Program of Study (CAS Bulletin)

Data Science (2022 - 2024)

Policy on Declaration of Major or Minor in Data Science

Students must complete either DS-UA 111 or 112 (depending on placement) with a grade of C or better before they can declare the major or minor in data science or the joint major in data science and mathematics. To declare the joint major in computer and data science, students must first meet this prerequisite and also complete either CSCI-UA 101 or 102 (depending on placement) with a grade of C or better. These policies apply to all NYU students, not just to those matriculated in CAS. For the data science major, minor, and joint data science and mathematics major, students may declare during the declaration periods in the fall and spring semesters and the summer sessions. During the fall semester, the declaration period is the month of October; during the spring semester, the declaration period is mid-February to mid-March; and in the summer, the declaration period is mid-June to mid-July. For the joint computer science and data science major, students may declare anytime throughout the academic year. Please write to cds-undergraduate@nyu.edu to request the declaration form during the proper timeframes. For more information, please visit https://cds.nyu.edu/academics/undergraduate-program/.

It is an official policy in CAS that students cannot enter their junior year undeclared. In order to comply with this policy, students must begin their data science course sequence 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 and CAS both advise that students begin their data science courses earlier, and, consistent with the usual practice in CAS, declare the major or minor in the spring of their sophomore year. While 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.

Major in Data Science

The major requires thirteen 4-point courses (52 points) and the completion of any CAS minor. The minor requirement only applies to students pursuing data science as a single major (not applicable for students pursuing a joint or double major). The major requires:

The following five courses (20 points) sponsored by the NYU Center for Data Science:

  • Data Science for Everyone (DS-UA 111, offered every semester)
  • Introduction to Data Science (DS-UA 112, offered every semester)
  • Causal Inference (DS-UA 201, offered every fall)
  • Responsible Data Science (DS-UA 202, offered every spring)
  • Advanced Topics in Data Science (DS-UA 301, offered every spring)

The following four courses (16 points) sponsored by NYU's Department of Computer Science (Courant):

  • Introduction to Computer Science (CSCI-UA 101)
  • Data Structures (CSCI-UA 102)
  • Fundamentals of Machine Learning (CSCI-UA 473)
  • Data Management and Analysis (CSCI-UA 479)

The following four courses (16 points) sponsored by NYU's Department of Mathematics (Courant):

  • Calculus I (MATH-UA 121) or Mathematics for Economics I (MATH-UA 131; formerly MATH-UA 211)
  • Calculus II (MATH-UA 122) or Mathematics for Economics II (MATH-UA 132; formerly MATH-UA 212)
  • Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148)
  • Probability and Statistics (MATH-UA 235)

Completion of any CAS minor. CAS minors range from four to six courses. This minor requirement only applies to students pursuing data science as a single major. Please note that due to the substantial overlap with computer science, data science majors cannot minor in computer science.

For descriptions and scheduling of, and prerequisites for, courses outside of DS-UA, please consult the relevant departmental sections of this Bulletin.

Policies Applying to the Major

  • 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.
  • Two courses may be double-counted between the data science major and another major. For permission to double-count more than two courses, students must first request approval from the Center for Data Science (cds-undergraduate@nyu.edu). If approved by the CDS, students must then petition CAS Academic Standards (726 Broadway, 7th floor; 212-998-8140).
  • Advanced Placement credit (or other advanced standing credit by examination) in computer science and calculus is treated exactly as in the majors and minors in computer science and mathematics. Consult the AP and other tables in the admission section of this Bulletin for course equivalencies.
  • Students must check the prerequisites for each course before enrolling. See the section on course offerings for all prerequisites.
  • 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.

Recommended Program of Study for the Major in Data Science

Year One, Fall Term:

  • Data Science for Everyone (DS-UA 111)
  • Calculus I (MATH-UA 121) or Mathematics for Economics I (MATH-UA 131; formerly MATH-UA 211)
  • If required, the prerequisite course Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2) or Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3). Students who do not need the prerequisite may enroll in Introduction to Computer Science (CSCI-UA 101).

Year One, Spring Term:

  • Introduction to Data Science (DS-UA 112)
  • Calculus II (MATH-UA 122) or Mathematics for Economics II (MATH-UA 132; formerly MATH-UA 212)
  • Introduction to Computer Science (CSCI-UA 101)

Year Two, Fall Term:

  • Data Structures (CSCI-UA 102)
  • Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148)

Year Two, Spring Term:

  • Probability and Statistics (MATH-UA 235)
  • Data Management and Analysis (CSCI-UA 479)

Year Three, Fall Term:

  • Causal Inference (DS-UA 201)
  • Fundamentals of Machine Learning (CSCI-UA 473)

Year Three, Spring Term:

  • Responsible Data Science (DS-UA 202)
  • Advanced Topics in Data Science (DS-UA 301)

Joint Major in Computer and Data Science

The prerequisite for declaring this major is completion of (1) either CSCI-UA 101 or 102 (depending on placement) and also (2) either DS-UA 111 or 112 (depending on placement) with a C or better. Offered by the Center for Data Science and the Courant Institute of Mathematical Sciences, this interdisciplinary major is designed for students who seek comprehensive training in two bodies of knowledge: (1) computer science, an established field that advances computing, programming, and building large-scale and intelligent systems, and (2) data science, an emerging field that leverages computer science, mathematics, and domain-specific 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.

The joint major requires 18 courses (72 points) taken in three departments: computer science, data science, and mathematics. 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 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. For descriptions and scheduling of, and prerequisites for, courses outside of DS-UA, please consult the relevant departmental sections of this Bulletin.

The computer science requirements (eight courses/32 points) are as follows:

  • Introduction to Computer Science (CSCI-UA 101)
  • Data Structures (CSCI-UA 102)
  • Computer Systems Organization (CSCI-UA 201)
  • Basic Algorithms (CSCI-UA 310)
  • Fundamentals of Machine Learning (CSCI-UA 473)
  • Data Management and Analysis (CSCI-UA 479)
  • Big data elective: choose one of the following:
    • Predictive Analytics (CSCI-UA 475)
    • Processing Big Data for Analytics Applications (CSCI-UA 476)
  • Computer science elective: choose one of the following:
    • Operating Systems (CSCI-UA 202)
    • Predictive Analytics (CSCI-UA 475)
    • Processing Big Data for Analytics Applications (CSCI-UA 476)
    • Special Topics: Computer Networks (CSCI-UA 480)
    • Special Topics: Introduction to Numerical Optimization (CSCI-UA 480)
    • Special Topics: Introduction to Social Networking (CSCI-UA 480)
    • Special Topics: Natural Language Processing (CSCI-UA 480)
    • Special Topics: Parallel Computing (CSCI-UA 480)

The data science requirements (five courses/20 points) are as follows:

  • Data Science for Everyone (DS-UA 111, offered every semester)
  • Introduction to Data Science (DS-UA 112, offered every semester)
  • Causal Inference (DS-UA 201, offered every fall)
  • Responsible Data Science (DS-UA 202, offered every spring)
  • Advanced Topics in Data Science (DS-UA 301, offered every spring)

The mathematics requirements (five courses/20 points) are as follows:

  • Discrete Mathematics (MATH-UA 120)
  • Calculus I (MATH-UA 121) or Mathematics for Economics I (MATH-UA 131; formerly MATH-UA 211)
  • Calculus II (MATH-UA 122) or Mathematics for Economics II (MATH-UA 132; formerly MATH-UA 212)
  • Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148)
  • Probability and Statistics (MATH-UA 235)

For descriptions and scheduling of, and prerequisites for, courses outside of DS-UA, please consult the relevant departmental sections of this Bulletin.

Joint Major in Data Science and Mathematics

The prerequisite for declaring this major is completion of either DS-UA 111 or 112 (depending on placement) with a C or better. Offered by the Center for Data Science and the Courant Institute of Mathematical Sciences, this interdisciplinary major trains students to use data science methods and enables them to understand the mathematical theories that go into the analysis of large data sets. It will allow students to apply mathematical theories to real-world challenges that require data science and computational solutions.

The joint major requires 18 courses (72 points) taken in three departments: data science, computer science, and mathematics. A grade of C or higher is required in all courses used to fulfill joint major requirements (courses taken under the Pass/Fail option cannot be counted toward the major). For descriptions and scheduling of, and prerequisites for, courses outside of DS-UA, please consult the relevant departmental sections of this Bulletin.

The data science requirements (five courses/20 points) are as follows:

  • Data Science for Everyone (DS-UA 111, offered every semester)
  • Introduction to Data Science (DS-UA 112, offered every semester)
  • Causal Inference (DS-UA 201, offered every fall)
  • Responsible Data Science (DS-UA 202, offered every spring)
  • Advanced Topics in Data Science (DS-UA 301, offered every spring)

The mathematics requirements (nine courses/36 points) are as follows:

  • Discrete Mathematics (MATH-UA 120)
  • Calculus I (MATH-UA 121)
  • Calculus II (MATH-UA 122)
  • Calculus III (MATH-UA 123) or Honors Calculus III (MATH-UA 129)
  • Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148)
  • Theory of Probability (MATH-UA 233) or Honors Theory of Probability (MATH-UA 238)
  • Mathematical Statistics (MATH-UA 234)
  • Numerical Analysis (MATH-UA 252)
  • Analysis (MATH-UA 325) or Honors Analysis I (MATH-UA 328)

The computer science requirements (four courses/16 points) are as follows:

  • Introduction to Computer Science (CSCI-UA 101)
  • Data Structures (CSCI-UA 102)
  • Fundamentals of Machine Learning (CSCI-UA 473)
  • Data Management and Analysis (CSCI-UA 479)

For descriptions and scheduling of, and prerequisites for, courses outside of DS-UA, please consult the relevant departmental sections of this Bulletin.

Minor in Data Science

The prerequisite for declaring this minor is completion of either DS-UA 111 or 112 (depending on placement) with a C or better. The minor requires five 4-point courses (20 points). All students in the minor will take these three courses offered by the Center for Data Science:

  • Data Science for Everyone (DS-UA 111, offered every semester)
  • Introduction to Data Science (DS-UA 112, offered every semester)
  • Causal Inference (DS-UA 201, offered every fall)

Students will also take two courses offered by the Department of Computer Science (Courant), and should consult that departmental section in this Bulletin for prerequisites and descriptions. Choose one pathway:

Students Entering the Minor with No Prior Programming Experience:

  • Introduction to Computer Programming (No Prior Experience) (CSCI-UA 2)
  • Either Database Design and Implementation (CSCI-UA 60) or Programming Tools for the Data Scientist (CSCI-UA 381)

Students Entering the Minor with Limited Prior Programming Experience:

  • Introduction to Computer Programming (Limited Prior Experience) (CSCI-UA 3)
  • Either Database Design and Implementation (CSCI-UA 60) or Programming Tools for the Data Scientist (CSCI-UA 381)

Students Entering the Minor with Extensive Prior Programming Experience:

  • Database Design and Implementation (CSCI-UA 60) or Data Management and Analysis
    (CSCI-UA 479)
  • Programming Tools for the Data Scientist (CSCI-UA 381)

For descriptions and scheduling of, and prerequisites for, courses outside of DS-UA, please consult the relevant departmental sections of this Bulletin.

Policies Applying to the Minor

  • All students who wish to minor in data science must complete a minor registration form, and must consult a minor adviser prior to any registration. Non-CAS students should fill out the minor declaration form via Albert. Please consult the page detailing policies and procedures for cross-school minors: http://cas.nyu.edu/academic-programs/majors-and-minors/cross-school-minors.html.
  • A grade of C or better is required in all courses used to fulfill minor requirements; courses graded Pass/Fail do not count toward the minor. This policy applies to all NYU students, not just to those matriculated in CAS.
  • Students must check the prerequisites for each course before enrolling. See the section on course offerings for all prerequisites.
  • Because of the substantial curricular overlap between computer science, data science, and mathematics, students who choose to minor in data science may double-count only one course between the computer science or mathematics major or minor and the data science minor. Students should consult the guidelines of their major or minor for any additional restrictions and policies.
  • In accordance with the cross-school minor policy, CAS students may not minor in data science at NYU Shanghai. They may, however, take applicable Shanghai courses and count them toward the CAS data science major or minor. Shanghai courses do not count toward the 64-point UA residency requirement required of internal and external transfers to the College.