Learning to avoid sources of bias and quantifying and minimizing its impact allow the realization of the promise of machine learning and artificial intelligence to benefit all, David Corliss says.
SAS
Curiosity Cup Offers Analytics Skills Competition
The Curiosity Cup is a global SAS student competition that brings teams together to grow their knowledge of data science and analytics. Registration opens in October.
My ASA Story: Richard Zink, Biostatistician and Podcaster
Zink is the co-host of ASA Biopharm’s Podcast and says volunteering has been good for his career and allowed him to help the next generation of statisticians and data scientists.
Nan Laird Honored with International Prize in Statistics
Louise Ryan shares an adapted version of the nomination statement that helped elect Nan Laird as this year’s International Statistics Prize winner.
Richard L. Anderson
Dick coauthored the well-known book, Statistical Theory in Research, one of the first books on linear models published. His research topics varied greatly. Early in his career, he published articles on econometrics. He was an expert on regression topics and he advanced the variance component estimation topic significantly.
North Carolina Chapter Gives Machine Learning Webinar
The North Carolina Chapter continued its professional webinar series with a machine learning webinar by Funda Gunes, a principal machine learning developer at SAS.
Arizona Chapter Members Participate in DataFest
The Arizona Chapter concluded its first ASA DataFest competition on March 25 with excellent participation from students of Arizona State University’s Tempe and West campuses.
Data Resources for Data for Good Researchers
In this month’s column, David Corliss lists a variety of places researchers can go to find good data.
The Paradox of Choice: Statistical Software Packages
With such a vast array of similar programs available, many students feel uneasy when thinking about which programs they would be asked to use in a professional setting.
Engaging Program Planned for JSM
The Section for Statistical Programmers and Analysts’ program includes presentations and discussions focusing on R and SAS programming, Bayesian methodology, and CDISC, along with a variety of other topics of interest to statistical programmers.