DATA SCIENCE & ARTIFICIAL INTELLIGENCE (DSAI)
DSAI-101R AI FOR EVERYONE (3 Credits)
Artificial Intelligence is changing the world. It is changing jobs, creating them, and even replacing them (but less than you think). More than ever before, companies need employees who can use AI tools to solve problems creatively and responsibly. This non-technical AI crash course builds the foundational skills needed to do that and is designed to be valuable to anyone. Learn how to distinguish problems that AI is useful for, master prompt engineering to improve outputs, detect AI-generated output, analyze ethics and privacy, and stay up-to-date on one of the most transformative technologies of our lifetimes.
DSAI-107 INTRO TO DATA & DATA ANALYTICS (3 Credits)
This course introduces students to core concepts and the current landscape of Data Science, Machine Learning, and Artificial Intelligence. Students explore how these fields intersect through a shared data lifecycle, including problem definition, data collection, preparation, analysis, and communication of results. Topics include types of data, sampling methods, web scraping, handling missing data, basic data storage, descriptive statistics, data visualization, dashboards, and ethical use of data. While a range of tools and technologies are discussed, this is not a programming course, and no prior programming experience is required.
DSAI-201 DATA SCIENCE TOOLKIT (3 Credits)
An introduction to traditional methods of Data Science with an emphasis on the relevant mathematics, statistics and theory. Topics to include regression, clustering, discriminant analysis, naive Bayes, variable selection, decision trees, and time series.
Prerequisite(s): TAKE CS-130
Corequisite(s): TAKE DSL-201
DSAI-201R DATA SCIENCE II (4 Credits)
This course takes a deep dive into machine learning models, natural language processing, and time series in Python. The following topics are covered: time series, regression, unsupervised learning, deep learning, feature engineering, and ethical implications. Students will use both unstructured and structured data to build machine learning models and use the models to create actionable and ethical predictions.
Prerequisite(s): TAKE CS-130
DSAI-211R AI FOR DECISION MAKING (3 Credits)
Artificial Intelligence decisions are often only as good as the person asking the question. In this course, youll learn how to ask the right ones and increase the productivity and innovation you can achieve with AI. Create better prompts, compare and contrast strengths and limitations, evaluate outcomes, and by the end of this course, understand and leverage the power of AI for decision-making across any discipline, opening up new career paths and personal growth.
DSAI-212R AI FOR CREATIVITY & DESIGN (3 Credits)
Generative AI has introduced a new paradigm of AI: the co-creator. Top professionals and companies use AI to improve productivity and creativity every day, and in this course, youll learn the iterative prompting, search, and functional evaluation metrics powering these uses. By the end of this course, youll integrate AI tools into a variety of creative skill sets, and your own projects, building new avenues for creativity in your career.
Prerequisite(s): TAKE DSAI-101R
DSAI-301 EMERGING TRENDS IN DATA SCIENCE (3 Credits)
An introduction to the established Data Science tool kit including clustering, the many faces of regression, random forests, and support vector machines. An optional introduction to neural nets and deep learning. An introduction to the presentation of Data Science results including proper visualization and storytelling. The course consists of three lecture hours and one two-hour laboratory per week.
Corequisite(s): TAKE DSAI-201 & DSL-201, OR TAKE DSAI-201RTAKE DSL-301
DSAI-301R AI & MACHINE LEARNING (4 Credits)
This course is a technical approach to cutting-edge AI methods. Students will produce machine learning models to solve business problems, evaluate modern AI use cases (such as computer vision) and adapt Large Language Models (LLMs) for specific applications.
Corequisite(s): TAKE DSAI-201 & DSL-201, OR TAKE DSAI-201RTAKE DSL-301
DSAI-341R PREDICITIVE MODELING IN AI (4 Credits)
This course brings the predictive power of AI to your toolbox. You'll discover how to analyze, interpret, and forecast complex data using AI tools. Learn through hands-on activities and practice techniques like regression analysis and neural networks. You'll also explore how to fill in missing data and estimate your confidence in your predictions. By the end of this course, you'll have in-demand skills for your career and be ready to take on more advanced studies.
Prerequisite(s): TAKE CS-130
DSAI-342R PRESCRIPTIVE AI (4 Credits)
Prescriptive AI teaches you the highest-value technical AI skills available. Youll use the advanced techniques of optimization, evolutionary computation, surrogate modeling, and agent building, helping you use AI for its true superpower: faster, better business decisions. Through real-world challenges and hands-on projects in decision-making, robotics, and more, youll be able to frame problems and train models that make you a desirable hire in any industry.
DSAI-351 DATA SCIENCE CASE STUDIES (3 Credits)
This course explores the challenges and opportunities in the evolving field of Data Science by evaluating case studies across the entire data science pipeline. Classes are interspersed with student presentations and discussions around best practices, technical implementation, and ethical concerns. Based on the reviewed case studies, students will analyze the future of Data Science and their role within the field.
DSL-201 DATA SCIENCE TOOLKIT LAB (1 Credit)
An introduction to traditional methods of Data Science with an emphasis on the relevant mathematics, statistics and theory. Topics to include regression, clustering, discriminant analysis, naive Bayes, variable selection, decision trees, and time series.
Prerequisite(s): TAKE CS-130 CSL-130
Corequisite(s): TAKE DS-201
DSL-301 EMERGING TRENDS IN DATA SCIENCE LAB (1 Credit)
An introduction to the established Data Science tool kit including clustering, the many faces of regression, random forests, and support vector machines. An optional introduction to neural nets and deep learning. An introduction to the presentation of Data Science results including proper visualization and storytelling. The course consists of three lecture hours and one two-hour laboratory per week.
Prerequisite(s): TAKE DS-201 DSL-201
Corequisite(s): TAKE DS-301