Spring 2019 Courses
Backbone
Title |
Course Number |
Times & Locations |
Description |
Instructor |
Units |
| Foundations of Data Science (Data 8) |
STAT/COMPSCI C8 Class #: 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 Class #: 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 Class #: 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 Class #: 21870 |
M 12-2pm Sutardja Dai Hall 254 | 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 |
| Computational Structures in Data Science |
COMPSCI 88 Class #: 32367 |
M 9-10am LeConte 4 |
Development of Computer Science topics appearing in Foundations of Data Science (C8); expands computational concepts and techniques of abstraction. Understanding the structures that underlie the programs, algorithms, and languages used in data science and elsewhere. Mastery of a particular programming language while studying general techniques for managing program complexity, e.g., functional, object-oriented, and declarative programming. Provides practical experience with composing larger systems through several significant programming projects. | Gerald Friedland | 2 |
| Computational Structures in Data Science |
COMPSCI 88 Class #: 32726 |
M 10:30-11:30am Soda 306 |
See above | Gerald Friedland | 2 |
| Reproducibility and Open Science |
L&S 88-2 Class #: 26919 |
M 1-3pm Cory 105 | 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 | Eric Van Dusen | 2 |
| Children in the Developing World |
L&S 88-1 Class #: 26918 |
M 3-6pm Cory 105 | Child nutrition and education are important for adult success, but children in developing countries fall behind with nutritional consumption and have fewer educational opportunities. Household surveys are an instrumental tool for understanding factors associated with investments in children. We will use household data sets to explore relationships between nutrition and education outcomes and a variety of socio-economic variables to establish an understanding of contextual elements which can hinder or promote child growth and learning. | Sarah Reynolds | 3 |
| Reading and Writing the Digital Age |
ENGLISH 98 Class #: 33111 |
M 11am-1pm Cory 105 | In this course, we will survey the production, consumption, and study of literary texts in the digital age. We will explore digital culture in social and networked practices of writing and reading, new theories of reading in the digital moment (distance reading, surface reading), and debates within the digital humanities, media and literary studies, including theorists Best, Chun, Gitelman, Hayles, Liu, Love, and Marcus. Moving from Internet 1.0 to Internet 2.0, we will engage many “born-digital” and online texts, such as Michael Joyce’s afternoon, William Gibson’s Aggripa, Claudio Pinhanez’s “Open Diary” as well as digital literary platforms and websites. We will conclude the semester by looking at digital writing and its reception now, including the Instagram poetry of Rupi Kaur and Warsan Shire’s poetics in Beyoncé’s Lemonade. | Hannah Zeavin | 2 |
| Data Science Applications in Physics |
PHYSICS 88 Class #: 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. | Yury Kolomensky | 2 |
| Probability and Mathematical Statistics in Data Science |
STAT 88 Class #: 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 Class #: 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. | Michael Mahoney |
4 |
Human Contexts & Ethics
Title |
Course Number |
Times & Location |
Description |
Instructor |
Units |
| Introduction to Urban Data Analytics* |
CYPLAN 101 Class #: 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 Class #: 27274 Class #: 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 |
| Human Contexts and Ethics of Data |
HISTORY C184D / STS C104 Class #: 32670 |
MWF
12-1pm
Dwinelle 145
|
This course teaches you to use the tools of applied historical thinking and Science, Technology, and Society (STS) to recognize, analyze, and shape the human contexts and ethics of data. It addresses key topics such as 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. It prepares you to engage as a knowledgeable and responsible citizen and professional in the varied arenas of our datafied world. |
Cathryn Carson | 4 |
| Moral Questions of Data Science |
PHILOS 121 Class #: 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 Class #: 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 Class #: 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 Class #: 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 Class #: 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 Class #: 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 Class #: 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 Class #: 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 Class #: 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 Class #: 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 Class #: 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 Mining and Analytics |
INFO 154 Class #: 32435 |
TuTh 2-4 GSPP 150 |
This course introduces students to practical fundamentals of data mining and machine learning with just enough theory to aid intuition building. The course is project-oriented, with a project beginning in class every week and to be completed outside of class by the following week, or two weeks for longer assignments. The in-class portion of the project is meant to be collaborative, with the instructor working closely with groups to understand the learning objectives and help them work through any logistics that may be slowing them down. Weekly lectures introduce the concepts and algorithms which will be used in the upcoming project. Students leave the class with hands-on data mining and data engineering skills they can confidently apply. | Zachary A Pardos | 3 |
| Data, Prediction & Law |
LEGALST 123 Class #: 31757 |
MW 10-12 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 |
|
Interdisciplinary Perspectives on Artificial Intelligence |
ISF 50
Class #: 31941
|
|
In this course, we will ask: how have different human societies conceived of “intelligence,” natural or artificial, and how has this varied with place and time? How have different technical experts been influenced by the time, place, constraints, and patronage they operated under? How does contemporary AI intersect with regimes of calculation, capitalism, standardization, gender, and speech? The class will be interdisciplinary in the method as well as subject: we will study technical and popular material, philosophy and empirical work, engineering and social science literature, as well as science fiction. | Shreeharsh Kelkar | 3 |
| Introduction to Computational Techniques in Physics |
PHYSICS 77 Class #: 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 | |
| Programming for Mathematical Applications |
MATH 124 Class #:31047 |
TuTh 2-330 Cory 247 |
An introduction to computer programming with a focus on the solution of mathematical and scientific problems. Basic programming concepts such as variables, statements, loops, branches, functions, data types, and object orientation. Mathematical/scientific tools such as arrays, floating point numbers, plotting, symbolic algebra, and various packages. Examples from a wide range of mathematical applications such as evaluation of complex algebraic expressions, number theory, combinatorics, statistical analysis, efficient algorithms, computational geometry, Fourier analysis, and optimization. Mainly based on the Julia programming language, but some examples will demonstrate other languages such as MATLAB, Python, C, and Mathematica. | Per-Olof Sigfrid Persson | 4 |
| Research and Data Analysis in Psychology |
PSYCH 101 Class #: 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 Class #: 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 Class #: 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 |