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 fall, spring, and 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
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: a 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. Not open to students who have placed into CSCI-UA 101. 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). Not open to students who have taken Data Management and Analysis (CSCI-UA 479). 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.
Computer Systems Organization
CSCI-UA 201 Prerequisite: Data Structures (CSCI-UA 102). Offered in the fall and spring. 4 points.
Operating Systems
CSCI-UA 202 Prerequisite: Computer Systems Organization (CSCI-UA 201). Offered in the fall and spring. 4 points.
Basic Algorithms
CSCI-UA 310 Prerequisites: Data Structures (CSCI-UA 102); Discrete Mathematics (MATH-UA 120); and Calculus I (MATH-UA 121) or Mathematics for Economics I (MATH-UA 211) or equivalent. Offered in the fall and spring. 4 points.
Programming Tools for the Data Scientist
CSCI-UA 381 Prerequisites: Data Science for Everyone (DS-UA 111) or equivalent proficiency in Python, and either Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3). Lecture and laboratory. Offered every semester. 4 points.
Introduction to Machine Learning
CSCI-UA 473 Prerequisites: Data Structures (CSCI-UA 102), Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148), and completion of the data science probability and/or statistics requirement. 4 points.
Predictive Analytics
CSCI-UA 475 Prerequisites: Computer Systems Organization (CSCI-UA 201), Basic Algorithms (CSCI-UA 310), and Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148). Offered every year. 4 points.
Processing Big Data for Analytics Applications
CSCI-UA 476 Prerequisites: Computer Systems Organization (CSCI-UA 201) and Basic Algorithms (CSCI-UA 310). Familiarity with Linux commands and SQL is helpful but is not required. Offered every year. 4 points.
Data Management and Analysis
CSCI-UA 479 Prerequisite: Data Structures (CSCI-UA 102). Not open to students who have taken Database Design and Implementation (CSCI-UA 60). Offered in the fall and spring. 4 points.
Special Topics in Computer Science
CSCI-UA 480 Topics determine prerequisites. 4 points.
MATHEMATICS
Discrete Mathematics
MATH-UA 120 Prerequisite: one of the following: a score of 670 or higher on the Mathematics portion of the SAT, a score of 650 or higher on the 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. Also acceptable as a prerequisite: for students entering NYU prior to fall 2021: an IB Mathematics SL score of 6 or higher, an IB Mathematical Studies SL score of 7, or an IB Mathematics HL score of 5; for students entering NYU in fall 2021 or later: an IB Analysis and Approaches SL score of 7, an IB Analysis and Approaches HL score of 5, or an IB Applications and Interpretations HL score of 5. Offered every term. 4 points.
Calculus I
MATH-UA 121 Prerequisite: one of the following: a score of 670 or higher on the Mathematics portion of the SAT, a score of 650 or higher on the 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. Also acceptable as a prerequisite: for students entering NYU prior to fall 2021: an IB Mathematics SL score of 6 or higher, an IB Mathematical Studies SL score of 7, or an IB Mathematics HL score of 5; for students entering NYU in fall 2021 or later: an IB Analysis and Approaches SL score of 7, an IB Analysis and Approaches HL score of 5, or an IB Applications and Interpretations HL score of 5. Offered every term. 4 points.
Calculus II
MATH-UA 122 Prerequisite: one of the following: Calculus I (MATH-UA 121) or equivalent with a grade of C or higher, a score of 4 or higher on the AP Calculus AB or BC exam, or a passing score on the departmental placement exam. Also acceptable as a prerequisite: for students entering NYU prior to fall 2021: an IB Mathematics HL score of 6 or higher (no Topic 9); for students entering NYU in fall 2021 or later: an IB Analysis and Approaches HL score of 6 or an IB Applications and Interpretations HL score of 6. Offered every term. 4 points.
Calculus III
MATH-UA 123 Prerequisite: one of the following: Calculus II (MATH-UA 122) or equivalent with a grade of C or higher, a score of 5 on the AP Calculus BC exam, or a passing score on the departmental placement exam. Also acceptable as a prerequisite: for students entering NYU prior to fall 2021: an IB Mathematics HL score of 6 or higher (with Topic 9) or an IB Further Mathematics HL score of 6 or higher; for students entering NYU in fall 2021 or later: an IB Analysis and Approaches HL score of 7. Offered every term. 4 points.
Linear Algebra
MATH-UA 140 Prerequisite: one of the following: a score of 670 or higher on the Mathematics portion of the SAT, a score of 650 or higher on the 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. Also acceptable as a prerequisite: for students entering NYU prior to fall 2021: an IB Mathematics SL score of 6 or higher, an IB Mathematical Studies SL score of 7, or an IB Mathematics HL score of 5; for students entering NYU in fall 2021 or later: an IB Analysis and Approaches SL score of 7, an IB Analysis and Approaches HL score of 5, or an IB Applications and Interpretations HL score of 5. Offered in the fall and spring. 4 points.
Honors Linear Algebra
MATH-UA 148 Prerequisite: one of the following: a score of 670 or higher on the Mathematics portion of the SAT, a score of 650 or higher on the 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. Also acceptable as a prerequisite: for students entering NYU prior to fall 2021: an IB Mathematics SL score of 6 or higher, an IB Mathematical Studies SL score of 7, or an IB Mathematics HL score of 5; for students entering NYU in fall 2021 or later: an IB Analysis and Approaches SL score of 7, an IB Analysis and Approaches HL score of 5, or an IB Applications and Interpretations HL score of 5. Offered in the fall and spring. 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.
Theory of Probability
MATH-UA 233 Prerequisite: a grade of C or higher (B or higher strongly recommended) in Calculus III (MATH-UA 123) or Honors Calculus III (MATH-UA 129) or Mathematics for Economics III (MATH-UA 213) (for economics majors) or equivalent, and a grade of C or higher in Linear Algebra (MATH-UA 140) or equivalent. The course is intended for mathematics majors and other students with a strong interest in mathematics and requires fluency in multi-variable integration. Not open to students who have taken Probability and Statistics (MATH-UA 235). Offered every term. 4 points.
Mathematical Statistics
MATH-UA 234 Prerequisite: a grade of C or higher in Theory of Probability (MATH-UA 233) or equivalent. Not open to students who have taken Probability and Statistics (MATH-UA 235). Offered in the fall and spring. 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.
Numerical Analysis
MATH-UA 252 Prerequisite: a grade of C or higher in both Calculus III (MATH-UA 123) or Honors Calculus III (MATH-UA 129) or Mathematics for Economics III (MATH-UA 213) (for economics majors) and Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148), or equivalent. Offered in the fall and spring. 4 points.
Analysis
MATH-UA 325 Prerequisites: a grade of C or higher in both Calculus III (MATH-UA 123) or Honors Calculus III (MATH-UA 129) or Mathematics for Economics III (MATH-UA 213) (for economics majors) and Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148), or equivalent. Offered in the fall and spring. 4 points.