8 courses with the subject DS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
DS 101Introduction to Data Science
The world is increasingly data-driven. This course provides a practical introduction to the burgeoning field of data science and provides students with an understanding of the fundamental concepts in data science. The course also introduces Python programming within the context of data science. The course is both analytical and technical, however no prior programming experience or statistical background is assumed. Prerequisite: none.
In this course, students will learn how to use Python and popular 3rd-party tools to store and manipulate large quantities of data and prepare it for further analysis. Typically, raw data received from a source has numerous issues that must be addressed before it can be used reliably, such as missing data, outlier values, and incompatible formats. Students will receive hands-on experience with the tools and techniques used to organize, clean, transform, and visualize data in a variety of formats and modalities, including tabular data, unstructured text, markup languages, 2D images, etc. Python will be used extensively throughout the course, along with popular software packages such as NumPy, pandas, and Matplotlib. While data manipulation is the primary focus, students will also further develop their general Python programming skills that are useful across all domains.
In this course, students will learn how to apply popular tools and techniques to solve practical machine learning problems using Python. Students will use scikit-learn to explore the entire ML workflow - from data preprocessing and feature engineering, to model selection and hyperparameter tuning, to model evaluation and deployment. The most popular supervised ML models will be discussed, including linear models, support vector machines, decision trees and forests, neural networks, and ensemble models. Additional topics will be covered as time permits, including unsupervised learning and clustering, Natural Language Processing (NLP), AutoML, and dimensionality reduction. Students are expected to have a strong grasp of Python programming.
This course will teach students the algorithms and mathematics underlying the most popular machine learning models. Students will study machine learning within the broad framework of optimization and loss minimization using techniques such as maximum likelihood estimation and gradient descent via backpropagation. The major theoretical machine learning concepts will be introduced, such as model fit and regularization, generalization, and bias-variance tradeoff. The ML models studied will include k-nearest neighbors, decision trees and forests, linear regression and logistic regression, hard- and soft-margin support vector machines, and neural networks. Students will use Python to implement basic versions of ML models and optimization algorithms by hand. As time permits, additional topics may include ensemble methods, bagging and boosting, dimensionality reduction, the use of kernels, and reinforcement learning. Prerequisite: CS 301 and MATH 312
The explosive growth of data being generated over the past several years requires data scientists to be able to handle ever larger and more diverse datasets efficiently. In this course, students will learn how to use the services of cloud platforms such as Amazon Web Services (AWS) and Google Cloud to address large-scale data analysis and machine learning tasks. Students will be introduced to traditional “Big Data” tools like Apache Spark and Hadoop, data warehousing technologies like Google BigQuery, enterprise-scale “NoSQL” database platforms like MongoDB and DynamoDB, and cloud-based machine learning platforms like Amazon Sagemaker and Google Vertex AI. These and 206
The Data Science Practicum is a two-term capstone experience for students in the data science program. The practicum is an opportunity for students to directly experience the work of a data scientist by applying their analytical, modeling, machine learning, programming, and visualization skills to solve a problem using real-world datasets. The first term focuses on framing a problem and the related research question(s), then having these guide the preliminary design and the supporting analysis and data acquisition necessary for creating a final design. The second term focuses on implementation, verification & validation, and the creation of a plan of action for addressing the problem. Each student will have an advisor for their project to lend their knowledge and expertise, and also to provide guidance in the technical and managerial decisions required by the project. Students will have the opportunity to work on projects with faculty from a variety of disciplines, as well as with external companies and organizations. The practicum experience culminates in a public presentation. Prerequisite: DS 311
The Data Science Practicum is a two-term capstone experience for students in the data science program. The practicum is an opportunity for students to directly experience the work of a data scientist by applying their analytical, modeling, machine learning, programming, and visualization skills to solve a problem using real-world datasets. The first term focuses on framing a problem and the related research question(s), then having these guide the preliminary design and the supporting analysis and data acquisition necessary for creating a final design. The second term focuses on implementation, verification & validation, and the creation of a plan of action for addressing the problem. Each student will have an advisor for their project to lend their knowledge and expertise, and also to provide guidance in the technical and managerial decisions required by the project. Students will have the opportunity to work on projects with faculty from a variety of disciplines, as well as with external companies and organizations. The practicum experience culminates in a public presentation. Prerequisite: DS 411 Exercise Science (Kinesiology) Courses
