Data science (2019 - 2020)
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). See syllabus. See course website.
Introduction to Data Science
DS-UA 112 Prerequisite: Data Science for Everyone (DS-UA 111), or Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2), or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or Introduction to Computer Science (CSCI-UA 101), 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. See draft syllabus.
Causal Inference
DS-UA 201 Prerequisites: Introduction to Data Science (DS-UA 112) and Probability and Statistics (MATH-UA 235), or permission of the program. Lecture and laboratory. Offered every semester. 4 points.
Provides students with the tools for understanding causation, i.e., the relationship between cause and effect. Begins with design and implemention 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, differences in differences, and contemporary advanced approaches.
Responsible Data Science
DS-UA 202 Prerequisites: Introduction to Data Science (DS-UA 112) and Probability and Statistics (MATH-UA 235). Lecture and laboratory. Offered every semester. 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 background in probability theory, e.g. Theory of Probability (MATH-UA 233); or permission of the instructor. Offered every spring semester. 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.
Advanced Topics in Data Science
DS-UA 301 Prerequisites: Introduction to Data Science (DS-UA 112) and Probability and Statistics (MATH-UA 235), or permission of the program. Lecture and laboratory. Offered every semester. 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
Consult departmental sections in this Bulletin for full descriptions.
COMPUTER SCIENCE
Introduction to Computer Programming (No Prior Experience)
CSCI-UA 2 Prerequisite: three years of high school mathematics or equivalent. No prior computing experience is assumed. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3. Offered in the fall and spring. 4 points.
Introduction to Computer Programming (Limited Prior Experience)
CSCI-UA 3 Prerequisite: Data Science for Everyone (DS-UA 101) or similar background in Python or other programming language, or a score of 1 or 2 on the AP Computer Science exam. Students who have taken or are taking Introduction to Computer Science (CSCI-UA 101) will not receive credit for this course. Students may not receive credit for both CSCI-UA 2 and CSCI-UA 3. Offered in the fall and spring. 4 points.
Database Design and Implementation
CSCI-UA 60 Prerequisite: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3). Offered in the spring. 4 points.
Introduction to Computer Science
CSCI-UA 101 Prerequisite: Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3), or a score of 3 on the AP Computer Science exam, or departmental permission assessed by placement exam. Offered in the fall and spring. 4 points.
Data Structures
CSCI-UA 102 Prerequisite: Introduction to Computer Science (CSCI-UA 101). Offered in the fall and spring. 4 points.
Programming Tools for the Data Scientist
CSCI-UA 381 Topics determine prerequisites. 4 points.
Introduction to Machine Learning
CSCI-UA 473 Prerequisites: Data Structures (CSCI-UA 102), Linear Algebra (MATH-UA 140), and Probability and Statistics (MATH-UA 235). 4 points.
MATHEMATICS
Calculus I
MATH-UA 121 Prerequisite: a score of 650 or higher on the mathematics portion of the SAT or on either SAT Subject Test in mathematics, an ACT mathematics score of 30 or higher, a score of 3 or higher on the AP Calculus AB exam or AB subscore, a score of 3 or higher on the AP Calculus BC exam, a grade of C or higher in Algebra and Calculus (MATH-UA 9) or equivalent, or a passing score on the departmental placement exam. Offered every term. 4 points.
Calculus II
MATH-UA 122 Prerequisite: Calculus I (MATH-UA 121) or equivalent with a grade of C or better, a score of 4 or higher on the AP Calculus AB or BC exam, or a passing score on the departmental placement exam. Offered every term. 4 points.
Linear Algebra
MATH-UA 140 Prerequisite: a score of 650 or higher on the mathematics portion of the SAT or on either SAT Subject Test in mathematics, an ACT mathematics score of 30 or higher, a score of 3 or higher on the AP Calculus AB exam or AB subscore, a score of 3 or higher on the AP Calculus BC exam, a grade of C or higher in Algebra and Calculus (MATH-UA 9) or equivalent, or a passing score on the departmental placement exam. Offered every term. 4 points.
Mathematics for Economics I
MATH-UA 211 Prerequisite: same as for Calculus I (MATH-UA 121). Offered every term. 4 points.
Mathematics for Economics II
MATH-UA 212 Prerequisite: completion of Mathematics for Economics I (MATH-UA 211) with a C or higher, or placement by departmental exam. Offered every term. 4 points.
Probability and Statistics
MATH-UA 235 Prerequisite: a grade of C or better in Calculus II (MATH-UA 122) or Mathematics for Economics II (MATH-UA 212) or equivalent. Not open to students who have taken Theory of Probability (MATH-UA 233) or Mathematical Statistics (MATH-UA 234). Offered in the spring. 4 points.