Data Science

Data Science at NYU Shanghai is designed to create data-driven leaders with a global perspective, a broad education, and the capacity to think creatively. Data science involves using computerized methods to analyze massive amounts of data and to extract knowledge from them. Data science addresses a wide-range of data types, including scientific and economic numerical data, textual data, and image and video data.

Requirements for the Major

Students can choose to follow the academic bulletin from the year that they were admitted or a more recent academic bulletin. For example, if you were admitted to NYU Shanghai in Fall 2019, you can choose to follow the academic bulletin 2019-2020, 2020-2021, and 2021-2022.

Planning the Major

To declare the Data Science major, students must have a final grade of C, or are currently enrolled in the following courses in MATH-SHU 131 Calculus and CSCI-SHU 11 Introduction to Computer Programming (or CSCI-SHU 101 Introduction to Computer Science).

Faculty Mentors

Faculty mentors are the leading faculty and experts in the major disciplines. Students can reach out to faculty mentors for specific questions about the major, and references for connecting with relevant discipline resources. If you have specific questions about specific fields of study within the major, you can search for faculty through the faculty directory.

 

Keith Ross

Dean of Engineering and Computer Science

 
Data Science FAQs
Announcements & Updates

Updated on May 4th. 2020

1. BUSF-SHU 101 Statistics for Business and Economics: BUSF-SHU 101 Statistics for Business and Economics can satisfy the data science major requirements. This applies to all semesters and all academic bulletin years.

2. CSCI-SHU 360 Machine Learning (Refer to Requirements for the Business Tracks)

Students who plan to pursue a double major in Data Science and Business majors with Business Analytics Track, cannot use CSCI-SHU 360 Machine Learning to fulfill Business majors Non-finance/non-marketing elective requirements. 

Please note one course can only be used for two purposes. In this case, CSCI-SHU 360 Machine Learning will be used for the following three purposes, which is not permitted. 1) Business majors: Non-finance/non-marketing elective 2) Business majors:  Business Analytics track requirement 3) Data Science major: Data Analysis requirement 

Note: CSCI-SHU 360 Machine Learning can only fulfill the non-finance elective requirement if students pursue a BA track under a Business major. 

3. 2022-2023 Academic Bulletin Update: Computer science and data science share many courses, so double-majoring is not allowed. However, Students in data science can minor in computer science.

 
Why should I major in Data Science?

Data Science draws from methodologies and tools in several well established fields, including computer science, statistics, applied mathematics, and economics. Data science has applications in just about every academic discipline, including sociology, political science, digital humanities, linguistics, finance, marketing, urban informatics, medical informatics, genomics, image content analysis, and all branches of engineering and the physical sciences.  The importance of data science is expected to accelerate in the coming years, as data from the web, mobile sensors, smartphones, and Internet-connected instruments continues to grow.

What knowledge and skills will students acquire by majoring in Data Science?

Students who complete the major will not only have expertise in computer programming, statistics, and data mining, but also know how to combine these tools to solve contemporary problems in a discipline of their choice, including the social science, physical science, and engineering disciplines. 

What are post-graduation and career opportunities for Data Science students?

Upon graduation, data science majors have numerous career paths. You can go on to graduate school in data science, computer science, social science, business, finance, medicine, law, linguistics, education, and so on. Outside of academe, there are also myriad career paths. Not only can you pursue careers with traditional data-driven computer-science companies and startups such as Google, Facebook, Amazon, and Microsoft, but also with companies in the transportation, energy, medical, and financial sectors. You can also pursue careers in the public sector, including urban planning, law enforcement, and education.

Double Major in Data Science Guidelines

Double Major Guidelines:

If you are interested in pursuing a Data Science major along with an Economics major, a Computer Science major, a Business major, or a Mathematics major, these are the relevant guidelines:

  • The course requirements need to be satisfied in both majors.
  • More than two courses may be double-counted between the majors but each major must have at least 7 singly-counted courses.
  • The double major must be approved by the faculty and Deans responsible for the two majors. Students should first work with their academic advisor to initiate this process.
  • Double-counted courses cannot also be counted for the core curriculum requirements since each course can only count for at most two requirements.

Double Major Sample Plans:

Declare your secondary major:

  • Students need to successfully complete more than half of the courses required for the primary and secondary majors.
  • Students should present their four-year plan that demonstrates they can complete all degree requirements to their academic advisor for review.  Create your four-year plan now!
Research Opportunities
Independent Study

Does not satisfy the major elective requirement. Students majoring in Computer Science, Data Science, or Engineering are permitted to work on an individual basis under the supervision of a full-time faculty member in the relevant discipline if they have maintained an overall GPA of 3.0 and a GPA of 3.5 in Computer Science/Data Science/Engineering and have a study proposal that is approved by a Computer Science/Data Science/Engineering professor.

Course Prerequisites

Courses 

Prerequisites

Semester

CSCI-SHU 101 Introduction to Computer and Data Science

prereq for CSCI-SHU 101 is CSCI-SHU 11 or placement exam

 

BUSF-SHU 101 Statistics for Business and Economics

NA

 

MATH-SHU 233 Theory of Probability

 

 

MATH-SHU 238 Honors Theory of Probability

PREREQ FOR MATH-SHU 238 is Grade C or better in either MATH-SHU 151 (Multivariable Calculus) or MATH-SHU 329 (Honors Analysis II), and grade C or better in either MATH-SHU 140 (Linear Algebra) or MATH-SHU 141 (Honors Linear Algebra I).

 

MATH-SHU 235 Probability and Statistics

Prereq for MATH-SHU 235 is Grade C or better in either MATH-SHU 131 (Calculus) or MATH-SHU 201 (Honors Calculus).

 

CSCI-SHU 210 Data Structures

Prereq for CSCI-SHU 210 is ICS or A- in ICP

 

MATH-SHU 151 Multivariable Calculus

Prereq for MATH-SHU 151 is Grade C or better in either MATH-SHU 131 (Calculus) or MATH-SHU 201 (Honors Calculus).Antirequisite: MATH-SHU 329 (Honors Analysis II)

 

MATH-SHU 328 Honors Analysis I

Prereq for MATH-SHU 328 is Grade C or better in MATH-SHU 201 (Honors Calculus), or grade A- or better in MATH-SHU 131 (Calculus) and A- or better in MATH-SHU 143 (Foundations of Mathematical Methods), or authorization of the instructor.

 

MATH-SHU 140 Linear Algebra

prereq for MATH-SHU 140 is Sufficient high school grades, or NYU SH “Calculus and Linear Algebra” placement exam, or a grade of C or better in MATH-SHU 9 (Precalculus).

 

MATH-SHU 141 Honors Linear Algebra I

 

 

MATH-SHU 265 Linear Algebra and Differential Equations

prereq for MATH-SHU 265 is Grade C or better in either MATH-SHU 131 (Calculus) or MATH-SHU 201 (Honors Calculus).

Fall 

CSCI-SHU 360 Machine Learning

Prereq for CSCI-SHU 360 is ICP, Calculus, Probability and Statistics OR Theory of Probability OR Statistics for Business and Economics

 

ECON-SHU 301 Econometrics

Prereq for ECON-SHU 301 is Statistics (BUSF-SHU 101 OR MATH-SHU 235 OR MATH-SHU 233 OR ECON-UA 18 OR STAT-UB 103 OR STAT-UB 1 OR MATH-GA 2901 OR SOCSC-UH 1010Q OR ECON-UA 20).

 

MATH-SHU 234 Mathematical Statistics

 

 

CSCI-SHU 220 Algorithms

prereq for CSCI-SHU 220 is Data Structures and (Discrete Math or Honors Math major) and Calculus.

 

CSCI-SHU 235 Information Visualization

PREREQ FOR DATS-SHU 235 is CSCI-SHU 210 Data Structures.

 

CSCI-SHU 240 Introduction to Optimization and Mathematical Programming

PREREQ FOR DATS-SHU 240 is (ICP or ICS) AND (Calculus or Honors Calculus).

 

CSCI-SHU 213 Databases

Prereq for CSCI-SHU 213 is CSCI-SHU 210 Data Structures.

 

DATS-SHU 420 Data Science Senior Project

Prereq for DS capstone is senior standing with DS primary or secondary major.

Fall ONLY

 

Python vs Java
  1. In Shanghai, there are three course sequence ICP, ICS, Data Structures (all taught in Python)
  2. At NYU CAS, there are the same three course sequence but teach ICS and Data Structures in Java.
  3. At Tandon, their three-course sequence is ICP, Data Structure, Object Oriented Programming. ICP and Data Structures is taught in Python,  OOP in Java.  

As an NYU Shanghai CS and Data Science major, students can take ICS or Data Structures in Python or Java. However:

  • If you are a DS major, we highly recommend you take both ICS and Data Structures in Python. Python is by far the most prominent language in data science, and several of our upper-level DS courses are taught in Python. But if it is difficult to get into a Python class, then you can take these courses in Java. 
  • For CS majors, either Java or Python is fine. But you should be warned that if you take ICS in Java and then return to NYU Shanghai, you’ll be taking Data Structures in Python, and may be at a disadvantage to those who took ICS in Python. 
  • All students should be warned that if they take  ICS  in Python, and then take Data Structures in NY in Java, they may be at a disadvantage since this would be their first course using Java, whereas most NY students will already have had ICS in Java. 
Senior Project

Please note that, starting in 2022-2023, DATS-SHU 420 Computer Science Senior Project will ONLY be offered in the Fall. The Senior Project course won't be offered in the Spring Semester. Check out the Fall 2022 Data Science Senior Project Info Session (Recording) and CS/DS senior projects from the previous classes!

1. What is the structure of the Data Science Senior Capstone course?

The goal of this class is to complete a concrete CS project from start to finish. You can either solve a research problem or try to tackle a real-world problem. You need to design a valid method/approach to solve the problem, build a solution using your method, and assess the quality of your solution. You may either work alone or form a team of at most 3 students.

2. What are the requirements of the capstone project?

At the end of the project, you must prepare a written technical report and a presentation. The final project report must be structured as a typical technical paper and will include four main sections:

  • Motivation, problem definition
  • Related literature and existing approaches
  • Proposed solution and details of implementation
  • Results, conclusion, and directions for improvement

3. What should students prepare in advance to get ready for the capstone?

  • Choose a research topic that NYU faculty have submitted or come up with a valuable topic of your own.
  • Contact a faculty supervisor whose area of expertise matches the field of your topic, and start preparing as early as possible.
 

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