DS

Introduction to core data science concepts and skills, including computing environments, visualization, modeling, and bias analysis. Think like a Data Scientist as you engage through lectures, discussions, labs, and guest talks while applying learning in a guided semester-long project. Concludes with an independent project to reinforce and extend skills.

Will expose student to fundamental coding languages in data science. Python and R will be the primary focus of the course. Popular packages such as pandas and tidyverse will be covered in depth. Additionally, project management skills such as Git and Github will be covered.

This course will center on exposing students to contemporary pipelines for data analysis through a series of steadily escalating use cases. The course will begin with simple local database construction such as SQLite and evolve to cloud base systems such as AWS or Google Cloud. This progression will include topics such as data lakes and other non-SQL applications as appropriate.

The course is designed to not only teach students tools necessary to visualize data but also effective techniques for explaining data driven results with an emphasis on communicating statistical output in a manner that best represents the findings. Examples might include tailoring messages based on the audience or shaping visualizations to follow a story-line. Content on the development of interactive plots and dashboards will also be included.

Explores principles and applications of data ethics within a broader social framework that prioritizes conversations about policy, regulatory frameworks, accountability, transparency, and governance models. Will discuss who is responsible for doing responsible data science, question how our work shapes the world around us, and understand the impacts of big data on people and communities.

Will center on exposing students to contemporary pipelines for data analysis through a series of steadily escalating use cases. The course will begin with simple local database construction such as SQLite and foundation knowledge in terms of computational environments. The content will lay the groundwork for more advanced Systems Domain courses in the major.

Designed not only to teach students tools necessary to visualize data but also effective techniques for explaining data driven results with an emphasis on communicating statistical output in a manner that best represents the findings. Lays the foundation for more advanced topics in the Data Design domain. Content on the development of interactive plots and dashboards will also be included.

Covers the fundamentals of probability theory & stochastic processes. Become conversant in the tools of probability. Clearly describe & implement concepts related to random variables, properties of probability, distributions, expectations, moments, transformations, model fit, basic inference, sampling distributions, discrete & continuous time Markov chains, & Brownian motion. Illustrate most topics with both analytic & computational solutions.

This course exposes students to foundational knowledge in each of the four high level domain areas of data science (Value, Design, Analytics, Systems). This includes an emphasis on ethical issues surrounding the field of data science and how these issues originate and extend into society more broadly.

Moves deeper into current best practices around data engineering in industry. Topics will review basic data collection, ingestion, processing, and storage, moving beyond to data governance, security, pipeline orchestration, monitoring and maintenance, optimization, and documentation. Relies heavily on DevOps principles of automation, continuous improvement, and an understanding of the entire software/data lifecycle.

The data science project course will allow students to take the knowledge gained in each of the four required courses and apply them to a data driven problem. Students will work in groups and can either choose a project provided by SDS faculty or can propose a project for approval. Upon completion of the course students will be required to present their results and publish project content to an open forum.

Critique models and adapt them to a variety of data sets. Gain a deeper understanding of core ML concepts. Build towards neural networks (latent index models, more complex linear models with non-linear transformations of the data). Compare new methods to kNN, clustering, linear models from ML1 to discuss performance differences as complex and predictive power increases. How mathematical concepts are present in the models presented.

Understand Deep Learning covering neural networks, activation functions, and optimization algorithms. Gain experience with TensorFlow and PyTorch, mastering key techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). Explore transfer learning, reinforcement learning, and natural language processing (NLP), along with industry applications and ethical considerations.

Explores new models of database design: graph, vector, and ledger. These have become required infrastructure in service of social media (graph databases), Large Language Models (vector databases), and cryptocurrency (ledger databases). Will learn their basic operations with an eye toward other purposes as well as the key advantages and drawbacks of these data models. Center on student projects built using one of these databases.

Introduces complex interplay between technology, regulation, and data science and exposes regulatory realities confronting the field. Read and parse regulatory texts. Navigate the international technology regulatory landscape, identify key actors, and appreciate how rules governing different kinds of data, platforms, copyright and intellectual property, and digital services and markets shape data science and AI/ML development practices.

Reading and research under the direction of a faculty member. Students must obtain approval from a faculty advisor to approve and direct the independent study. Final approval by the Director of Undergraduate Programs is also required.

Covers foundations and applications of NLP with a focus on the most popular form of unstructured data - text. Convert source texts into structure-preserving analytical form and then apply information theory, NLP tools, and vector-based methods to explore language models, topic models, sentiment analyses, and GenAI techniques. Focus is on unsupervised methods to explore cognitive patterns in texts, with real-world examples and demonstrations.

Provides healthcare domain knowledge, healthcare data understanding, and data science methodologies to solve problems. Understand data types, models, and sources, including electronic health record data; health outcomes, quality, risk, and safety data; and unstructured data, such as clinical text data; biomedical sensor data; and biomedical image data. Querying with SQL, data visualization with Tableau, and analysis and prediction with Python.

Provides a foundation in discrete mathematics, data structures, algorithmic design and implementation, computational complexity, parallel computing, and data integrity and consistency. Case studies and exercises will be drawn from real-world examples (e.g., bioinformatics, public health, marketing, and security).

Provides an in-depth exploration of probabilistic and statistical methods used to understand, quantify, and manage uncertainty. Learn foundational concepts in probability and statistics, simulation techniques, and modern approaches to parameter estimation, decision theory, and hypothesis testing. Topics include parametric and nonparametric methods, Bayesian and frequentist paradigms, and applications of uncertainty in real-world problems. 

Genomics Foundations introduces core concepts in modern genomics and human genetics underlying computational biology and public health genomics. The course integrates key biological principles with quantitative reasoning and hands-on use of real genomics data and databases.

Trends in hardware and software for Big Data Systems and applications. Cover principles driving data infrastructures, which enabled the training of AI models on datasets (speech, sounds, images, video, languages) and may extend to structured data (text, images, time series). AI and machine learning practitioners build and deploy data science projects on Amazon Web Services unifying data science, data engineering, and application development.

This course focuses on making students more effective at identifying and designing AI use cases to create novel AI-powered products and services. Students will work with a variety of AI technologies across several projects. They will gain a deep understanding of design considerations for incorporating AI into products in ways that create value for users and businesses.

Covers the practice of data science, including communication, exploratory data analysis, and visualization. Also covered are the selection of algorithms to suit the problem to be solved, user needs, and data. Case studies will explore the impact of data science across different domains.

This course examines the ethical issues arising around big data and provides frameworks, context, concepts, and theories to help students think through and deal with the issues as they encounter them in their professional lives.