Computer and Data Science (BA)
Program Description
The Computer and Data Science major integrates the full Data Science curriculum with the Computer Science curriculum. Students develop strong foundations in statistical modeling, machine learning, and data-driven reasoning, alongside advanced coursework in algorithms, computer systems, and related areas of computer science. The curriculum emphasizes computational methods and scalable system design, preparing students to build and deploy data-driven systems and to engage with the computational foundations of modern data science and artificial intelligence.
Students may contact cds-undergraduate@nyu.edu and undergrad@cs.nyu.edu with questions.
Honors Program
This major program of study does not currently offer an honors track.
Admissions
New York University's Office of Undergraduate Admissions supports the application process for all undergraduate programs at NYU. For additional information about undergraduate admissions, including application requirements, see How to Apply.
Program Requirements
The joint major in Computer and Data Science requires eighteen 4-credit courses (72 credits) as outlined below. The prerequisite for declaring this major is completion of (1) either CSCI-UA 101 Intro to Computer Science or CSCI-UA 102 Data Structures (depending on placement) AND (2) either DS-UA 111 Principles of Data Science I or DS-UA 112 Principles of Data Science II (depending on placement) with a C or better.
Students interested in this major should consult with the directors of undergraduate studies in the departments and CDS 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.
| Course | Title | Credits |
|---|---|---|
| General Education Requirements | ||
| First-Year Seminar | 4 | |
| EXPOS-UA 1 | Writing as Inquiry | 4 |
| Foreign Language | 16 | |
| Physical Science | 4 | |
| Life Science | 4 | |
| Texts and Ideas | 4 | |
| Cultures and Contexts | 4 | |
| Societies and the Social Sciences | 4 | |
| Expressive Culture | 4 | |
| Major Requirements | ||
| Computer Science Requirements | ||
| CSCI-UA 101 | Intro to Computer Science | 4 |
| CSCI-UA 102 | Data Structures | 4 |
| CSCI-UA 201 | Computer Systems Organization | 4 |
| CSCI-UA 310 | Basic Algorithms | 4 |
| CSCI-UA 473 | Fundamentals of Machine Learning | 4 |
| CSCI-UA 479 | Data Management and Analysis | 4 |
| Data Science Requirements | ||
| DS-UA 111 | Principles of Data Science I (offered every semester) | 4 |
| DS-UA 112 | Principles of Data Science II (offered every semester) | 4 |
| DS-UA 201 | Causal Inference (offered every semester) | 4 |
| DS-UA 202 | Responsible Data Science (offered every Spring) | 4 |
| Mathematics Requirements | ||
| MATH-UA 120 | Discrete Mathematics | 4 |
| MATH-UA 121 | Calculus I | 4 |
| or MATH-UA 131 | Mathematics for Economics I | |
| MATH-UA 122 | Calculus II | 4 |
| or MATH-UA 132 | Mathematics for Economics II | |
| MATH-UA 123 | Calculus III | 4 |
| or MATH-UA 133 | Mathematics for Economics III | |
| MATH-UA 140 | Linear Algebra | 4 |
| or MATH-UA 148 | Honors Linear Algebra | |
| MATH-UA 185 | Probability & Statistics (formerly UA 235) | 4 |
| Computer Science Electives (choose two): | 8 | |
| Operating Systems | ||
| Natural Language Processing | ||
| Predictive Analytics | ||
| Processing Big Data for Analytics Applications | ||
| Special Topics: (Depending on semester offerings, topics include: Computer Networks, Social Networking, Numerical Optimization, & Parallel Computing) | ||
| Advanced Elective Requirement (choose one): | 4 | |
| Machine Learning for Climate Change | ||
| Advanced Topics in Data Science | ||
| Natural Language Processing | ||
| Electives | ||
| Other Elective Credits | 4 | |
| Total Credits | 128 | |
Sample Plan of Study
| 1st Semester/Term | Credits | |
|---|---|---|
| MATH-UA 120 | Discrete Mathematics | 4 |
| MATH-UA 121 | Calculus I | 4 |
| First-Year Seminar | 4 | |
| Texts and Ideas | 4 | |
| Credits | 16 | |
| 2nd Semester/Term | ||
| CSCI-UA 101 | Intro to Computer Science | 4 |
| MATH-UA 122 | Calculus II | 4 |
| EXPOS-UA 1 | Writing as Inquiry | 4 |
| Cultures and Contexts | 4 | |
| Credits | 16 | |
| 3rd Semester/Term | ||
| DS-UA 111 | Principles of Data Science I | 4 |
| CSCI-UA 102 | Data Structures | 4 |
| MATH-UA 123 | Calculus III | 4 |
| Foreign Language I | 4 | |
| Credits | 16 | |
| 4th Semester/Term | ||
| DS-UA 112 | Principles of Data Science II | 4 |
| CSCI-UA 201 | Computer Systems Organization | 4 |
| MATH-UA 185 | Probability & Statistics ((formerly UA 235)) | 4 |
| Foreign Language II | 4 | |
| Credits | 16 | |
| 5th Semester/Term | ||
| CSCI-UA 310 | Basic Algorithms | 4 |
| MATH-UA 140 | Linear Algebra | 4 |
| DS-UA 201 | Causal Inference | 4 |
| Foreign Language III | 4 | |
| Credits | 16 | |
| 6th Semester/Term | ||
| CSCI-UA 479 | Data Management and Analysis | 4 |
| CS Elective 1 | 4 | |
| Foreign Language IV | 4 | |
| Societies and the Social Sciences | 4 | |
| Credits | 16 | |
| 7th Semester/Term | ||
| CSCI-UA 473 | Fundamentals of Machine Learning | 4 |
| CS Elective 2 | 4 | |
| Physical Science | 4 | |
| Other Elective Credits | 4 | |
| Credits | 16 | |
| 8th Semester/Term | ||
| DS-UA 202 | Responsible Data Science | 4 |
| DS-UA 301 | Advanced Topics in Data Science | 4 |
| Life Science | 4 | |
| Expressive Culture | 4 | |
| Credits | 16 | |
| Total Credits | 128 | |
Learning Outcomes
Upon completion of program requirements, students are expected to:
- Achieve a rigorous understanding of the mathematical, statistical, and computational principles that underpin data science, so that students will have the foundational mastery to pursue of the many applications of data sciences without limitation.
- Understand approaches such as causal inference, machine learning, and data management that are involved in different settings across varied academic and applied contexts.
- Examine the relationship between data science and society by addressing ethical and philosophical issues in modern statistics, data science, and AI, and develop the ability not only to design data models but also to communicate effectively about these models and their outputs.
- Develop fundamental theoretical and practical knowledge of the foundational areas of computer science, including algorithm design, machine learning, and programming.
- Achieve an understanding of what is going on "under the hood" of computer software in terms of the underlying computer architecture and operating systems.
Policies
Program Policies
Major Policies
- A grade of C or better is necessary in all courses used to fulfill major requirements; courses graded Pass/Fail do not count toward the major.
- To enroll in CSCI-UA 101 Intro to Computer Science students must first fulfill the prerequisite CSCI-UA 2 Introduction to Computer Programming (No Prior Experience) or CSCI-UA 3 Introduction to Computer Programming (Limited Prior Experience). Alternatively, they must first present a score of 3 on the AP Computer Science exam; students with a score of 4 or 5 may also register for CSCI-UA 101 (they are encouraged but not obliged to start with CSCI-UA 102 Data Structures), but they will forfeit the AP credit. Finally, students may take a placement test given by the department to enter CSCI-UA 101.
- Advanced Placement (AP) credit for Computer Science A is the equivalent of CSCI-UA 101 Intro to Computer Science and counts toward the major. However, the AP exam in Computer Science Principles cannot count toward any major or minor in this department.
- Students who score a 4 or 5 on the AP Computer Science exam are encouraged to register for CSCI-UA 102 Data Structures but are not obliged to; they may choose to take CSCI-UA 101 Intro to Computer Science before CSCI-UA 102 (and forfeit the AP credit).
- Students will also lose AP credit if they take certain other courses in the department; this is noted in the relevant course descriptions.
- Students are required to take CSCI-UA 101 Intro to Computer Science through CSCI-UA 201 Computer Systems Organization in sequence.
- Note that Albert will automatically block: students who complete CSCI-UA 2 Introduction to Computer Programming (No Prior Experience) with a C or better from registering for CSCI-UA 3 Introduction to Computer Programming (Limited Prior Experience); students who complete CSCI-UA 467 Applied Internet Technology with a C or better from registering for CSCI-UA 61 Web Development and Programming; and students who complete CSCI-UA 479 Data Management and Analysis with a C or better from registering for CSCI-UA 60 Database Design and Implementation.
- Two courses may be double-counted between the joint Computer and Data Science major and another major and/or another minor.
- CAS students (in any major or minor) are not permitted to take computer science courses in the Tandon School of Engineering.
- Those interested in spending a semester away should work out their schedule with an adviser as early as possible.
- Students must choose one of the two calculus tracks and cannot take courses from both tracks.
Policy on Declaration of Major
Students must complete either CSCI-UA 101 Intro to Computer Science or CSCI-UA 102 Data Structures (depending on placement) and also complete DS-UA 111 Principles of Data Science I with a grade of C or better before declaring the joint major in Computer and Data Science. This policy applies to all NYU students, not just to those matriculated in CAS. Note that students are not permitted to double major in Computer Science and Data Science. To pursue both disciplines, students must declare this joint major.
Students may declare at any time during the academic year using the links below. Any questions or concerns regarding the declaration process should be directed to cds-undergraduate@nyu.edu.
- Data Science Major or Minor
- Joint Major in Computer and Data Science (all NYU students must complete both DS-UA 111 Principles of Data Science I and CSCI-UA 101 Intro to Computer Science with a grade of C or better before declaring this major)
- Joint Major in Data Science and Mathematics
College of Arts and Science students cannot enter their junior year undeclared and must begin their data science and computer science course sequences no later than the spring semester of their sophomore year, which will allow them to declare the major or minor during the summer before their junior year. The Center for Data Science (CDS) and CAS both advise that students begin their data science courses earlier and declare the major in the spring of their sophomore year. Although students may begin their data science courses later than this point, there is no guarantee they will finish their major requirements in time to graduate within four years. Students cannot declare any major or joint major with CDS after completion of their junior year.
Required Coursework in CAS (-UA) for all Majors and Minors in Courant
At least half of the courses applied to the Courant requirements of the CAS majors and minors in Computer Science and in Mathematics (including joint programs) must be CSCI-UA and MATH-UA courses taken at the Washington Square campus or at NYU study away sites. This is a built-in limit on how many courses students may take in these subjects that are (for example) sponsored by NYU Abu Dhabi and NYU Shanghai under CS-UH, MATH-UH, CENG-SHU, CSCI-SHU, and MATH-SHU. Internal and external transfers must pay close attention to this policy, but it also applies to students who matriculate as first years.
The usual CAS policies on -UA residency for the baccalaureate degree still apply.
NYU Policies
University-wide policies can be found on the New York University Policy pages.
College of Arts and Science Policies
A full list of relevant academic policies can be found on the CAS Academic Policies page.