IBM Code Bristol in collaboration with Jean Golding Institute at the University of Bristol hosted our first Women in Data Science Bristol event, to coincide with the annual Global Women in Data Science (WiDS) Conference held at Stanford University.

Being the WiDs ambassador for Bristol, I organised and presented this online event which was hosted on the BigMarker Platform. Our event had an all-female lineup of speakers with various Tech Talks and a panel discussion, with just over 100 attendees tuned in live for the event.

About the Speakers : Our speakers for the event were from various different backgrounds of Industry and Academia covering a myriad of topics during their talks.

Our first speaker for the session was Dr. Elena Hensinger. A data scientist with a PhD in Computer Science and Machine Learning (University of Bristol), with experience as an entrepreneur and Computing educator. Moved from academia to education with the goal to bridge the digital divide in our society, support teachers with the Computing curriculum (which was very new at the time), and foster girls’ interests for STEM subjects.

In her talk, Elena spoke about how you can deal with real-life challenges with time-series data and discussed ways to provide value to your client, no matter the quality of data. Rightly naming her talk ,‘Spot the odd’ — Anomaly Detection in time-series data.

Following this, the next speaker for our event was Alexandra Craciun. An algorithm engineer at XMOS in Bristol. Her current focus is to develop efficient low-complexity algorithms for speech analysis.

Alexandra spoke to us about Machine learning in speech applications. In order to achieve human-like classification precision, the algorithms become more and more complex. Yet many of these algorithms need to run on low-power devices such as mobile phones, where power consumption becomes as critical as performance.In her talk she covered voice activity detection and discussed optimisation approaches for designing an efficient low-power algorithm to detect speech.

Our third speaker for the afternoon was Malvika Sharan. She is the community manager for The Turing Way project at The Alan Turing Institute in London. Malvika works with its diverse community of researchers, educators, funders and other stakeholders to develop resources and ways that can make data science accessible for a wider audience.

During her session, Malvika talked about lowering barriers for people (with a focus of women and other minority groups) to participate in online data science projects.

Her talk also introduced The Turing Way project that aims to bridge the gap between innovative data research techniques and best practices that make them accessible and comprehensible for everyone.

Our next speaker for the session was Dr Margriet Groenendijk. She is the Data Science Developer Advocacy focal at IBM. She has a background as a climate scientist researching large observational datasets of carbon uptake by forests and the output of global scale weather and climate models.

During her talk Margriet spoke about how bias can take root in machine learning algorithms and ways to overcome it. From the power of open source, to tools built to detect and remove bias in machine learning models. She also discussed how we can achieve AI fairness, robustness and explainability, and become part of the solution.

Following these amazing talks by our speakers we had a panel discussion where we discussed some of the below mentioned questions, which includes questions from the audience as well as the moderator.

“Could you advice on some of the key skills/training that is required for someone who is starting a career in Data Science”

“What resources do you use to stay up to date with the changes in the field”

“How much of Data Science is to be taught vs a craft to be practiced”

“How important is it for DataScientists to understand the underlying problem. Is it more important to enhance communication between Data Scientists and Engineer”

“What inspired the Turing Way project and its inclusive approach” .

The Women in Data Science (WiDS) initiative aims to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field.

We had a great time hosting our first ever Women in Data Science Bristol event, and look forward to participating in many more events in the years to come.

Getting started with Data Science

Developer Advocate at IBM

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