DATA SCIENCE (DATA)

This course presents relevant techniques and tools for solving real-world data science problems. Students will learn to apply industry standard libraries and services to build and host data science pipelines in the cloud. Data processing steps in the pipeline include fetching data from SQL databases and the web (via application programming interfaces and web scraping), cleaning and exploring data, applying statistical analyses, training and deploying machine learning models, and presenting insights with interactive dashboarding.

This course covers foundational math and statistics topics for data science. Statistics topics include probability, statistical inference, prediction techniques (e.g., classification, regression), and evaluating the performance of predictive models. Additional topics include study design and evaluation, as well as an introduction to linear algebra and optimization.

In-depth examination of the ethical foundations concerning data science and algorithms as well as in-depth examination of the moral problems that arise from the application of such ethical frameworks as utilitarianism, Kantian ethics, and virtue ethics in the context of data science and AI. Topics include: privacy and fairness in data gathering and data (pre)processing; algorithm bias and mitigation of bias amplification, risk, harm and injustice; transparency and explainability in algorithmic decision-making, machine learning, and AI models; identifying responsibility gaps and holding of power in models; ethical evaluation and ethical deployment of data models, including AI models; reflection on and awareness of adverse social consequences of widespread use of machine learning and AI; and critical development of a human-centered and value-focused approach to data science, machine learning, and generative AI.

This course provides a technical introduction to neural networks and their use in building models for classification and regression. Different types of neural network architectures are introduced including recurrent neural networks, convolution neural networks, transformers and attention, deep learning, and relevant application areas such as image classification, natural language processing, and generative modeling.

This course covers topics in database management with applications for data science. Topics include database data models, logical database design, physical database design, query languages, and analytical extensions to database systems. Integration of database systems with popular programming environments and libraries is also discussed, including both storage and in-memory open data formats. The course includes a variety of hands-on assignments and projects.

In this course, students will learn a variety of techniques and tools for effectively communicating data analysis questions, results, and insights to a range of audiences. The course will cover techniques related to data storytelling, data visualization, interactive dashboarding, digital portfolio design and development, technical report writing, and technical presentation skills for data science. Students will also learn to effectively use modern tools related to data storytelling and visualization, interactive dashboarding, and project hosting.

This course covers tools and techniques used in applying statistical and machine learning approaches to real-world data sets. Through hands-on assignments and projects, students learn relevant architectures, programming models, and tools related to data modeling and storage, extract-transform-load (ETL) processes, data warehousing, and data pipeline creation and management. The course also explores scalable, distributed, and cloud-based approaches used in data-intensive applications for accessing, filtering, clustering, and classifying data.

This course is the first course in a two-course Data Science Capstone sequence. The Capstone sequence provides an overview of how to design a data science system and deploy the system into a production environment. Students complete a large project that involves researching a data science problem, proposing a solution to the problem, implementing the solution, and deploying the solution as a hosted web application. Emphasis is placed on working with web-based application programming interfaces, gathering and processing data, researching and implementing common machine algorithms for data mining and classification, and securely deploying models in the cloud.

A continuation of the project started in DATA 583 Data Science Capstone I.

These seminars cover topics of importance to tech professionals. This course may be repeated for credit with a change in subject matter. On sufficient demand.