Computer Science Majors
If you have any questions regarding the major requirements, send an email to the Undergraduate Program Administrator.
Students must complete CSCIUA 101, Introduction to Computer Science (or higher) with a grade of C or better before they may declare a major in this department.
Major in Computer Science (12 Courses)
Core Requirements (7)

CSCIUA.0101 Introduction to Computer Science (Fall / Spring)
prerequisite: CSCIUA.0002 or CSCIUA.0003 or placement exam 
CSCIUA.0102 Data Structures (Fall / Spring)
prerequisite: CSCIUA.0101 
CSCIUA.0201 Computer Systems Organization (Fall / Spring)
prerequisite: CSCIUA.0102 
CSCIUA.0202 Operating Systems (Fall / Spring)
prerequisite: CSCIUA.0201 
CSCIUA.0310 Basic Algorithms (Fall / Spring)
prerequisite: CSCIUA.0102 and MATHUA.0120 
MATHUA.0121 Calculus I (Fall / Spring / Summer)
prerequisite: MATHUA.0009 
MATHUA.0120 Discrete Mathematics (Fall / Spring)
prerequisites
Electives (5)
 FIVE electives, numbered CSCIUA.04xx
Electives vary every fall, spring semester and there is one elective option offered in the summer semester. Students may substitute a 400level elective with one of the following Math classes: MATHUA.0122 Calculus II, MATHUA.0140 Linear Algebra, and/or MATHUA.0235 Probability and Statistics; a maximum of two classes can be substituted.
Joint Major in Computer Science and Data Science (18 Courses)
The joint major in computer and data science targets students who seek comprehensive training in two bodies of knowledge: (1) computer science, an established field that advances computing, programming, and building largescale and intelligent systems, and (2) data science, an emerging field that leverages computer science, mathematics, and domainspecific knowledge to analyze large data collections using data mining, predictive statistics, visualization, and efficient data management. The joint major in computer and data science trains students to use data science systems, the automated systems that effectively predict outcomes of interest and that extract insights from increasingly large data sets. This training enables students to participate in harnessing the power of data and in influencing policies that will govern the rollout of data science technologies. In addition, students gain the ability to build such systems. This is an interdisciplinary major (eighteen courses/72 points) offered by the Department of Computer Science and the Center for Data Science. A grade of C or better is necessary in all courses used to fulfill joint major requirements. Interested students should consult with the directors of undergraduate studies in the department and the center for additional information. Please note that the CAS minor requirement associated with the major in data science is waived for the computer and data science joint major, just as it is waived for a data science major pursuing a double major.
The computer science requirements (eight courses/32 points) are as follows:
 Introduction to Computer Science (CSCIUA 101) (Fall / Spring)
prerequisite: CSCIUA.0002 or CSCIUA.0003  Data Structures (CSCIUA 102) (Fall / Spring)
 Computer Systems Organization (CSCIUA 201) (Fall / Spring)
 Basic Algorithms (CSCIUA 310) (Fall / Spring)
 Introduction to Machine Learning (CSCIUA 473) (Fall / Spring)
 Data Management and Analysis (CSCIUA 479) (Spring)
Big data elective: choose one (1) of the following:
 Predictive Analytics (CSCIUA 475) (Fall / Spring)
 Processing Big Data for Analytics Applications (CSCIUA 476) (Fall / Spring)
Computer science elective: choose one (1) of the following:
 Operating Systems (CSCIUA 202) (Fall / Spring)
 Predictive Analytics (CSCIUA 475) (Fall / Spring)
 Processing Big Data for Analytics Applications (CSCIUA 476) (Fall / Spring)
 Special Topics: Computer Networks (CSCIUA 480) (Fall / Spring)
 Special Topics: Introduction to Numerical Optimization (CSCIUA 480) (Fall / Spring)
 Special Topics: Introduction to Social Networking (CSCIUA 480) (Fall / Spring)
 Special Topics: Natural Language Processing (CSCIUA 480) (Fall / Spring)
 Special Topics: Parallel Computing (CSCIUA 480) (Fall / Spring)
Data science requirements (five courses/20 points) are as follows:
 Data Science for Everyone (DSUA 111)
 Introduction to Data Science (DSUA 112)
 Causal Inference (DSUA 201)
 Responsible Data Science (DSUA 202)
 Advanced Topics in Data Science (DSUA 301)
Mathematics requirements (five courses/20 points) are as follows:
 Calculus I (MATHUA 121) or Mathematics for Economics I (MATHUA 211)
 Calculus II (MATHUA 122) or Mathematics for Economics II (MATHUA 212)
 Discrete Mathematics (MATHUA 120)
 Linear Algebra (MATHUA 140)
 Probability and Statistics (MATHUA 235)
Joint Major in Computer Science/Mathematics (18 Courses)
REQUIREMENTS FOR STUDENTS WHO ENROLLED AT NYU FALL 2014 OR LATER
An interdisciplinary major offered jointly by the Department of Mathematics and Computer Science, providing the opportunity to study both computer science and such relevant mathematics courses as analysis, algebra, probability, and statistics.
The requirements are ten courses numbered MATHUA 120 or higher, except MATHUA 125, 211, 212, 213 or 246 from the Mathematics Department, which must include:
 MATHUA 120 Discrete Math
 MATHUA 121 Calculus I
 MATHUA 122 Calculus II
 MATHUA 123 Calculus III
 MATHUA 140 Linear Algebra
 MATHUA 325 Analysis I or MATHUA 328 Honors Analysis I
 MATHUA 343 Algebra I or MATHUA 348 Honors Algebra I
And two of the following:
 MATHUA 233 Theory of Probability
 MATHUA 234 Mathematical Statistics
 MATHUA 251 Introduction to Mathematical Modeling
 MATHUA 252 Numerical Analysis
 MATHUA 263 Partial Differential Equations
 MATHUA 282 Functions of a Complex Variable
 MATHUA 329 Honors Analysis II
 MATHUA 349 Honors Algebra II
 MATHUA 377 Differential Geometry
And one additional Math course numbered MATHUA 120 or higher, except MATHUA 211, 212, 213 or 270.
The requirements are eight courses from the Computer Science Department, which must include:

CSCIUA.0101 Introduction to Computer Science (Fall / Spring)
prerequisite: CSCIUA.0002 or CSCIUA.0003 or departmental permission 
CSCIUA.0102 Data Structures (Fall / Spring)
prerequisite: CSCIUA.0101 
CSCIUA.0201 Computer Systems Organization (Fall / Spring)
prerequisite: CSCIUA.0102 
CSCIUA.0202 Operating Systems (Fall / Spring)
prerequisite: CSCIUA.0201 
CSCIUA.0310 Basic Algorithms (Fall / Spring)
prerequisite: CSCIUA.0102 and MATHUA.0120  CSCIUA.0421 Numerical Computing (Spring) ^{[1]}
 CSCIUA.04xx Advanced CS Elective
 CSCIUA.04xx Advanced CS Elective
A grade of C or better is required in all these courses to fulfill the major requirement.
[1] If the student has chosen Numerical Analysis from the Math side, they can be excused by the DUS from the CSCIUA.421 Numerical Computing requirement and substitute it for a different upper level elective.
Joint Major in Economics/Computer Science (22 Courses)
Computer Science Requirements (9)

CSCIUA.0101 Introduction to Computer Science (Fall / Spring)
prerequisite: CSCIUA.0002 or CSCIUA.0003 or departmental permission 
CSCIUA.0102 Data Structures (Fall / Spring)
prerequisite: CSCIUA.0101 
CSCIUA.0201 Computer Systems Organization (Fall / Spring)
prerequisite: CSCIUA.0102 
CSCIUA.0202 Operating Systems (Fall / Spring)
prerequisite: CSCIUA.0201 
CSCIUA.0310 Basic Algorithms (Fall / Spring)
prerequisite: CSCIUA.0102 and MATHUA.0120  CSCIUA.04xx Advanced CS Elective
 CSCIUA.04xx Advanced CS Elective
 CSCIUA.04xx Advanced CS Elective
 CSCIUA.04xx Advanced CS Elective OR
one of the following:
 MATHUA.0140 Linear Algebra
 ECONUA.0310 Strategic Decision Theory
 ECONUA.0365 Advanced Micro Theory
 ECONUA.0375 Topics in Economic Theory
Economics Requirements  Theory Concentration (9)
 ECONUA.0001 Introduction to Macroeconomics
 ECONUA.0002 Introduction to Microeconomics
 ECONUA.0011 Microeconomics
 ECONUA.0013 Macroeconomics
 ECONUA.0020 Analytical Statistics
 ECONUA.0266 Introduction to Econometrics
 Two economics theory electives, numbers ECONUA 300399
 One additional economics elective
Mathematics Requirements (4)