Swetha (she/her) is a 5th Year Master of Information and Data Science student at the School of Information, with experience in Cognitive Science, Psychology research, and product management. Her research interests include building ethical, transparent AI and the impacts of technologies (specifically, mass media, surveillance, and algorithms of bias) on longitudinal behavioral health. She is happy to help with questions on Python, R, SQL, machine learning, neural networks, statistical analysis, and research design!
Eileen is currently a first year Information Management and Systems student committed to studying human-centered design for the utility and usability of healthcare systems. She spent the last few years working in genomic research program analysis and management at the National Human Genome Research Institute. Prior to that, Eileen attended Georgetown University where she studied biology and studio art. During this time, she performed research on water contaminants in an analytical chemistry lab as well as research on estrogen mimicking compound effects on Zebrafish in a brain...
Hello! I’m a Sociology Ph.D. student and National Science Foundation Graduate Research Fellow. My research focuses on children of incarcerated parents as collateral consequences of mass incarceration and the criminal justice system. My two current projects examine: 1) the traumatic impact of viewing a parental arrest on a child in their understanding of criminality, law enforcement, and delinquency outcomes and 2) the compounding effect of parental incarceration and the child welfare system on children. Before joining Berkeley, I was a research assistant at Columbia University...
One of the frequent ways people can run into random numbers is through their research. We often hear the term “random sample,” or a “randomized” assignment to control. Or, sometimes, we can randomly select a certain number of rows or columns from data to perform an analysis on a representative snapshot of the data. Additionally, for many of us from a natural science or engineering background, random numbers are often used in simulations or optimization models. Given the wide variety of uses for random numbers in Data Science, I thought it would be interesting to take an...
When creating a survey for an audience it is important to make your survey tool accessible, succinct, and understandable. The following 7 step guide gives you an important tool kit to improve your survey response rates and completion rates, and give you clear results.
1: Set the stage on your intro page.
Inform the respondent of the purpose of the survey on the title page of your survey. The purpose of this page is to build trust with the audience and provide the necessary information...
Anushah's background is in history and economics and she is interested in questions of how technological and social trends inform one another. She uses the models and methods of these disciplines - theories of technological change, an eye towards historical contingencies, and familiarity with programming tools - to undergird her work. In the past she's studied how internet users make sense of barriers they encounter when accessing the web, how cellular communications alter the nature of village life in the Philippines, and how the South Asian diaspora finds...
If you take a quantitative methods course here at Berkeley, chances are that you will learn how to perform a bootstrap. As an introductory data science instructor, it’s one of my favorite topics to teach, not just because it’s a powerful and useful tool, but also because it’s incredibly intuitive. In short, the bootstrap -- also known as resampling with replacement -- allows us to generate a distribution of sample statistics given only a single sample, estimating sampling error.The name of this method...
Amanda is a PhD candidate in the statistics department at Berkeley. Her research focuses on causal inference with applications in education, political science and sports. Previously she earned her Bachelor’s degree in mathematics and statistics, with a secondary in computer science, from Harvard.