# Data Science (2020 - 2022)

## Policy on Declaration of Major or Minor in Data Science

Students must complete one DS-UA course with a recorded grade of C or better before they can declare the major or minor in data science or the joint majors in (1) computer and data science and (2) data science and mathematics. This policy applies to all NYU students, not just to those matriculated in CAS. 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. Please write to *cds-undergraduate@nyu.edu* to request the declaration form during these timeframes. For more information, please visit *https://cds.nyu.edu/academics/undergraduate-program/.*

It is official CAS policy that students cannot enter their junior year undeclared. 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. If students begin their data science courses later than this point, there is no guarantee they will finish their major or minor 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)
- Introduction to 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 211)
- Calculus II (MATH-UA 122) or Mathematics for Economics II (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 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 (Silver Center; Room 909; 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.

## Sample 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 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 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)
- Introduction to Machine Learning (CSCI-UA 473)

**Year Three, Spring Term:**

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

## Joint Major in Computer and Data Science

The joint major in computer and data science 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.

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. For descriptions 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)
- Introduction to 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)
- Introduction to Data Science (DS-UA 112)
- Causal Inference (DS-UA 201)
- Responsible Data Science (DS-UA 202)
- Advanced Topics in Data Science (DS-UA 301)

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 211)
- Calculus II (MATH-UA 122) or Mathematics for Economics II (MATH-UA 212)
- Linear Algebra (MATH-UA 140) or Honors Linear Algebra (MATH-UA 148)
- Probability and Statistics (MATH-UA 235)

## Joint Major in Data Science and Mathematics

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 need data science and computational solutions.

The joint major requires 18 courses (72 points) taken in three departments: data science, mathematics, and computer science. 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 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)
- Introduction to Data Science (DS-UA 112)
- Causal Inference (DS-UA 201)
- Responsible Data Science (DS-UA 202)
- Advanced Topics in Data Science (DS-UA 301)

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)
- Linear Algebra (MATH-UA 140)
- Theory of Probability (MATH-UA 233) or Honors Theory of Probability (MATH-UA 238)
- Statistics (MATH-UA 234)
- Numerical Analysis (MATH-UA 252)
- Analysis (MATH-UA 325)

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

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

## Minor in Data Science

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)
- Introduction to Data Science (DS-UA 112)
- Causal Inference (DS-UA 201)

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)

## 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.
- A grade of C or better is required in all courses used to fulfill minor requirements (Pass/Fail grades do not count). 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.