learning on the continent when access to quality education, clean water and healthcare are a luxury. Did I already mention I am a dreamer? I would like to see these efforts in building technical skills on the continent translate into solutions to some of our most pertinent problems. Literacy and learning, access to energy and possibly a redefining of our governance,” says Kathleen.
We need more. Much much more. Representation is important.
However, we are not left behind in this evolution, we have hardly even began, but we are aware, we are here and we are doing something. Google setting up an AI lab in Ghana and setting up a Machine Intelligence Masters in Rwanda also validates the work that all these communities have been undertaking in the past couple of years.
“We need more. Much much more. Representation is important. Besides the desire to work in tech, something which has played a major role in my sticking with it this long is because I had female colleagues at Africa’s Talking who I looked up to. Every time I went for an engineering meeting and felt out of my depth, the fact that there were other women in the room taking on that stuff helped me keep going. It meant I could too if I applied myself, whereas the alternative might have been me sitting in that room and thinking that perhaps it is because I am a woman that I cannot keep up, as opposed to the reality which was inexperience…
We also need that diversity in leadership because, let’s face it, until you walk in someone’s shoes, you are not in a position to address their problems or build solutions for them. Teams and organisations that are diverse do better courtesy of the fact that they have a pool of knowledge and experience that is reflective of the world so they can build and strategize with a holistic view of what the market actually needs,” she concludes.