The Unit for Data Science and Analytics at the ASU Library is excited to announce the return of the Volunteer Open Projects. There are two Spring 2025 Open Projects to choose from this semester. The Volunteer Open Projects are an opportunity to get involved in real-world issues in which to collaborate, learn and expand your knowledge and expertise in data science and analytics, regardless of your discipline or level of experience.
Are you ready to take your survey data skills to the next level? Join us for part two of our survey-focused Open Lab series, where we dive into some useful methods to analyze survey data and turn raw responses into actionable insights.
Are you looking to create surveys that deliver meaningful and reliable insights? Whether you are a student, researcher or professional, understanding survey design is essential for success in fields like psychology, education, social sciences, user experience design and healthcare.
Are you ready to dive into the world of data-driven decision-making? Join the Unit for Data Science and Analytics for an engaging and interactive Open Lab designed to demystify hypothesis testing for students and professionals across disciplines. Whether you’re an aspiring data scientist, a social scientist or simply curious about working with data, this session is for you!
Transformer-Based Models, the final lab of the Machine Learning and Deep Learning Series focuses on new developments in artificial intelligence, specifically transformer-based models. Participants will learn fundamental components of transformers and how they differ from traditional neural networks. The session will cover pretrained models like BERT, fine-tuning, and transfer learning, providing practical insights into their application. Participants will also explore the role of Large Language Models (LLMs) in AI advancements, the resurgence of RNNs, and discuss the future of AI.
Deep Learning and Neural Networks, the sixth lab of the Machine Learning and Deep Learning Series introduces participants to the fundamentals of deep learning and neural networks, focusing on their architecture and functionality. Attendees will explore how neural networks work, from input layers to output layers, and the role of activation functions. The session will also cover backpropagation, optimization techniques like SGD and Adam. Participants will gain hands-on experience by building a simple feedforward neural network for classification tasks.
Take a break and join us at the Downtown Phoenix campus Library for a game afternoon. Bring your friends and play games in the library. No need to sign-up, just show up and play!
The library is located on the lower level of the University Center Building and will be held from 3 - 5.
See you there!
Unsupervised Learning, the fourth lab of the Machine Learning and Deep Learning Series, participants will learn to work with unlabeled data using unsupervised learning techniques. The lab will explore clustering methods, including k-Means and hierarchical clustering, to uncover patterns in datasets. Participants will also learn dimensionality reduction techniques like Principal Component Analysis (PCA) to simplify high-dimensional datasets.
Regression, the third lab of the Machine Learning (ML) and Deep Learning Series will cover ML models for regression tasks. The session begins with the fundamentals of linear regression and progresses to polynomial regression and regularization methods such as Lasso and Ridge regression. In addition, decision trees, support vector machines and K-nearest neighbors (KNN) will be introduced to demonstrate their effectiveness in regression tasks.
Classification, the second lab of the Machine Learning and Deep Learning Series, shifts the focus to classification models for predicting categorical outcomes. Participants will explore a range of classification algorithms, starting from logistic regression and decision trees to more advanced models like support vector machines (SVMs), and k-nearest neighbors (KNN). Naive Bayes and other classifiers will also be covered to provide a comprehensive understanding of classification methods.