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WiDSTORY | January 10, 2020

The 2nd Place Winners of the 2019 WiDS Datathon “Full Fuego” Collaborated Across Countries and Generations

Tell us about your background.

Alison: I was born in the UK, earned my first degree in geography and statistics in Wales, followed by a master’s degree in cartography and digital mapping in Scotland. When I was 28, I was sent to Paris by Britain’s national mapping agency to work on European mapping projects. After five years in Paris, I moved to Madrid with my Spanish husband and our baby (Laura).

Laura: I was born in Paris but moved to Madrid as a baby and am still here, currently pursuing a nursing degree.

Maria: I am from Seville, Spain. After I finished my master’s degree in Telecommunications Engineering, I moved to the UK to improve my English skills. I started working as a software engineer and loved working in this country, so I am still living here after more than 17 years. During this time, I got married and had a son.

How did you get interested in data science?

Alison: I was interested in data science from the time of my first degree, although it wasn’t called that at the time. I always enjoyed the analytical side of geography most. After all, a map is a great data visualisation, lots of information represented by different styles of points, lines and polygons and color.

Laura: My mother nagged me into being interested in data science through family discussions over dinner. Having investigated a little more, I now see that data science will be very important in the future in health care.

Maria: I lost my job while expecting my son, so I decided to take a long maternity break after he was born. During that time, I started to think that I needed a career change, as I wasn’t enjoying my most recent jobs. I started to learn Python, and that was when I discovered data science.

What are you currently working on?

Alison: I am currently studying at the Data Science Bootcamp at IE in Madrid, which was a prize from WiDS Madrid in the WiDS 2019 Datathon. This includes a project using real data in partnership with a company. My team is working on identifying sales territories with the highest sales potential, so there are lots of opportunities for studying the demography of Spain and the impact of socio-economic factors on sales.

Laura: My nursing course includes biostatistics and research practices. A greater awareness of data has helped me to better understand the role of data in medical research.

Maria: I just finished a Data Science Bootcamp (S2DS by Pivigo) and my plan is start looking for a job early in 2020.

How did you first discover WiDS?

Alison: I discovered WiDS through the data science Twitter community. I saw the WiDS 2019 Datathon being promoted and decided to enter what seemed to be a manageable competition in terms of a binary classification challenge and a reasonable volume of data. Once the competition was underway, I thought it would be good to have an all-female team, so invited Maria and Laura to form a team with me. I learned that working as a team in the datathon is more fun than working alone. The more ideas, the better. We chose the team name “Full Fuego” (“Let’s go for it, 100%!”) for the mixture of English and Spanish and because it made us laugh.

I was most inspired by Jeremy Howard and Rachel Thomas of fastai. Not only by the superb content of the online courses but also by their philosophy that anybody can do this if they put the effort in, and their creation of a community of people helping each other. I have learned from many online resources, including Coursera and Udacity, but primarily from fastai.

Laura: I discovered WiDS once my mother had entered the 2019 Datathon. She was constantly updating us on progress.

Maria: I discovered WiDS when Alison mentioned the 2019 Datathon to me and asked if I was interested in participating with her. I had ‘cyber-met’ Alison during a Facebook Scholarship organized by Facebook and Udacity and she was always very supportive, so I decided it was the best way to participate in my ‘first-ever’ Kaggle competition.

I had never heard of ‘fastai’ until I started this competition. Alison is a great fan of fastai and after I learned what you can do, I now understand why and have been using it for other computer vision projects since the datathon.

I learned a lot from Alison during the competition, not only about fastai, but also about general concepts that I wasn’t aware of. Also, I was quite afraid of participating in a competition but she made me feel more confident and I enjoyed the whole experience.

Have you been involved with WiDS since that first experience? If so, in what way?

Alison: Since that first experience I have been listening to the WiDS Podcast. I find the podcasts are a great way of passively learning about the wide range of projects in data science. When I first started listening to data science podcasts there was an awful lot of technical content that I didn’t understand but I did start to build up a picture of the whole field, which then meant I could identify the areas that I found to be most interesting.

Maria: I am following WiDS on Twitter and read many of the blogs.

How has WiDS made an impact on your life and/or work?

Alison: Coming in second in the WiDS 2019 Datathon was a huge boost to my self-confidence. I really started to feel that I could do this and was not just an onlooker. Thanks to the local prize from WiDS Madrid, I am now mid-way through the IE Data Science Bootcamp in Madrid.

Laura: I was inspired by WiDS to read Invisible Women: Exposing Data Bias in a World Designed for Men by Caroline Criado Perez. This gave me greater awareness of how data bias impacts our lives.

Maria: WiDS gave me an excellent awareness of working in data science as part of a team. Previously all my experience had been self-learning and participating in forums. I have participated in a few WiDS meetups in London, which is an excellent opportunity to meet other women in the field, learn from them and share experiences.

What comes next for you? And what are your hopes for women in the data science in the future?

Alison: I don’t know what the future holds but I hope to continue learning and entering data science competitions. Let’s see what happens: the possibilities are endless. I really hope that we can get more young women interested in data science. We need to change the image from techy, young men to make the field attractive for women and help to mitigate the negative effects of the technical revolution that we are now seeing; many of which are a result of the lack of diversity in thinking at the early stages of a project.

Laura: These challenges are very useful for encouraging greater participation from women. I look forward to continuing to learn.

Maria: I would love to go back to working as a data scientist soon. There are so many interesting projects and things to learn that I can’t wait. I have found that WiDS is a strong and supportive community and it is something that I am very enthusiastic about, as I never felt that way while working as a software engineer. I think this kind of initiative is helping to get more women into data science and technology in general.