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. This new discipline 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.

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. 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.

 

Degree Requirements – 2020-21 Bulletin

* = offered in Fall ’20 in Shanghai

Prerequisite Courses
CSCI-SHU 101 Introduction to Computer Science* Pre-req: ICP or placement exam
Choose one Statistics course from the following two
MATH-SHU 235 Probability and Statistics* Pre-req: Calculus
MATH-SHU 233 Honors Theory of Probability Pre-reqs: “Honors Analysis 1” and “Linear Algebra or Honors Linear Algebra 1” 
Programming/Computer Science Courses
CSCI-SHU 210 Data Structures* Pre-req: ICS, or A- and above in ICP
Math Courses  
Choose one course from the following two
MATH-SHU 151  Multivariable Calculus*  Pre-req: Calculus
MATH-SHU 328 Honors Analysis I Pre-req: Honors Calculus
Choose one course from the following three
MATH-SHU 140 Linear Algebra* Pre-req: Calculus
MATH-SHU 265 Linear Algebra and Differential Equations* Pre-req: Calculus
MATH-SHU 141 Honors Linear Algebra I* Pre-req or Co-req: Honors Calculus
Data Analysis Courses
CSCI-SHU 360 Machine Learning* Pre-reqs: ICP,Calculus/Honors Calculus, Probability and Statistics or Honors Theory of Probability
Choose one course from the following two

ECON-SHU 301

Econometrics*

Pre-req: a prior Stats course

MATH-SHU 234 Mathematical Statistics* (formerly The Mathematics of Statistics and Data Science, Part 1) Pre-reqs: Multivariable Calculus or Honors Analysis 2, Linear Algebra or Honors Linear Algebra 1, and Probability and Statistics or Honors Theory of Probability​
Choose one course from the following three
CSCI-SHU ​235 Information Visualization* Pre-req: Data Structures
CSCI-SHU 220 Algorithms* Pre-reqs: Data Structures, Discrete Math or Linear Algebra or Honors Linear Algebra 1
CSCI-SHU 240 Introduction to Optimization and Mathematical Programming Pre-reqs: ICP; AND Calculus or Honor Calculus; AND Prob and Stats or Stats for Bus and Econ or Theory of Probability 
Data Management Course
CSCI-SHU 213 / CS-UY 3083 Databases* Pre-req: Data Structures
Concentration Courses
Domain-area courses
CSCI-SHU 420 Data Science Capstone
Concentration Options
Domain-Area Courses for Concentration in Finance
  Data Science Capstone (Not Required for students who are enrolled in 6-credit Business and Econ Honors Program)
ECON-SHU 3 Microeconomics*
BUSF-SHU 250 Principles of Financial Accounting*
BUSF-SHU 202 Foundations of Finance*
BUSF-SHU 303 Corporate Finance*

14 courses total.

Domain-Area Courses for Concentration in Marketing
  Data Science Capstone (Not Required for students who are enrolled in 6-credit Business and Econ Honors Program)
ECON-SHU 3 Microeconomics*
BUSF-SHU 250 Principles of Financial Accounting*
BUSF-SHU 202 Foundations of Finance*
MKTG-SHU 1 Introduction to Marketing*

14 courses total.

Domain-Area Courses for Concentration in Economics
  Data Science Capstone (Not Required for students who are enrolled in 6-credit Business and Econ Honors Program)
ECON-SHU 3 Microeconomics*
ECON-SHU 1 Principles of Macroeconomics*

12 courses total.

Domain-Area Courses for Concentration in Genomics
  Data Science Capstone
BIOL-SHU 21 Foundations of Biology 1 and lab
BIOL-SHU 22 Foundations of Biology 2 and lab*
BIOL-SHU 261 Genomics and Bioinformatics*

Foundations of Biology 1 can count as core curriculum course.

12 courses total.

Domain-Area Courses for Concentration in Computer Science
  Data Science Capstone
Two courses from:
CENG-SHU 202 OR CSCI-UA 201 Computer Architecture OR Computer Systems Organization
CSCI-SHU 215 Operating Systems*
CSCI-SHU 2314 Discrete Mathematics*
CS-UY 2413 / CSCI-UA 310 / CSCI-SHU 220 Algorithms*

12 courses total.

Domain-Area Courses for Concentration in Mathematics
  Data Science Capstone
Two courses from:  
MATH-SHU 201 Honors Calculus*
MATH-SHU 233 Honors Theory of Probability
MATH-SHU 234 Mathematical Statistics(formerly The Mathematics of Statistics and Data Science, Part 1)
MATH-SHU 142 Honors Linear Algebra 2
MATH-SHU 329 (203) Honors Analysis II (Analysis II)*

12 courses total.

Domain-Area Courses for Concentration in Artificial Intelligence
  Data Science Capstone
Two courses from:
CSCI-UA 480 Natural Language Processing
CSCI-SHU 372 / CS-UY 4613 / CSCI-GA 2560 Artificial Intelligence
CSCI-GA 2566  Foundations of Machine Learning
DS-GA 1008 / CSCI-GA 2572 Deep Learning
DS-GA 1012 Natural Language Understanding and Computational Semantics
DS-GA 1013 Mathematical Tools for Data Science
DS-GA 3001 Probabilistic Times Series Analysis
CSCI-SHU 240 Introduction to Optimization and Mathematical Programming
CSCI-SHU 235 Information Visualization*
CS-UY 2413 / CSCI-UA 310 / CSCI-SHU 220 Algorithms*
CSCI-SHU 375 Reinforcement Learning*

12 courses total.

Domain-Area Courses for Concentration in Political Science
  Data Science Capstone
SOCS-SHU 150 Introduction to Comparative Politics
SOCS-SHU 160 Introduction to International Politics*


12 courses total.

Domain-Area Courses for Concentration in Psychology
  Data Science Capstone 
Two Required Courses:
SOCS-SHU 350 Empirical Research Practice*
PSYC-SHU 101 Introduction to Psychology*
One course from:
PSYC-SHU 234 Developmental Psychology
PSYCH-UA 25 Cognitive Neuroscience
PSYCH-UA 32 Social Psychology
PSYCH-UA 30 Personality
PSYC-SHU 352 OR PSYCH‐UA 300 Psychology of Human Sexuality* OR Human Sexuality
SOCS-SHU 334 Legal Psychology

13 courses total.

Double Majors

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.

 

You can view sample plans on how a major in Data Science and an Economics major, a Computer Science major, a Business major, or a Mathematics major may be completed HERE (for students matriculated before Fall 2019) or HERE (for students who matriculated in Fall 2019 or later, pending review from the area heads).

Recommended Fall 2020 Courses
Recommended courses for Rising Sophomores to take in Fall 2020
  1. Perspectives on the Humanities (POH)
  2. Data Structures or Domain-area class
  3. Multivariable Calculus / Linear Algebra / Machine Learning / Econometrics or The Mathematics of Statistics and Data Science
  4. Chinese or Core Curriculum Elective
Faculty Mentors

Prof. Keith Ross, Dean of Engineering and Computer Science Office: 1415 | Email: kwr200@nyu.edu | Profile

 

Prof. Yuxin Chen, Dean of Business Office: 1124 | Email: yc18@nyu.edu | Profile

Minor in Data Science: 5 courses
CSCI-SHU 101 Introduction to Computer Science*
CSCI-SHU 210 Data Structures*

ECON-SHU 301 OR

MATH-SHU 234

EconometricsOR

The Mathematics of Statistics and Data Science*

CSCI-SHU 360 Machine Learning*
One Statistics course from the following two
MATH-SHU 235 Probability and Statistics*
MATH-SHU 233 Honors Theory of Probability
Note: Computer Science majors should additionally take Information Visualization OR Databases to earn at least 12 unique credits for the minor.
Undergraduate Research
Major Curriculum Worksheet

Data Science Major Curriculum Worksheet (AY 2020-21 Academic Bulletin) [PDF] [Editable Version