DATA 0080. Data, Ethics and Society.

A course on the social, political, and philosophical issues raised by the theory and practice of data science. Explores how data science is transforming not only our sense of science and scientific knowledge, but our sense of ourselves and our communities and our commitments concerning human affairs and institutions generally. Students will examine the field of data science in light of perspectives provided by the philosophy of science and technology, the sociology of knowledge, and science studies, and explore the consequences of data science for life in the first half of the 21st century. Fulfills requirement for Certificate in Data Fluency

DATA 0200. Data Science Fluency.

As data science becomes more visible, are you curious about its unique amalgamation of computer programming, statistics, and visualizing or storytelling? Are you wondering how these areas fit together and what a data scientist does? This course offers all students regardless of background the opportunity for hands-on data science experience, following a data science process from an initial research question, through data analysis, to the storytelling of the data. Along the way, you will learn about the ethical considerations of working with data, and become more aware of societal impacts of data science. Course does not count toward CS concentration requirements.

DATA 0250. Applied Statistics in Python.

As more students engage in data science there is a need to provide guidance on conducting basic statistical analysis in Python. This course will provide a non-specialist approach to applied statistics, specifically linear models Python. Students will learn how to conduct linear modules using the Statsmodels package in Python. Students should have good working knowledge of descriptive statistics (equivalent to a high school AP level). Python coding experience is helpful but not required. Student learning would be assessed through hands-on Python coding activities and written interpretation of statistical reports. Students from the humanities and social sciences are particularly encouraged to enroll in this course. Pre-req- Basic High School A.P. knowledge of statistics.

DATA 1010. Probability, Statistics, and Machine Learning.

An introduction to the mathematical methods of data science through a combination of computational exploration, visualization, and theory. Students will learn scientific computing basics, topics in numerical linear algebra, mathematical probability (probability spaces, expectation, conditioning, common distributions, law of large numbers and the central limit theorem), statistics (point estimation, confidence intervals, hypothesis testing, maximum likelihood estimation, density estimation, bootstrapping, and cross-validation), and machine learning (regression, classification, and dimensionality reduction, including neural networks, principal component analysis, and unsupervised learning).

DATA 1030. Hands-on Data Science.

Develops all aspects of the machine learning pipeline: data acquisition and cleaning, handling missing data, exploratory data analysis, visualization, feature engineering, modeling, interpretation, presentation in the context of real-world datasets. Fundamental considerations for data analysis are emphasized (the bias-variance tradeoff, training, validation, testing). Classical models and techniques for classification and regression are included (linear and logistic regression with regularization, support vector machines, decision trees, random forests, XGBoost). Uses the Python data science ecosystem (e.g., sklearn, pandas, matplotlib). Prerequisites: Python coding experience suggested. Familiarity with linear algebra and calculus. A course equivalent to CSCI 0050, CSCI 0150 or CSCI 0170 is strongly recommended.

DATA 1050. Data Engineering.

The course will cover the storage, retrieval, and management of various types of data and the computing infrastructure (such as various types of databases and data structures) and algorithmic techniques (such as searching and sorting algorithms) and query languages (such as SQL) for interacting with data, both in the context of transaction processing (OLTP) and analytical processing (OLAP). Students will be introduced to measures for evaluating the efficacy of different techniques for interacting with data (such as ‘Big-Oh’ measure of complexity and the number of I/O operations) and various types of indexes for the efficient retrieval of data. The course will also cover several components of the Hadoop ecosystem for the processing of ‘big data.’ Additional topics include cloud computing and NoSQL databases. Introduction to concepts and techniques of computer science essential for data science will also be covered.

DATA 1080. Large Language Models and Generative AI.

The primary emphasis of this course will be on the technical foundations of large language models (LLMs) and generative AI (Gen AI). The course will cover the use of agents and tools to combine the power of large language models and Gen AI with a wide range of traditional computer algorithms. Additionally, the course will cover the social impacts of this technology and the safety concerns it raises. Students will be required to complete a project to build a multi-agent system in which each agent uses both the power of generative AI and traditional algorithms and in which the collaboration of the agents produces a system which can perform a complex task such as planning a trip or building a predictive model or creating a database or a creating a website. Programming assignments will be done in Python or PyTorch.

DATA 1150. Data Science Fellows.

DATA 1150 for juniors and seniors possessing data science skills, seeking to apply these skills and collaborate with faculty to integrate data science content into Brown courses. The course teaches communication, teaching and learning strategies, and determining project requirements. Qualified students have a combination of programming experience (intermediate level or above in R or Python), statistical knowledge (intermediate level or above) and knowledge of how data and computing can be used in applied fields. Students in the data fluency certificate must have DATA 0200 prior to DATA 1150. Students need to complete the application (url below) no later than August 1st for consideration.  Qualified students must participant in an interview with the instructor and override requests will be granted only to students by instructor approval. https://forms.gle/Je3Prrzs3NDEo4eG9

DATA 1200. Reality Remix - Experimental VR.

This course pursues collaborative experimentation with virtual and augmented reality (AR and VR). The class will work as a team to pursue research (survey of VR/AR experiences, scientific and critical literature review), reconnaissance (identifying VR/AR resources on campus, in Providence and the region), design (VR/AR prototyping). Research findings are documented in a class wiki. The course makes use of Brown Arts Initiative facilities in the Granoff Center where an existing VR laboratory will be expanded through the course of the semester based on student needs. Class culminates in the release the class wiki as a resource for the Brown community.

DATA 1340. Machine Learning for the Earth and Environment (EEPS 1340)..

Interested students must register for EEPS 1340.

DATA 1450. Text Analytics.

This course will first cover techniques for compiling textual corpora from web pages, pdfs, scanned pdfs, images, audio clips, etc. Secondly, it will look at processes for extracting some common types of information from these corpora. In particular, we will cover extracting named entities (persons, locations, organizations, etc.), relations between entities, events, transactions, topics, document summaries, abstracts, legal clauses, etc. This course is different from standard courses in Natural Language Processing and Computational Linguistics in that we will spend significant amount of course time on compiling textual corpora from documents in a variety of formats and our emphasis will be on extracting information that can be fed to analytics pipelines.

DATA 1491. Fairness in Automated Decision Making (CSCI 1491)..

Interested students must register for CSCI 1491.

DATA 1500. Data Visualization & Narrative.

Data visualization is an essential tool in both discovering and communicating key analytical findings. However, data practitioners and developers can sometimes undervalue the visual polish that goes into creating the most effective graphics. This course will act as a technical primer for building data visualization using code, but a core focus will be the graphic design decisions – color, hierarchy, font selection, labeling – that elevate visualizations. Additional topics will include cartography, web design, and interaction.

DATA 1720. Tackling Climate Change with Machine Learning (EEPS 1720).

Interested students must register for EEPS 1720.

DATA 2020. Statistical Learning.

A modern introduction to inferential methods for regression analysis and statistical learning, with an emphasis on application in practical settings in the context of learning relationships from observed data. Topics will include basics of linear regression, variable selection and dimension reduction, and approaches to nonlinear regression. Extensions to other data structures such as longitudinal data and the fundamentals of causal inference will also be introduced.

DATA 2040. Deep Learning and Special Topics in Data Science.

A hands-on introduction to neural networks, reinforcement learning, and related topics. Students will learn the theory of neural networks, including common optimization methods, activation and loss functions, regularization methods, and architectures. Topics include model interpretability, connections to other machine learning models, and computational considerations. Students will analyze a variety of real-world problems and data types, including image and natural language data.

DATA 2050. Data Science Practicum.

This course is a requirement for master’s students in Data Science and is only open to them. The course includes a semester-long capstone project as well as instruction on topics that prepare students for working as data scientists, such as requirements gathering, version control, bug tracking, software deployment, and other professional development. Capstone projects will be sourced from entities in Brown (departments, labs, researchers, etc.) as well as external entities (companies, nonprofits, etc.) and will engage most of the core components of data science: Data sculpting (data cleaning, formatting, feature selection, etc.), exploratory data analysis, data modeling, and data visualization. Students will also address any social and ethical issues raised by their project. Students will usually work in teams of 3 - 4.

DATA 2060. Machine Learning: from Theory to Algorithms.

Data science techniques and tools are all around us. Machine learning is a term used across many different disciplines, and often people use machine learning tools without a thorough understanding of how and why the tools work. This course will provide a foundation of machine learning grounded in the mathematical models behind the techniques. We will cover the theory, computational methods, and visualization inherent in the application of machine learning models. Students will learn the statistical learning framework, common assumptions in the data generation process, the mathematics behind machine learning models, including supervised and unsupervised techniques, as well as how to implement machine learning models in Python from scratch. Strong python skills (preferably object-oriented programing) are required. All DSI and CS ScM students will receive overrides as this is a required course for them.

DATA 2080. Data and Society.

A course on the social, political, and philosophical issues raised by the theory and practice of data science. Explores how data science is transforming not only our sense of science and scientific knowledge, but our sense of ourselves and our communities and our commitments concerning human affairs and institutions generally. Students will examine the field of data science in light of perspectives provided by the philosophy of science and technology, the sociology of knowledge, and science studies, and explore the consequences of data science for life in the first half of the 21st century.

DATA 2110. Topics in Econometrics.

This course will begin with a survey of the literature on identification using instrumental variables, including identification bounds, conditional moment restrictions, and control function approaches. The next part of class will cover some of the theoretical foundations of machine learning, including regularization and data-driven choice of tuning parameters. We will discuss in some detail the canonical normal means model, Gaussian process priors, (empirical) Bayes estimation, and reproducing kernel Hilbert space norms. We will finally cover some selected additional topics in machine learning, including (deep) neural nets, text as data (topics models), multi-armed bandits, and data visualization.

DATA 2980. Research in Data Science.

Section numbers vary by instructor. Please check Banner for the correct section number and CRN to use when registering for this course.

DSIO 2000. Technical Foundation for Data Science Success.

Focused on practical applications, this course covers the foundational concepts from programming, linear algebra, calculus, probability, and statistics that are most relevant to technical data science skills in the curriculum. At the end of the course, all students will achieve a baseline level of technical knowledge to prepare them for the technical components of the program. Learners will have the opportunity to explore more in-depth topics to discover how foundational topics like derivatives, matrices, and distributions are applied in machine learning and data modeling. Examples are drawn from advanced courses to highlight how these tools can be applied to theories and practice examined in this course.

DSIO 2010. Data Engineering in Disguise.

This course introduces students to the core principles of data engineering, emphasizing the often-hidden ethical choices that shape how massive datasets are managed. Students will learn about the fundamentals of data architecture, storage, and processing, while exploring critical values issues such as privacy, biases, data provenance, ownership, and copyright. This course interweaves the ethical considerations with the technical mechanics of data engineering, which exemplifies the real-world choices data engineers make as well as their broader societal implications. By the end of the course, students will understand not just the technical foundations of data engineering, but also the value-laden decisions involved in handling large-scale data.

DSIO 2100. Basic AI & Policy Ethics.

This course provides an introduction to the ethical and policy considerations surrounding artificial intelligence (AI) in today's society. Students will explore key ethical concerns, such as data privacy, bias, and accountability, as well as the societal and historical contexts that have shaped current AI governance. In addition, the course will offer a high-level overview of how AI systems are developed, including the basics of data collection, usage, and the training process for machine learning models. By examining these topics, students will gain a foundational understanding of the complex interactions between AI technologies and societal impacts, preparing them for deeper discussions in future courses on AI governance and responsible innovation.

DSIO 2110. Evidence-Driven Policy Making.

This course explores the role of artificial intelligence (AI) and machine learning (ML) in shaping evidence-driven policy decisions across various sectors. Using case studies from various AI/ML sectors, students will critically examine how AI/ML tools influence policy outcomes. Rather than delving into the technical intricacies of ML, this course emphasizes a "black box" approach, where data inputs lead to predictions. Students will learn to distinguish between prediction and intervention, recognize the limitations of AI/ML, and develop a transparent, precise language for discussing these technologies. By fostering healthy skepticism, this course equips students to make informed decisions about AI's role in evidence-based policy making.