These courses are taught in a way that permits students to build on Data 8. Please review prerequisites. To add a proposed course to this list, please contact the Data Science Education Program.
Title |
Course Number |
Times & Location |
Description |
Instructor |
Units |
|---|---|---|---|---|---|
| Statistical Methods for Data Science(link is external) |
STAT 28 |
Tu, Th 12:30 - 2 pm Evans 60 |
This is a lower-division course that is a follow-up to STAT8/CS8 (Foundations of Data Science). The course will teach a broad range of statistical methods that are used to solve data problems. Topics will include group comparisons and ANOVA, standard parametric statistical models, multivariate data visualization, multiple linear regression and classification, classification and regression trees and random forests. An important focus of the course will be on statistical computing and reproducible statistical analysis. The students will be introduced to the widely used R statistical language and they will obtain hands-on experience in implementing a range of commonly used statistical methods on numerous real world datasets. |
William Fithian | 4 |
| Engineering Data Analysis(link is external)(link is external) |
CIVENG 93 |
TuTh 9 - 10 am O'Brien 212 |
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. Applications to various CEE problems and real data will be developed by use of MATLAB and existing codes. The course also introduces the student to various domains of uncertainty analysis in CEE. |
Mark Hansen | 3 |
| Civil and Environmental Engineering Systems Analysis(link is external) |
CIVENG 191 |
TuTh 1 - 2 pm Davis 502 |
This course is organized around five real-world large-scale CEE systems problems. The problems provide the motivation for the study of quantitative tools that are used for planning or managing these systems. The problems include design of a public transportation system for an urban area, resource allocation for the maintenance of a water supply system, development of repair and replacement policies for reinforced concrete bridge decks, traffic signal control for an arterial street, scheduling in a large-scale construction project. |
Daniel Arnold | 3 |
| Computational Models of Cognition(link is external)(link is external) |
COGSCI 131 |
TuTh 2 - 3:30 pm 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. |
Anne Ge Collins | 4 |
| Sensemaking and Organizing(link is external)(link is external) |
COGSCI 190 |
MW 9 - 10 am Dwinelle 283 |
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: As an individual, as a member of a social, cultural, or language community, in institutional contexts, or in data‐intensive or scientific contexts. At the end of the course, students will be more aware of their existing mechanisms and methods for sensemaking and organizing and will have learned a variety of new ones that they can apply as appropriate in the four contexts. |
Robert J. Glushko | 3 |
| Introduction to Machine Learning(link is external) |
COMPSCI 189 |
TuTh 3:30 - 5 pm Dwinelle 155 |
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. |
TBD | 4 |
| Designing, Visualizing and Understanding Deep Neural Networks(link is external) |
COMPSCI 194-129 |
MW 5 - 6:30 pm North Gate 105 |
Topics will vary semester to semester. See the Computer Science Division announcements. | John F. Canny | 4 |
| Social Networks(link is external)(link is external) |
DEMOG 180 |
TuTh 11 am - 12:30 pm 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 M. Feehan | 3 |
| Seminar on Topics in Law and Society(link is external)(link is external) |
LEGALST 190 |
TuTh 10 am - 12 pm Barrows 122 |
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(link is external)(link is external) |
PHYSICS 77 |
M 2 - 4 pm Barrows 20 |
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. |
Yuri Kolomensky |
3 |
|
Research and Data Analysis in Psychology(link is external)(link is external) (10) Research and Data Analysis in Psychology(link is external)(link is external) (101) |
PSYCH 10/101 |
M 2 - 5 pm Lewis 100 |
The class covers research design, statistical reasoning, and statistical methods appropriate for psychological research. Topics covered in research design include the scientific method, experimental versus correlational designs, controls and placebos, within and between subject designs and temporal or sequence effects. Topics covered in statistics include descriptive versus inferential statistics, linear regression and correlation and univariate statistical tests: t-test, one way and two-way ANOVA, chi-square test. The class also introduces non-parametric tests and modeling. Prospective Psychology majors need to take this course to be admitted to the major. |
Arman D. Catterson |
4 |
|
Concepts in Computing with Data(link is external)(link is external) |
STAT 133 |
MWF 8 - 9 am Dwinelle 155 |
An introduction to computationally intensive applied statistics. Topics will include organization and use of databases, visualization and graphics, statistical learning and data mining, model validation procedures, and the presentation of results. |
Gaston Sanchez Trujillo |
3 |
|
Applied Data Science with Venture Applications(link is external)(link is external) |
IEOR 135 |
TUTh 3:30 - 5 pm Cory 277 |
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 |
3 |
|
Data Mining and Analytics(link is external)(link is external) |
INFO 190 - 1 |
TuTh 2 - 3:30 pm South Hall 202 |
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 |
|
Geographic Information Analysis(link is external)(link is external) |
GEOG 187 |
MW 9:30 - 10:30 am McCone 575 |
A spatial analytic approach to digital mapping and GIS. Given that recording the geolocation of scientific, business and social data is now routine, the question of what we can learn from the spatial aspect of data arises. This class looks at challenges in analyzing spatial data, particularly scale and spatial dependence. Various methods are considered such as hotspot detection, interpolation, and map overlay. The emphasis throughout is hands on and practical rather than theoretical. |
David Bernard O'Sullivan |
4 |
|
Introductory Applied Econometrics(link is external)(link is external)(ENVECON) Introductory Applied Econometrics(link is external)(link is external)(IAS) |
ENVECON/IAS C118 |
TuTh 9:30 - 11 am 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 |
|
Quantitative Sociological Methods(link is external)(link is external) |
SOCIOL 106 |
Th 8 - 10 am Barrows 475 |
This course will cover more technical issues in quantitative research methods, and will include, according to discretion of instructor, a practicum in data collection and/or analysis. Recommended for students interested in graduate work in sociology or research careers. |
Mao-Mei Liu |
4 |
|
Modern Statistical Prediction and Machine Learning(link is external)(link is external) |
STAT 154 |
MWF 11 am - 12 pm Tan Hall 180 |
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. |
Gaston Sanchez Trujillo |
4 |