Machine learning impacts so many parts of our daily lives, whether it’s doing a Google search, using your smart watch, or getting movie recommendations. Healthcare providers and businesses are using it to gain insights to guide decision making. And it’s also being applied to solve some of the world’s toughest problems like climate change and food security. Several recent WiDS talks, workshops, and podcasts describe the exciting applications of machine learning today and offer skills development workshops for new and experienced data scientists.

​After watching the WiDS conference via livestream from Wisconsin, Sarah Dahl felt inspired and empowered to pivot her career to data science.

Tickets are selling quickly for the upcoming Women in Data Science (WiDS) Stanford conference, happening in-person at Stanford University and on livestream on March 8, 2023. We’re excited to announce some of our distinguished speakers addressing topics ranging from climate change to bioinformatics to authoritarian politics, and more. They include:

​With the massive amounts of data that are generated and collected today, data visualization is an invaluable tool to help people explore and understand what it all means. Data visualizations can be exploratory to help analyze the data and explanatory to present insights to a broader audience. Both art and science, data visualization turns information into images and helps people see patterns, trends, and outliers in large data sets. Here is a sampling of recent WiDS talks and workshops that delve into different aspects of data visualization.

The 8th Annual Women in Data Science (WiDS) Stanford conference will be held in-person at Stanford University and on livestream on March 8, 2023. WiDS Stanford is a technical conference featuring amazing women doing outstanding work in data science and related fields. This year’s conference will feature keynote addresses, technical talks, panel discussions, and time for networking. ​

Register now for the Women in Data Science (WiDS) Stanford conference 2023, a technical conference featuring outstanding women discussing their exceptional work in data science and related fields, in a wide variety of research domains. Everyone is welcome and encouraged to attend. The conference will take place in-person, at Stanford University. Don’t wait to get your tickets for the in-person event, as they will sell out quickly! ​

​AI and machine learning are increasingly being used across industry and government to make decisions impacting many parts of our lives. These technologies could determine who gets a job interview, what products are advertised to different audiences, or what government resources are allocated to different populations. Bias can become embedded in the development of AI systems either through the data and/or the development and evaluation of algorithms. This can result in inaccurate predictions that can significantly impact people’s lives. Several WiDS talks describe how bias in machine learning can impact everything from online ads to search recommendations to bus routes.

While there have been amazing achievements with machine learning in recent years, reproducing results for state-of-the-art deep learning methods is seldom straightforward. Three leading data scientists share their views at recent WIDS conferences on the importance of establishing structures, standards and best practices to guide us towards consistently producing high quality science and reliable findings.

​Data science, machine learning and AI have the potential to have a major impact on the healthcare industry. In several WiDS Worldwide conference panels and technical talks, experts describe the challenges and opportunities for applying data science, machine learning and AI to healthcare research and patient care.

​The pandemic has changed the way that people think about their lives, and their work. A recent survey conducted by Gartner suggests that instead of using the term ‘great resignation’, organizations need to think about the ‘great reflection’, as employees are seeking more purpose in their work.

Whether it’s asking Siri about the weather, Google for directions, or a customer service bot about your bank account, Natural Language Processing (NLP) is an expected capability across applications today. In WiDS Conference keynotes, workshops and technical talks, experts explore various uses for NLP and how it is shaping how people interact with computers.

Financial services is an industry built on data and not surprisingly data science plays a pivotal role in helping financial firms assess risk, detect fraud, make profitable decisions, and improve the customer experience. The Women in Data Science (WiDS) Conference and podcast series shares the perspectives of leading experts on the vital role of data science in financial services.

Tolúlọpẹ́ Ògúnrẹ̀mí’s involvement with WiDS began as a WiDS London ambassador, working with Stanford prior to getting accepted to the Stanford Computer Science (CS) PhD program. She has been able to combine a love of languages with her ability in mathematics and computer science and is now focused on Natural Language Processing (NLP) for low-resource languages, particularly in sub-Saharan African languages. Tolúlọpẹ́ came full circle with WiDS, attending and volunteering for the WiDS Worldwide event in 2022.

On March 7, 2022 more than 10,000 people from 99 countries participated in the Women in Data Science (WiDS) hybrid conference held in-person at Stanford University, and online in a virtual event platform and via livestream. The in-person conference included keynote addresses, technical vision talks, panel discussions, and a fireside chat. The online audience could view the in-person conference broadcast, while the virtual event platform attendees also attended workshops, meet-the-speaker sessions, a career expo with our sponsors, and one-on-one networking.

The 7th Annual Women in Data Science (WiDS) Worldwide conference will be a hybrid conference, held in-person at Stanford University and online on March 7, 2022. WiDS Worldwide is a technical conference featuring outstanding women doing outstanding work in data science and related fields. This year’s conference will feature keynote addresses, several technical talks, panel discussions, and time for networking. The simultaneous online conference will include a broadcast of the conference at Stanford, in addition to workshops, speaker interviews, a career expo, and networking.

Join us online on March 7, 2022, for the Women in Data Science (WiDS) Worldwide conference, a technical conference featuring outstanding women doing exceptional work in data science and related fields, in a wide variety of domains. Everyone is welcome and encouraged to attend.

​Manvi Madan, a Data Analytics Specialist for the Ports of Auckland, New Zealand says WiDS has changed her perception about what’s possible for women in data science and has inspired her to become a leader in the space.

​Cindy Catherine Orozco Bohorquez started her PhD at Stanford’s Institute for Computational and Mathematical Engineering (ICME) the same year that WiDS launched its first conference. Her involvement with WiDS started as a shy volunteer and evolved to fulfilling her dream of becoming a speaker at global and regional WiDS conferences, a WiDS workshop instructor, and a member of a thriving community of women data scientists.

The 7th Annual Women in Data Science (WiDS) Worldwide conference will be a hybrid conference, held in-person at Stanford University and online on March 7, 2022. WiDS Worldwide is a technical conference featuring outstanding women doing outstanding work in data science and related fields. This year’s conference will feature keynote addresses, several technical talks, panel discussions, and time for networking. The simultaneous online conference will include a broadcast of the conference at Stanford, in addition to workshops, speaker interviews, a career expo, and networking.

Data science is a crucial tool to quantify, predict and communicate about the impact of climate change. For example, a recent study used machine learning to analyze over 100,000 weather events that could be linked to global warming and discovered that 80 percent of the earth’s land has been adversely impacted and at least 85 percent of the world’s population has been affected by extreme weather events caused by climate change.

The Women in Data Science (WiDS) Conference and podcast series has been showcasing leading women data science experts who are using data science to help us understand the impact and potential solutions to combat climate change. Our WiDS Datathon 2022 dataset will also be focused on the impacts of climate change.
 
Here are some recent podcasts, panels, and talks from our WiDS conferences that address sustainability.

Register now for the Women in Data Science (WiDS) Worldwide conference 2022, a technical conference featuring outstanding women discussing their exceptional work in data science and related fields, in a wide variety of research domains. All genders are welcome and encouraged to attend!​

Data scientists work with large data sets that require computational analysis to gain insights and knowledge that often drive important decisions within organizations in industry, academia, non-profits, and government. In order for these insights to have the desired impact, data scientists need to communicate clearly to be well--and quickly--understood. Data visualization allows data scientists the ability to provide unique views into the data and about the data, turning data sets into insights at-a-glance. ​

We live in an era of big data with data sets that require computational analysis to gain insights and knowledge. The volume of big data has been increasing steadily, and will only continue to climb. Since we started the WiDS initiative in 2015, Statistica estimates that the volume of data has increased from 15.5 to 74 zetabytes, and they forecast that data volume will double again by 2024.

Yet with all of this data, one of the biggest challenges that data scientists and researchers face is dealing with missing data. In some cases, the missing data is due to not readily having access to the data sets that are required to perform the analysis, while other cases involve data sets that are incomplete and not uniformly populated.

Data science is being applied in a growing number of domains that affect everyone’s lives, in healthcare, financial services, agriculture, resource management, and beyond.  While data science has huge potential for good, there are also unintended consequences. Data scientists need to take steps  to mitigate as many unintended consequences as they can using Responsible Data Science — a set of policies, procedures, and best practices to ensure algorithmic fairness, transparency, and explainability.