Data Science

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. 

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.

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 double-count 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
• BUSF-SHU 101 Statistics for Business and Economics
• BUSF-SHU 202 Foundations of Finance
• BUSF-SHU 250 Principles of Financial Accounting
• BUSF-SHU 303 Corporate Finance
• ECON-SHU 3 Microeconomics

Data Science (Concentration in Marketing) and Business & Marketing
• BUSF-SHU 101 Statistics for Business and Economics
• BUSF-SHU 202 Foundations of Finance
• BUSF-SHU 250 Principles of Financial Accounting
• ECON-SHU 3 Microeconomics
• MKTG-SHU 1 Intro to Marketing

Data Science (Concentration in Economics) and Economics
• ECON-SHU 1 Principles of Macroeconomics
• ECON-SHU 3 Microeconomics
• ECON-SHU 301 Econometrics
• MATH-SHU 140 Linear Algebra
• MATH-SHU 151 Multivariable Calculus
• MATH-SHU 235 Probability and Statistics OR BUSF-SHU 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
• ECON-SHU 3 Microeconomics
• MATH-SHU 140 Linear Algebra
• MATH-SHU 151 Multivariable Calculus
• MATH-SHU 235 Probability and Statistics OR BUSF-SHU 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
• MATH-SHU 140 Linear Algebra
• MATH-SHU 151 Multivariable Calculus
• MATH-SHU 235 Probability and Statistics OR MATH-SHU 238 Honors Theory of Probability

Data Science (Concentration in Mathematics) and Math
• MATH-SHU 140 Linear Algebra
• MATH-SHU 151 Multivariable Calculus
• MATH-SHU 201 Honors Calculus
• MATH-SHU 235 Probability and Statistics OR MATH-SHU 238 Honors Theory of Probability
• MATH-SHU 329 Honors Analysis II OR MATH-SHU 142 Honors Linear Algebra II

Data Science (Concentration in Mathematics) and Honors Math
• MATH-SHU 141 Honors Linear Algebra I
• MATH-SHU 142 Honors Linear Algebra II
• MATH-SHU 238 Honors Theory of Probability
• MATH-SHU 329 Honors Analysis II

Data Science (Concentration in Political Science) and Social Science (Political Science Track)
• SOCS-SHU 150 Introduction to Comparative Politics
• SOCS-SHU 160 Introduction to International Politics
• MATH-SHU 235 Probability and Statistics OR BUSF-SHU 101 Statistics for Business and Economics

Data Science (Concentration in Psychology) and Social Science (Psychology Track)
• PSYC-SHU 101 Introduction to Psychology
• MATH-SHU 235 Probability and Statistics OR BUSF-SHU 101 Statistics for Business and Economics
• SOCS-SHU 350 Empirical Research Practice
• Choose One:
SOCS-SHU 334 Legal Psychology OR PSYC-SHU 234 Developmental Psychology OR PSYC-SHU 352 Psychology of Human Sexuality

Data Science (Concentration in Genomics) and Neural Science
• MATH-SHU 140 Linear Algebra
• MATH-SHU 235 Probability and Statistics
• BIOL-SHU 21 Foundations of Biology I
• BIOL-SHU 22 Foundations of Biology II
• BIOL-SHU 123 Foundations of Biology Lab

Data Science (Concentration in Genomics) and Biology
• MATH-SHU 140 Linear Algebra
• MATH-SHU 235 Probability and Statistics
• BIOL-SHU 21 Foundations of Biology I
• BIOL-SHU 22 Foundations of Biology II
• BIOL-SHU 123 Foundations of Biology Lab
• BIOL-SHU 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 lower-level 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 double-majoring 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)
• 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 four-year plan that demonstrates they can complete all degree requirements to their academic advisor for review.  Create your four-year 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 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 data science and have a study proposal that is approved by the faculty and an academic area head.

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 high-demand courses.
• Senior Capstone: Students should not plan to study away during senior fall due to the in-person DS Senior Capstone course offering

Study Away Course Registration

Refer to the Fall 2023 Computer Science Pre-requisites 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 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. 

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 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.

• Project Setup Guidelines
• Spring 2024 Info Session Recording
• Spring 2024 Presentation Slides
• Spring 2024 Faculty-proposed Topics
• 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.