Computer and Data Science (BA)
Program Description
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 comprised of eighteen courses (72 credits) offered by the Department of Computer Science and the Center for Data Science.
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 Data Science for Everyone or DS-UA 112 Principles of Data Science (depending on placement) with a C or better.
| Course | Title | Credits |
|---|---|---|
| General Education Requirements | ||
| First-Year Seminar | 4 | |
| EXPOS-UA 1 | Writing as Inquiry | 4 |
| Foreign Language 1 | 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 2 | Introduction to Computer Programming (No Prior Experience) 2 | 4 |
| CSCI-UA 101 | Intro to Computer Science | 4 |
| CSCI-UA 102 | Data Structures | 4 |
| CSCI-UA 201 | Computer Systems Org | 4 |
| CSCI-UA 310 | Basic Algorithms | 4 |
| CSCI-UA 473 | Fundamentals of Machine Learning | 4 |
| CSCI-UA 475 | Predictive Analytics | 4 |
| or CSCI-UA 476 | Processing Big Data for Analytics Applications | |
| CSCI-UA 479 | Data Management and Analysis | 4 |
| Data Science Requirements | ||
| DS-UA 111 | Data Science for Everyone | 4 |
| DS-UA 112 | Principles of Data Science | 4 |
| DS-UA 201 | Causal Inference | 4 |
| DS-UA 202 | Responsible Data Science | 4 |
| DS-UA 301 | Advanced Topics in Data Science | 4 |
| Mathematics Requirements | ||
| MATH-UA 120 | Discrete Mathematics | 4 |
| MATH-UA 121 | Calculus I 3 | 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 140 | Linear Algebra | 4 |
| or MATH-UA 148 | Honors Linear Algebra | |
| MATH-UA 235 | Probability & Statistics | 4 |
| Electives | ||
| Select one Computer Science elective: | 4 | |
| Operating Systems | ||
| Natural Language Processing | ||
| Predictive Analytics | ||
| Processing Big Data for Analytics Applications | ||
| Special Topics: (Computer Networks) | ||
| Special Topics: (Introduction to Numerical Optimization) | ||
| Special Topics: (Introduction to Social Networking) | ||
| Special Topics: (Parallel Computing) | ||
| Other Elective Credits | 4 | |
| Total Credits | 128 | |
- 1
The foreign language requirement is satisfied upon successful completion through the Intermediate level of a language. This may be accomplished in fewer than 16 credits, but those credits must then be completed as elective credit.
- 2
This course does not count towards the joint major but is a required prerequisite for CSCI-UA 101 Intro to Computer Science.
- 3
Students must choose one of the two calculus tracks and cannot take courses from both tracks.
Note: 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.
Sample Plan of Study
| 1st Semester/Term | Credits | |
|---|---|---|
| CSCI-UA 2 | Introduction to Computer Programming (No Prior Experience) | 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 | Data Science for Everyone | 4 |
| CSCI-UA 102 | Data Structures | 4 |
| MATH-UA 120 | Discrete Mathematics | 4 |
| Foreign Language I | 4 | |
| Credits | 16 | |
| 4th Semester/Term | ||
| DS-UA 112 | Principles of Data Science | 4 |
| CSCI-UA 201 | Computer Systems Org | 4 |
| MATH-UA 140 | Linear Algebra | 4 |
| Foreign Language II | 4 | |
| Credits | 16 | |
| 5th Semester/Term | ||
| CSCI-UA 310 | Basic Algorithms | 4 |
| CSCI-UA 479 | Data Management and Analysis | 4 |
| Foreign Language III | 4 | |
| Expressive Culture | 4 | |
| Credits | 16 | |
| 6th Semester/Term | ||
| MATH-UA 235 | Probability & Statistics | 4 |
| CSCI-UA 475 | Predictive Analytics | 4 |
| Foreign Language IV | 4 | |
| Societies and the Social Sciences | 4 | |
| Credits | 16 | |
| 7th Semester/Term | ||
| DS-UA 201 | Causal Inference | 4 |
| CSCI-UA 202 | Operating Systems | 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 |
| CSCI-UA 473 | Fundamentals of Machine Learning | 4 |
| Life Science | 4 | |
| Credits | 16 | |
| Total Credits | 128 | |
Learning Outcomes
Upon completion of program requirements, students are expected to have developed:
- Fundamental theoretical and practical knowledge of the foundational areas of computer science, including algorithm design, machine learning, and programming.
- Knowledge of current methods and tools used to analyze big data and inferences, and to explore data-driven decision making.
- Knowledge of ethical issues regarding data science. These include the topics of fairness, diversity, and privacy.
- The ability to build and use data science systems, the automated systems that effectively predict outcomes of interest and that extract insights from increasingly large data sets.
- 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 Introduction to Computer Science (CSCI-UA 101) students must first fulfill the prerequisite Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3). 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), 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 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 Data Structures (CSCI-UA 102) but are not obliged to; they may choose to take CSCI-UA 101 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 through CSCI-UA 201 in sequence.
- Note that Albert will automatically block: students who complete CSCI-UA 2 with a C or better from registering for CSCI-UA 3; students who complete CSCI-UA 467 with a C or better from registering for CSCI-UA 61; and students who complete CSCI-UA 479 with a C or better from registering for CSCI-UA 60.
- 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.
Policy on Declaration of Major
Students must complete either CSCI-UA 101 or 102 (depending on placement) and also complete either DS-UA 111 or 112 (depending on placement) 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.
Students are not able to double major in Computer Science and Data Science. To pursue both disciplines, students must declare the joint computer science and data science major.
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.