Expanding the involvement of women in Science, Technology, Engineering, and Mathematics (STEM) across Latin America is crucial for economic advancement, social equity, and global competitiveness; however, these efforts have proven to be challenging. Women in the region are underrepresented in STEM10 and even more so in leadership positions.17,18 The limited availability of current information and the difficulties associated with obtaining reliable data to mitigate gender disparities create difficulties in implementing policies to reduce the gender gap in STEM. Researchers, organizations, and policymakers working to reduce the gender gap need access to dependable data to understand the root causes of gender disparities, promote evidence-based interventions, and increase accountability and transparency. In the quest for solutions to these challenges, an international research network between Bolivia, Brazil, and Peru, "Equality in Leadership for Latin America STEM" (ELLAS), emerged in 2022.6

Gartner recently stated that all industries and levels of government agree the top three game-changing technologies today are AI/machine learning, data analytics/predictive analytics and cloud technologies. However, there are some primary sticking points when it comes to innovation in these areas. Government organizations continue to encounter challenges when trying to pursue these initiatives due to complex security and compliance requirements, poor scalability of legacy IT infrastructure, and perceived risks associated with cloud and IT modernization efforts. How can these challenges be addressed? One solution to these concerns is being addressed by Databricks, a leader in Unified Analytics and founded by the original creators of Apache Spark, and Booz Allen Hamilton, a leading provider of machine learning services to the U.S. Federal Government, by announcing the two companies are teaming up to provide a Unified Analytics solution to meet the U.S. government's mission requirements on any data, anywhere.

I'm proud and excited to share that Kaggle is joining Google Cloud! Seven years ago, we launched our first ever competition, to predict the voting patterns for the Eurovision Song Contest. It was won by Jure Zbontar, who beat 21 teams to win the $1000 prize. Since then, the Kaggle community has used machine learning to grade high school essays, diagnose heart failure and increase the discovery significance of the Higgs Boson. Geoff Hinton and George Dahl showed the power of deep neural networks on a Merck competition and Tianqi Chen used Kaggle Kernels to introduce the community to XGBoost. Kaggle profiles have become a well recognized credential, with community members landing jobs at companies ranging from DeepMind to Walmart.

Whether you call it soccer or football, this sport is the world's favorite to watch and play. Thanks to Hugo Mathien who compiled, cleaned, and shared a dataset of stats on European professional football on Kaggle, it can become a data scientist's favorite playground, too. Among other data points, the database includes 25,000 matches from 2008 to 2011, 10,000 players from 11 countries, and betting odds from up to 10 providers. This impressive collection of data allows Kagglers test their machine learning techniques by building models predicting match outcomes (can you beat the bookies?) and find insights through data visualization and storytelling. In this interview, Hugo explains how he pulled data from a number of sources using Python's Scrapy and overcame data integrity issues with manual effort to build this incredible dataset for Kagglers to enjoy.