Sean Corp, Content Strategist
After obtaining a degree in biotechnology from the State University of Rio de Janeiro and a master’s degree in microbiology from the Federal University of Viçosa, Dione Garcia decided to pivot his focus toward computer science. Deciding to embark on a career transition is always a bit scary, and after so many years dedicated to science, a shift to the data side of things left Dione a bit uneasy.
While the 27-year-old resident of Brazil remained passionate about science and biology, his interest in computer science was spurred by what he saw as the intersection of public health and machine learning. Dione began a new academic pursuit in computer science at the State University of Rio de Janeiro and found Michigan Online’s Statistics with Python series a great complement to his residential education.
“I am currently an intern at Albert Einstein Hospital in the area of big data in the modeling sector,” according to Dione. “The course has helped me see data in a different way, and I believe that my work can help create machine learning models that improve public health in Brazil and in the world.”
Dione says he appreciated the “practical and didactic content” and praised the dynamic presentation and excellent instruction from lecturer Brenda Gunderson and Professor Kerby Shedden, both in the Department of Statistics, and Brady West, an associate professor at the Institute for Social Research.
Most importantly, Dione believes taking this three-course series will provide a leg up during an impending job search.
‘‘Facing a competitive job market, this course is a game-changer for me,” Dione says. “This course is putting me in position to become an excellent professional candidate with a respectable curriculum on my resume.”
Learning from experts from other cultures is satisfying, Dione says. “I’m growing as a professional with a more international scope. I hope to be able to show this to more Brazilian students so we can continue to evolve and expand our perspective.”
This specialization is designed to teach learners beginning and intermediate concepts of statistical analysis using the Python programming language. Learners will learn where data come from, what types of data can be collected, study data design, data management, and how to effectively carry out data exploration and visualization. They will be able to utilize data for estimation and assessing theories, construct confidence intervals, interpret inferential results, and apply more advanced statistical…