• Read more about Traditional Welcome Event - Julia Keefe Indigenous Big Band

• Read more about Data Science and Analytics Spring 2025 Open Project Kickoff

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.

• Read more about So You Want to Create a Survey: Unlocking Insights in Your Data

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. 

• Read more about So You Want to Create a Survey: Testing Validity and Internal Consistency

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. 

• Read more about Exploring Data Insights: A Beginner’s Journey into Hypothesis Testing

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! 

• Read more about Machine Learning and Deep Learning Series: Transformer-Based Models

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.

• Read more about Machine Learning and Deep Learning Series: Deep Learning and Neural Networks

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.

• Read more about Machine Learning and Deep Learning Series: Ensemble Methods and Model Optimization

Advanced Machine Learning - Ensemble Methods and Model Optimization, the fifth lab of the Machine Learning and Deep Learning Series covers advanced machine learning techniques to enhance model performance. Participants will dive into ensemble methods like random forests and gradient boosting algorithms such as XGBoost and LightGBM. The session will also introduce hyperparameter tuning strategies, including GridSearchCV to optimize model parameters effectively. To ensure robust model evaluation, participants will learn cross-validation techniques and how to interpret their results.

• Read more about Machine Learning and Deep Learning Series: Unsupervised Learning

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.

• Read more about Machine Learning and Deep Learning Series: Supervised Learning - Regression

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.