Past student reviews · Cecile Ane, Mitchell Paukner
Cecile Ane’s group project made the class feel like 4 credits, and her homework was not enough for preparation. Mitchell Paukner’s tests accurately reflected lecture content, though students found the lectures exhausting.
Data Science Computing Project focuses on tools for collecting, managing, and analyzing large data sets using Linux, R, and distributed computing, with team-based projects involving research, writing, and presentations.
STAT 424 covers the design and analysis of experiments, including randomization, blocking, factorial designs, and response surfaces, with a focus on engineering applications and A/B testing.
One student reported that the course required reading through limited lecture slides, with a midterm weighted at 40% and a final at 50% of the total grade.
Covers statistical data visualization techniques in data science, including data prep, EDA, spatial/graph data, dimensionality reduction, and interactive models.
Introduction to algorithms and applications of classification and regression trees, covering recursive partitioning, pruning, cross-validation, and various regression tree models.
Introduction to Machine Learning and Statistical Pattern Classification covering classification, regression, clustering, dimensionality reduction, and model evaluation using Python.
Covers deep neural networks and generative models with statistical connections, emphasizing practical implementation via open-source computing for supervised learning and dataset synthesis.
One student noted the semester had little workload and was manageable if concepts were understood, while another described exams as easy. Difficulty ratings varied, ranging from 2 to 5.
Applied Multivariate Analysis covers dimension reduction, clustering, and classification using R, emphasizing conceptual underpinnings and core models for complex datasets.
Statistical Methods I provides a thorough grounding in modern statistical methods, including probability concepts, statistical inference, and data analysis using modern models, methods, and software.
Students reported having to do all the heavy lifting themselves, noting that the teaching style was unhelpful and office hours were not a viable resource for getting assistance.
Develops tools for collecting, managing, and analyzing large data sets using Linux, R, distributed computing, editors, and git/github through team-based data analysis projects.
Computing in Data Science and Statistics surveys popular tools and frameworks for data scientists, covering Python, machine learning, data visualization, web scraping, and MapReduce.
Covers statistical inference methods including parameter estimation, Bayesian approaches, hypothesis testing, ANOVA, and linear regression for data science applications.
Computational Statistics covers statistical inference via computation, including data reduction, parameter estimation, hypothesis testing, prediction, modeling, and Bayesian/non-Bayesian inference.
Advanced Financial Statistics covers statistical theory and methodology for modern financial data, including stochastic models, inference, and learning applications.
Students found the class required significantly more work than expected for an elementary course, with some describing the grading criteria as unclear and confusing.
Examines key themes, issues, and scholarship in the interdisciplinary field of science and technology studies, focusing on how disciplinary perspectives shape questions, methods, and theory.
Current Topics in Science and Technology Studies covers key themes, issues, and scholarship in STS, featuring faculty and speakers from social sciences, humanities, and physical and life sciences.