Data Science at NYU Shanghai is designed to create datadriven 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 widerange 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 20192020, 20202021, and 20212022.
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 MATHSHU 131 Calculus and CSCISHU 11 Introduction to Computer Programming (or CSCISHU 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.
Data Science Area Head
Updated on May 4th. 2020
1. BUSFSHU 101 Statistics for Business and Economics: BUSFSHU 101 Statistics for Business and Economics can satisfy the data science major requirements. This applies to all semesters and all academic bulletin years.
2. CSCISHU 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 CSCISHU 360 Machine Learning to fulfill Business majors Nonfinance/nonmarketing elective requirements.
Please note one course can only be used for two purposes. In this case, CSCISHU 360 Machine Learning will be used for the following three purposes, which is not permitted. 1) Business majors: Nonfinance/nonmarketing elective 2) Business majors: Business Analytics track requirement 3) Data Science major: Data Analysis requirement
Note: CSCISHU 360 Machine Learning can only fulfill the nonfinance elective requirement if students pursue a BA track under a Business major.
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 Internetconnected 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 postgraduation 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 datadriven computerscience 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.
Data Science Double Major Guidelines
Students who are interested in pursuing a Data Science major along with a Business major, an Economics major, a Mathematics major, a Neural Science or a Social Science major have the option to doublecount more than two courses between the majors. To complete both majors successfully, students would need to complete course requirements for both majors. However, the following courses are allowed to be double counted toward both majors:
Data Science (Concentration in Finance) and Business & Finance
• BUSFSHU 101 Statistics for Business and Economics
• BUSFSHU 202 Foundations of Finance
• BUSFSHU 250 Principles of Financial Accounting
• BUSFSHU 303 Corporate Finance
• ECONSHU 3 Microeconomics
Data Science (Concentration in Marketing) and Business & Marketing
• BUSFSHU 101 Statistics for Business and Economics
• BUSFSHU 202 Foundations of Finance
• BUSFSHU 250 Principles of Financial Accounting
• ECONSHU 3 Microeconomics
• MKTGSHU 1 Intro to Marketing
Data Science (Concentration in Economics) and Economics
• ECONSHU 1 Principles of Macroeconomics
• ECONSHU 3 Microeconomics
• ECONSHU 301 Econometrics
• MATHSHU 140 Linear Algebra
• MATHSHU 151 Multivariable Calculus
• MATHSHU 235 Probability and Statistics OR BUSFSHU 101 Statistics for Business and Economics
Note: Students who take both Linear Algebra and Multivariable Calculus can substitute Mathematics for Economists (Advanced Economics Elective) with these two courses. If the student chooses this option, they would need to take one Additional approved quantitative economics course.
Data Science (Concentration in Finance) and Economics
• ECONSHU 3 Microeconomics
• MATHSHU 140 Linear Algebra
• MATHSHU 151 Multivariable Calculus
• MATHSHU 235 Probability and Statistics OR BUSFSHU 101 Statistics for Business and Economics
Note: Students who take both Linear Algebra and Multivariable Calculus can substitute Mathematics for Economists (Advanced Economics Elective) with these two courses.
Data Science (Concentration in Finance) and Mathematics
• MATHSHU 140 Linear Algebra
• MATHSHU 151 Multivariable Calculus
• MATHSHU 235 Probability and Statistics OR MATHSHU 238 Honors Theory of Probability
Data Science (Concentration in Mathematics) and Math
• MATHSHU 140 Linear Algebra
• MATHSHU 151 Multivariable Calculus
• MATHSHU 201 Honors Calculus
• MATHSHU 235 Probability and Statistics OR MATHSHU 238 Honors Theory of Probability
• MATHSHU 329 Honors Analysis II OR MATHSHU 142 Honors Linear Algebra II
Data Science (Concentration in Mathematics) and Honors Math
• MATHSHU 141 Honors Linear Algebra I
• MATHSHU 142 Honors Linear Algebra II
• MATHSHU 238 Honors Theory of Probability
• MATHSHU 329 Honors Analysis II
Data Science (Concentration in Political Science) and Social Science (Political Science Track)
• SOCSSHU 150 Introduction to Comparative Politics
• SOCSSHU 160 Introduction to International Politics
• MATHSHU 235 Probability and Statistics OR BUSFSHU 101 Statistics for Business and Economics
Data Science (Concentration in Psychology) and Social Science (Psychology Track)
• PSYCSHU 101 Introduction to Psychology
• MATHSHU 235 Probability and Statistics OR BUSFSHU 101 Statistics for Business and Economics
• SOCSSHU 350 Empirical Research Practice
• Choose One:
SOCSSHU 334 Legal Psychology OR PSYCSHU 234 Developmental Psychology OR PSYCSHU 352 Psychology of Human Sexuality OR
Data Science (Concentration in Genomics) and Neural Science
• MATHSHU 140 Linear Algebra
• MATHSHU 235 Probability and Statistics
• BIOLSHU 21 Foundations of Biology I
• BIOLSHU 22 Foundations of Biology II
• BIOLSHU 123 Foundations of Biology Lab
Data Science (Concentration in Genomics) and Biology
• MATHSHU 140 Linear Algebra
• MATHSHU 235 Probability and Statistics
• BIOLSHU 21 Foundations of Biology I
• BIOLSHU 22 Foundations of Biology II
• BIOLSHU 123 Foundations of Biology Lab
• BIOLSHU 261 Genomics and Bioinformatics
Note for Data Science (Concentration in Genomics) and Neural Science & Data Science (Concentration
in Genomics) and Biology: Students who take Linear Algebra and Probability and Statistics are not
allowed to take the lowerlevel Math Tools for Life Science course. Students who have not decided yet to
pursue a double major and take Math Tools for Life Science first are required to take Linear Algebra and
Probability and Statistics.
Note: Computer Science and Data Science share many courses, so doublemajoring is not allowed. However,
students in Data Science can minor in Computer Science (and vice versa).
Double Major Sample Plans:
 Sample plans (Academic bulletin: 2022/2023 & later)
 Sample plans (Academic bulletin: 2019/2020; 2020/2021; 2021/2022)
 Sample plans (Academic bulletin: 2018/2019)
Declare your secondary major:
 Students need to complete more than half of the courses required for the primary and secondary majors.
 Students should present their fouryear plan that demonstrates they can complete all degree requirements to their academic advisor for review. Create your fouryear plan now!
 Center for Data Science and Artificial Intelligence
 Deans' Undergraduate Research Fund (DURF)
 CS, DS, and Engineering Research Night
 Bring your programming and data analysis skills to professors’ ongoing research projects!
Does not satisfy the major requirement. Students majoring in data science are permitted to work on an individual basis under the supervision of a fulltime faculty member in the relevant discipline if they have maintained an overall GPA of 3.0 and a GPA of 3.5 in data science and have a study proposal that is approved by the faculty and an academic area head.
Courses 
Prerequisites 
Semester 
CSCISHU 101 Introduction to Computer and Data Science 
prereq for CSCISHU 101 is CSCISHU 11 or placement exam 

BUSFSHU 101 Statistics for Business and Economics 
NA 

MATHSHU 233 Theory of Probability 


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

MATHSHU 235 Probability and Statistics 
Prereq for MATHSHU 235 is Grade C or better in either MATHSHU 131 (Calculus) or MATHSHU 201 (Honors Calculus). 

CSCISHU 210 Data Structures 
Prereq for CSCISHU 210 is ICS or A in ICP 

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

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

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

MATHSHU 141 Honors Linear Algebra I 


MATHSHU 265 Linear Algebra and Differential Equations 
prereq for MATHSHU 265 is Grade C or better in either MATHSHU 131 (Calculus) or MATHSHU 201 (Honors Calculus). 
Fall 
CSCISHU 360 Machine Learning 
Prereq for CSCISHU 360 is ICP, Calculus, Probability and Statistics OR Theory of Probability OR Statistics for Business and Economics 

ECONSHU 301 Econometrics 
Prereq for ECONSHU 301 is Statistics (BUSFSHU 101 OR MATHSHU 235 OR MATHSHU 233 OR ECONUA 18 OR STATUB 103 OR STATUB 1 OR MATHGA 2901 OR SOCSCUH 1010Q OR ECONUA 20). 

MATHSHU 234 Mathematical Statistics 


CSCISHU 220 Algorithms 
prereq for CSCISHU 220 is Data Structures and (Discrete Math or Honors Math major) and Calculus. 

CSCISHU 235 Information Visualization 
PREREQ FOR DATSSHU 235 is CSCISHU 210 Data Structures. 

CSCISHU 240 Introduction to Optimization and Mathematical Programming 
PREREQ FOR DATSSHU 240 is (ICP or ICS) AND (Calculus or Honors Calculus). 

CSCISHU 213 Databases 
Prereq for CSCISHU 213 is CSCISHU 210 Data Structures. 

DATSSHU 420 Data Science Senior Project 
Prereq for DS capstone is senior standing with DS primary or secondary major. 
Fall ONLY 
Study Away Considerations
 Courses: Before studying abroad, students are recommended to complete Introduction to Computer Science and Data Science, Data Structures, Econometrics, Probability and Statistics, Multivariable Calculus, and Machine Learning. Students who wish to study in New York ideally complete Databases.
 Location: Students planning to study away for two semesters are strongly encouraged to spend the first semester in a location other than New York. Applicants who spend the first semester away in another location will receive priority consideration for New York in their second semester away. Students who elect to spend the spring of their junior year in New York (versus the fall of the junior year) will have more earned credit points, which will enable them to have an earlier registration time and have a better chance of enrolling in highdemand courses.
 Senior Capstone: Students should not plan to study away during senior fall due to the inperson DS Senior Capstone course offering
Study Away Course Registration
Refer to the Fall 2023 Computer Science Prerequisites and Equivalents for course inforamtion. Please note that students must follow the prerequisites of the school hosting the course. For example, if Shanghai does not require a course for Class X, but New York does, then you will need to have that required course.
Python vs Java
In Shanghai, there are three course sequence ICP, ICDS, Data Structures (all taught in Python). At NYU CAS, there are the same three course sequence but teach ICS and Data Structures in Java. At Tandon, their threecourse 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 upperlevel 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.
Please note that, starting in 20222023, DATSSHU 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 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 realworld 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.
 Project Setup Guidelines
 Capstone Info Session (Slides)
 Course Instructors, Supervisors, and Mentors
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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