| DATA 101-01 |
Data Science for the Humanities and Social Sciences
Instructor: Eren Bilen
Course Description:
What can data science do for you? This application-driven course invites students from the humanities, arts, and social sciences to use data as a lens for asking, and answering, disciplinary questions. Through a task-oriented introduction to statistical software, students will wrangle real-world datasets drawn from literary corpora, maps, music, sports, visual arts, and viral trends. Along the way we introduce core ideas in data science such as descriptive statistics, modeling, visualization, and ethics, while demystifying the field itself. This course equips you with practical, portable skills to address the following types of questions. How has the usage of the word Queer evolved? What makes hip hop lyrics different from country lyrics? How do the color palettes of the Renaissance compare to those of the French Impressionists? Did a viral Tik Tok increase sales of Stanley tumblers? No prior coding or statistics background is assumed. Topics to be announced when offered.Prerequisite: Dependent upon topic.
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01:30 PM-02:45 PM, MR
ALTHSE 201 |
| DATA 180-01 |
Introduction to Data Science
Instructor: Lulu Wang
Course Description:
Cross-listed with COMP 180-01 and MATH 180-01. An introduction to theprinciples and tools of data science focusing on exploratory data analysis. Topics include types of variables, mathematical representations of data, data wrangling and transformations, data visualization and numerical summaries, and supervised and unsupervisedmachinelearning. The course includes an introduction to the R statistical programming language. Prerequisites: MATH 170 or department placement. This course is cross-listed as COMP 180 and MATH 180. Offered every semester.
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09:00 AM-10:15 AM, TR
DENNY 104 |
| DATA 180-02 |
Introduction to Data Science
Instructor: Lulu Wang
Course Description:
Cross-listed with COMP 180-02 and MATH 180-02. An introduction to theprinciples and tools of data science focusing on exploratory data analysis. Topics include types of variables, mathematical representations of data, data wrangling and transformations, data visualization and numerical summaries, and supervised and unsupervisedmachinelearning. The course includes an introduction to the R statistical programming language. Prerequisites: MATH 170 or department placement. This course is cross-listed as COMP 180 and MATH 180. Offered every semester.
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10:30 AM-11:45 AM, TR
DENNY 104 |
| DATA 198-01 |
Philosophy of Data
Instructor: Chauncey Maher
Course Description:
Cross-listed with PHIL 258-01. This an introduction to philosophical issues arising in data science. Students will discuss, read and write about some important ethical issues that arise in the practice of data sciences, such as discrimination, privacy, consent, trust, and justice. To help clarify those issues, students will also learn about some connected issues in the epistemology and metaphysics of data science, such as the nature of statistical inference and of algorithms. Prerequisites: MATH 121 or DATA/COMP/MATH 180 or ECON 298. This course is cross-listed as PHIL 258. Offered every semester.
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12:30 PM-01:20 PM, MWF
ALTHSE 08 |
| DATA 200-01 |
Data Systems for Data Analytics
Instructor: Zach Kessler
Course Description:
Cross-listed with COMP 200-01. A comprehensive introduction to the access, structure, storage, and representation of data as it applies to data analytics. The tabular data model, relational data model, and hierarchical data model are studied. Topics include the use of structured query language (SQL) to extract and manipulate data from a relational database, APIs to extract information from web services, and methodologies for processing unstructured data. The primary programming language used in the course is Python.Prerequisite: COMP 130 or 132, and DATA/COMP/MATH 180. Cross-listed with COMP 200. Offered every semester.
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01:30 PM-02:45 PM, TF
STERN 11 |
| DATA 201-01 |
Agent-Based Simulation
Instructor: Zach Kessler
Course Description:
Cross-listed with COMP 203-01 and ECON 314-03. In this course, you will expand your data science skills to explore Agent-Based Modeling using languages like Python and NetLogo. Through the semester we will explore a variety of applications in economics, sociology, ecology, and more, emphasizing data-driven simulations of complex systems. This robust method will enable you to create highly detailed models covering critical areas like supply chains and trade networks, the impact of commercial fishing on aquatic environments, the rapid disappearance of historic civilizations, the emergence of wealth inequality in economies, and the spread of misinformation in social networks. In addition to this toolkit, you will learn various programming principles such as vectorized programming, object-oriented design, and key principles of visualization. By the end of the course you will have a fundamental understanding of the core ideas of complex systems analysis, the ability to build agent-based models across a variety of contexts, and a collection of core programming skills which will assist you in building other advanced models. Topics to be announced when offered.Prerequisite: Dependent upon topic.
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03:00 PM-04:15 PM, TF
STERN 11 |
| DATA 300-01 |
Statistical and Machine Learning
Instructor: Dick Forrester
Course Description:
An introduction to the fundamental concepts and methods for statistical and machine learning. Focus is given on providing both a theoretical foundation and the practical skills needed to apply machine learning to a variety of applications in various disciplines. Topics include supervised methods such as regression and classification, and unsupervised methods such as clustering and dimensionality reduction.Prerequisite: COMP/DATA 200 and MATH 225. Offered every semester.
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10:30 AM-11:20 AM, MWF
TOME 121 |
| DATA 400-01 |
Data Analytics Capstone
Instructor: Eren Bilen
Course Description:
. A capstone course that provides students with an opportunity to apply the data analytics knowledge they have acquired to independent research projects. At least one of the projects must be derived from the chosen discipline specific electives. Students will get experience in all aspects of solving real-world problems, including project planning, consideration of legal and ethical issues, collecting and processing data, analyzing and interpreting results, writing reports, and giving presentations.Prerequisites: DATA 300, completion of ECON 298 or concurrent registration in MATH 325, DATA 198/PHIL 258 and the three-course disciplinary sequence. Offered every spring.
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03:00 PM-04:15 PM, MR
ALTHSE 206 |
| DATA 500-01 |
Advanced Methods for Data Visualization
Instructor: Zach Kessler
Course Description:
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