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.

​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.

​Julia Olivieri, assistant professor of computer science at the University of the Pacific, has been instrumental to the development of the WiDS Workshops while launching her own teaching career. The WiDS Global team has been grateful to have Julia serve on the WiDS Workshop committee for the past year.

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.

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.