Courses

Spring 2019 Courses

Backbone

Title	Course Number	Times & Locations	Description	Instructor	Units
Foundations of Data Science (Data 8)	STAT/COMPSCI C8 CCN: 24996	MWF 10-11am Wheeler 150	Foundations of data science from three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It delves into social and legal issues surrounding data analysis, including issues of privacy and data ownership.	William Fithian & Ani Adhikari	4
Principles & Techniques of Data Science (Data 100)	STAT/COMPSCI C100 CCN: 28558	TTh 6:30pm-8 Wheeler 150	In this course, students will explore the data science lifecycle, including question formulation, data collection and cleaning, exploratory data analysis and visualization, statistical inference and prediction, and decision-making. This class will focus on quantitative critical thinking and key principles and techniques needed to carry out this cycle. These include languages for transforming, querying and analyzing data; algorithms for machine learning methods including regression, classification and clustering; principles behind creating informative data visualizations; statistical concepts of measurement error and prediction; and techniques for scalable data processing.	John S. DeNero & Sandrine Dudoit	4
Probability for Data Science	STAT 140 CCN: 25622	TuTh 5-6:30pm Valley Life Sciences 2050	An introduction to probability, emphasizing the combined use of mathematics and programming to solve problems. Random variables, discrete and continuous families of distributions. Bounds and approximations. Dependence, conditioning, Bayes methods. Convergence, Markov chains. Least squares prediction. Random permutations, symmetry, order statistics. Use of numerical computation, graphics, simulation, and computer algebra.	Ani Adhikari	4

Connectors

Title	Course Number	Times & Location	Description	Instructor	Units
Data Science and the Mind	COGSCI 88 CCN: TBD	TBD	How does the human mind work? We explore this question by analyzing a range of data concerning such topics as human rationality and irrationality, human memory, how objects and events are represented in the mind, and the relation of language and cognition. This class provides young scientists with critical thinking and computing skills that will allow them to work with data in cognitive science and related disciplines.	Dmetri Hayes	2
Reproducibility and Open Science	L&S 88 CCN: TBD	TBD	The purpose of this course is to introduce students to the issues of scientific reproducibility and highlight some recent examples in the scientific literature. The class will present and cover different tools and concepts used in open science. In this course, Data Science students across domains will be introduced to concepts in scientific reproducibility and transparency through readings, case-studies, and hands-on lab activities. The course is intended as a survey of topics for students who have taken or are currently enrolled in Data 8. Students in the course will learn about the principles of doing open science, be able to create their own reproducible workflows, and gain an introduction to computational tools for Reproducible Data Analysis	TBD	2
Data Science Applications in Physics	PHYSICS 88 CCN: 27035	M 2-4 Evans 60	Introduction to data science with applications to physics. Topics include: statistics and probability in physics, modeling of the physical systems and data, numerical integration and differentiation, function approximation. Connector course for Data Science 8, room-shared with Physics 77. Recommended for freshmen intended to major in physics or engineering with emphasis on data science.		2
Probability and Mathematical Statistics in Data Science	STAT 88 CCN: 24717	WF 12-1 Birge 50	In this connector course we will state precisely and prove results discovered in the foundational data science course through working with data. Topics include: total variation distance between discrete distributions; the mean, standard deviation, and tail bounds; correlation, and the derivation of the regression equation; probabilities, random variables, and the Central Limit Theorem; probabilistic models; symmetries in random permutations; prior and posterior distributions, and Bayes' rule.	Fletcher H Ibser	2
Linear Algebra for Data Science	STAT 89A CCN: 24718	TuTh 12:30-2 Evans 60	This connector will cover introductory topics in the mathematics of data science, focusing on discrete probability and linear algebra and the connections between them that are useful in modern theory and practice. We will focus on matrices and graphs as popular mathematical structures with which to model data. For examples, as models for term-document corpora, high-dimensional regression problems, ranking/classification of web data, adjacency properties of social network data, etc.		4
Data & Decisions	UGBA 96-4 CCN: 17360	M 2-4	The objective of the course is to provide an understanding of how data and statistical analysis can improve managerial decision-making. Students learn how to ask the right questions, find or collect relevant data, and apply appropriate statistical methods to solve problems and make better business decisions. We will explore statistical methods for gleaning insights from economic and social data, with an emphasis on approaches to identifying causal relationships. We will discuss how to design and analyze randomized experiments and introduce econometric methods for estimating causal effects in non-experimental data. This course, in combination with the Foundations course, satisfies the statistics prerequisite for admissions to Haas.	Conrad Miller	2
Data & Decisions	UGBA 96-5 CCN: 17361	M 4-6	(see above)	Conrad Miller	2

Human Contexts & Ethics

Title	Course Number	Times & Location	Description	Instructor	Units
Introduction to Urban Data Analytics*	CYPLAN 101 CCN: 32303	MW 12:30-2 Wurster 112	This course (1) provides a basic intro to census and economic data collection, processing, and analysis; (2) surveys forecasting and modeling techniques in planning; (3) demonstrates the uses of real-time urban data and analytics; and (4) provides a socio-economic-political context for the smart cities movement, focusing on data ethics and governance. *This course is approved for the Human Contexts & Ethics requirement for students completing certain approved Domain Emphases only. Please see the Domain Emphasis page for more information.	Karen Chapple	4
Environmental Health and Development*	ESPM C167/ PBHLTH C160 CCN: 27274 CCN: 29594	TuTh 9:30am-11am Genetics & Plant Bio 100	The health effects of environmental alterations caused by development programs and other human activities in both developing and developed areas. Case studies will contextualize methodological information and incorporate a global perspective on environmentally mediated diseases in diverse populations. Topics include water management; population change; toxics; energy development; air pollution; climate change; chemical use, etc. *This course is approved for the Human Contexts & Ethics requirement for students completing certain approved Domain Emphases only. Please see the Domain Emphasis page for more information.	Rachel A. Morello-Frosch Simona A Yi-balan	4
Introduction to Science, Technology, and Society	HISTORY C182C CCN: 26861	MWF 12-1pm Dwinelle 145	This course explores how data science is entangled with diverse human contexts (histories, institutions, and material bases) and ethics (domains of value-laden choice). We will bring historically-grounded perspectives as well as frameworks and methods from Science, Technology, and Society (STS) (such as cross-national comparison, co-production, and controversy studies) to bear on topics that include: Doing ethical data science amid shifting definitions of human subjects, consent, and privacy; the changing relationship between data, democracy, and law; the role of data analytics in how corporations and governments provide public goods such as health and security to citizens; sensors, machine learning and artificial intelligence and changing landscapes of labor, industry, and city life; and the implications of data for how publics and varied scientific disciplines know the world.	Cathryn Carson	4
Moral Questions of Data Science	PHILOS 121 CCN: 31934	TuTh 8-9:30am Barrows 56	This course explores, from a philosophical perspective, ethical questions arising from collecting, drawing inferences from, and acting on data, especially when these activities are automated and on a large scale. Topics include: bias, fairness, discrimination, interpretability, privacy, paternalism, freedom of speech, and democracy.	Nicholas G Kolodny	4

Data Enabled Courses

These courses are taught in a way that permits students to build on Data 8. Please review the prerequisites.

Title	Course Number	Times & Locations	Description	Instructor	Units
Astronomy Data Science Laboratory	ASTRON 128 CCN: 32227	M 4-7pm Campbell 131	This course features 3 data-centric laboratory experiments that draw on a variety of tools used by professional astronomers. Students will learn to procure and clean data (drawn from a variety of world-class astronomical facilities), assess the fidelity/quality of data, build and apply models to describe data, learn statistical and computational techniques to analyze data (e.g., Bayesian inference, machine learning, parallel computing), and effectively communicate data and scientific results. There is a heavy emphasis on software development in the Python language, statistical techniques, and high-quality communication (e.g., written reports, oral presentations, and data visualization).	Daniel R. Weisz	4
Engineering Data Analysis	CIVENG 93 CCN: 27748	MW 9-10 Davis 502	Application of the concepts and methods of probability theory and statistical inference to CEE problems and data; graphical data analysis and sampling; elements of set theory; elements of probability theory; random variables and expectation; simulation; statistical inference. Use of computer programming languages for analysis of CEE-related data and problems. The course also introduces the student to various domains of uncertainty analysis in CEE.	Mark Hansen	3
Computational Models of Cognition	COGSCI 131 CCN: 25868	TuTh 11-12:30 Haas F295	This course will provide advanced students in cognitive science and computer science with the skills to develop computational models of human cognition, giving insight into how people solve challenging computational problems, as well as how to bring computers closer to human performance. The course will explore three ways in which researchers have attempted to formalize cognition -- symbolic approaches, neural networks, and probability and statistics -- considering the strengths and weaknesses of each.	Steven T. Piantadosi	4
Sensemaking and Organizing	COGSCI 190 CCN: 19849	TuTh 12:30-2 Wheeler 104	Sensemaking and Organizing: When something "makes sense” or " is organized” we are imposing or discovering order in the arrangement of concepts, events, or resources of some kind. Sensemaking and organizing are fundamental human activities that raise many multi- or trans-disciplinary questions about perception, knowledge, decision making, interaction with things and with other people, values and value creation.We can analyze sensemaking and organizing from four interrelated perspectives. The most fundamental one is provided by language and culture, which shapes the perspectives one takes as an individual, in institutional contexts governed by business or legal processes, or in data-intensive or scientific contexts. CogSci 1 required.	Robert J. Glushko	3
Introduction to Machine Learning	COMPSCI 189 CCN: 28076	MW 6:30-8pm Wheeler 150	Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.	Jonathan Shewchuk	4
Social Networks	DEMOG 180 CCN: 26510	TuTh 9:30-11am McCone 141	The science of social networks focuses on measuring, modeling, and understanding the different ways that people are connected to one another. We will use a broad toolkit of theories and methods drawn from the social, natural, and mathematical sciences to learn what a social network is, to understand how to work with social network data, and to illustrate some of the ways that social networks can be useful in theory and in practice. We will see that network ideas are powerful enough to be used everywhere from UNAIDS, where network models help epidemiologists prevent the spread of HIV, to Silicon Valley, where data scientists use network ideas to build products that enable people all across the globe to connect with one another.	Dennis Feehan	4
Applied Econometrics and Public Policy	ECON C142/POL SCI C131A/PUB POL C142 CCN: 25380	TuTh 5-6:30 Moffitt Library 102	This course focuses on the sensible application of econometric methods to empirical problems in economics and public policy analysis. It provides background on issues that arise when analyzing non-experimental social science data and a guide for tools that are useful for empirical research. By the end of the course, students will have an understanding of the types of research designs that can lead to convincing analysis and be comfortable working with large scale data sets.	David Card	4
Introductory Applied Econometrics	ENVECON/IAS C118 CCN: 27500	TuTh 9:30-11am VLSB 2060	Formulation of a research hypothesis and definition of an empirical strategy. Regression analysis with cross-sectional and time-series data; econometric methods for the analysis of qualitative information; hypothesis testing. The techniques of statistical and econometric analysis are developed through applications to a set of case studies and real data in the fields of environmental, resource, and international development economics. Students learn the use of a statistical software for economic data analysis.	Sofia B. Villas-Boas	4
Terrestrial Hydrology	GEOG C136 CCN: 30492	TuTh 9:30-11am Evans 9	A quantitative introduction to the hydrology of the terrestrial environment including lower atmosphere, watersheds, lakes, and streams. All aspects of the hydrologic cycle, including precipitation, infiltration, evapotranspiration, overland flow, streamflow, and groundwater flow. Chemistry and dating of groundwater and surface water. Development of quantitative insights through problem solving and use of simple models. This course requires one field experiment and several group computer lab assignments.	Laurel Larsen	4
Applied Data Science with Venture Applications	INDENG 135 CCN: 28885	TuTh 2-3:30 Dwinelle 145	This highly-applied course surveys a variety of key of concepts and tools that are useful for designing and building applications that process data signals of information. The course introduces modern open source, computer programming tools, libraries, and code samples that can be used to implement data applications. The mathematical concepts highlighted in this course include filtering, prediction, classification, decision-making, Markov chains, LTI systems, spectral analysis, and frameworks for learning from data. Each math concept is linked to implementation using Python using libraries for math array functions (NumPy), manipulation of tables (Pandas), long term storage (SQL, JSON, CSV files), natural language (NLTK), and ML frameworks.	Ikhlaq Sidhu, Alexander Fred Ojala	3
Data, Prediction & Law	LEGALST 123 CCN: 31757	MW 10-12 in Barrows 110	Data, Prediction, and Law is a new Legal Studies seminar that allows students to explore different data sources that scholars and government officials use to make generalizations and predictions in the realm of law. The course will also introduce critiques of predictive techniques in law. Students will apply the statistical and Python programming skills from Foundations of Data Science to examine a traditional social science dataset, “big data” related to law, and legal text data.	Jonathan D. Marshall	4
Introduction to Computational Techniques in Physics	PHYSICS 77 CCN: 24060	M 2-4 Evans 60	Introductory scientific programming in Python with examples from physics. Topics include: visualization, statistics and probability, regression, numerical integration, simulation, data modeling, function approximation, and algebraic systems. Recommended for freshman physics majors.		3
Research and Data Analysis in Psychology	PSYCH 101 CCN: 24314	TuTh 2-4 Lewis 100	The course will concentrate on hypothesis formulation and testing, tests of significance, analysis of variance (one-way analysis), simple correlation, simple regression, and nonparametric statistics such as chi-square and Mann-Whitney U tests. Majors intending to be in the honors program must complete 101 by the end of their junior year.	Christopher J. Gade	4
Concepts in Computing with Data	STAT 133 CCN: 24723	TuTh 2-4 Lewis 100	The course will concentrate on hypothesis formulation and testing, tests of significance, analysis of variance (one-way analysis), simple correlation, simple regression, and nonparametric statistics such as chi-square and Mann-Whitney U tests. Majors intending to be in the honors program must complete 101 by the end of their junior year.	Gaston Sanchez Trujillo	3
Modern Statistical Prediction and Machine Learning	STAT 154 CCN: 24765	TuTh 8-9:30am VLSB 2040	Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.	Bin Yu	4

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