Data Science (2020 - 2022)

Data Science for Everyone
DS-UA 111 Prerequisite: high school algebra or permission of the program. Lecture and laboratory. Offered every semester. 4 points.
Prepares students to participate in today's data-driven world. Students engage with core principles of data analysis and programming and gain practical experience with real-world datasets from the humanities, social sciences, and natural sciences. Introduces ethical, legal, and privacy issues. Aims to transform students from passive consumers of conclusions about data that other people have made to informed, empowered, and critical readers and producers of data-driven insights. Open to students from any discipline with any level of experience in computer science and/or statistics (including no experience at all).

Introduction to Data Science
DS-UA 112 Prerequisite: Data Science for Everyone (DS-UA 111) or permission of the program. Lecture and laboratory. Offered every semester. 4 points.
Fundamental principles and techniques of the field. Students examine real-world examples and cases so as to place data science techniques in context, to develop data-analytic and inferential thinking, and to illustrate that the discipline is as much an art as it is a science. Students gain hands-on experience with the Python programming language and its associated data analysis libraries. Examines ethical implications surrounding privacy and data sharing, as well as algorithmic decision making for given data science solutions.

Causal Inference
DS-UA 201 Prerequisite: Data Science for Everyone (DS-UA 111) or permission of the program. Recommended corequisite through fall 2021, and additional prerequisite thereafter: Introduction to Data Science (DS-UA 112). Lecture and laboratory. Offered every fall. 4 points.
Provides students with the tools for understanding the relationship between cause and effect. Begins with design and implementation of the data-gathering process (the experiment) and then identifies preconditions required for A to cause B and discusses threats to the validity of less-than-perfect experiments. Other topics: quasi-experiments, regression discontinuities, and contemporary advanced approaches.

Responsible Data Science
DS-UA 202 Prerequisite: Introduction to Data Science (DS-UA 112). Lecture and laboratory. Offered every spring. 4 points.
The first wave of data science focused on accuracy and efficiency: on what we can do with data. The second wave is about responsibility: what we should and should not do. Addresses the issues of ethics and responsibility in data science, including legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. Emphasizes a holistic treatment of the data science lifecycle, beginning with data discovery and acquisition and progressing through data cleaning, integration, querying, analysis, and result interpretation.

Machine Learning for Language Understanding
DS-UA 203 Identical to LING-UA 52. Prerequisites: at least one course with a substantial Python programming component, such as Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or an advanced CSCI-UA or other programming course; Calculus I (MATH-UA 121) or higher, or equivalent; and completion of the probability and/or statistics requirement; or permission of the instructor. Offered every spring. 4 points.
Covers widely-used machine learning methods for language understanding—with a special focus on methods based on artificial neural networks—and culminates in a substantial final project in which students write an original research paper in AI or computational linguistics. Introduces the many approaches that researchers use to teach language to computers. Students gain skills to design and build computational models, to design experiments to test those models, and to read and evaluate results from the scientific literature.

Practical Training
DS-UA 204 Restricted to data science majors or minors, who must have earned both a 3.0 cumulative GPA and a 3.5 data science GPA and must have completed half of the data science program of study. Does not count toward any major or minor. May be repeated once (taken two times total) for credit. Internship. Offered in the summer. 2 or 4 points.
Provides data science students with an opportunity to apply the knowledge gained in their coursework to practical problems in industry. This course is for majors and minors only and is graded on a pass/fail basis.

Advanced Topics in Data Science
DS-UA 301 Prerequisites: Introduction to Data Science (DS-UA 112) and completion of the probability and/or statistics requirement, or permission of the program. Lecture and laboratory. Offered every spring. 4 points.
Time series, deep learning, and other advanced machine learning topics. Provides the theoretical underpinnings of advanced data science techniques, as well as hands-on activities to build a practical toolkit.

Courses in Other Departments

For descriptions of, and prerequisites for, courses outside of DS-UA (such as computer science and mathematics), please consult the relevant departmental sections of this Bulletin.