Entry Requirements for the Major
Students wishing to declare a major in Data Science must have successfully completed three courses: Data Literacy/Data101, Statistical Inference (or one of its equivalents), and one of the Data Management courses with a grade of C or better.
Major Requirements
Required Courses for All Major Options
01:198:142/01:960:142 Data 101: Data Literacy
01:960:291 Statistical Inference for Data Science or one of the following equivalents:
01:960:212 Statistics II
01:960:384 Intermediate Statistical Analysis
33:136:385 Statistical Methods in Business
One of the following Data Management courses:
01:198:210 Data Management for Data Science
01:960:295 Data Management and Wrangling with R
04:547:221 Fundamentals of Data Curation and Management
01:640:250 Introductory Linear Algebra
04:189:220 Data in Context
B.A. in Data Science-Statistics Track NB219TJ (must do a Minor, except a Minor in Statistics):
01:640:136 Calculus II or 01:640:/152 Calculus II
01:198:111 Intro Comp Science
01:198:112 Data Structures
01:960:463 Regression Methods
01:960:486 Applied Statistical Learning
One of the following:
01:960:365 Bayesian Data Analysis
01:960:467 Applied Multivariate Analysis
01:960:490 Intro to Experimental Design
B.A. in Data Science-Societal Impact Track NB219IJ (must do a Minor):
01:640:135 Calculus I or 01:640:151 Calculus I
01:960:463 Regression Methods
01:960:486 Applied Statistical Learning
04:189:103 IT and Informatics
04:547:201 Information Technology Fundamentals
04:547:321 Information Visualization
One of the following:
01:960:365 Bayesian Data Analysis
01:960:467 Applied Multivariate Analysis
01:960:490 Intro to Experimental Design
B.S. in Data Science-Computer Science Track NB219SJ:
01:640:152 Calculus II
01:640:251 Multivariable Calculus
01:198:111 Introduction to Computer Science
01:198:112 Data Structures
01:198:205 Intro to Discrete Structures I
01:198:206 Intro to Discrete Structures II
01:198:336 Principles of Information and Data Management
01:198:439 Introduction to Data Science
01:198:461 Machine Learning Principles or, 01:198:462 Introduction to Deep Learning
01:960:463 Regression Methods
01:960:486 Applied Statistical Learning
04:547:321 Information Visualization
B.S. in Data Science-Economics Track NB219EJ:
01:640:136 Calculus II or /01:640:152 Calculus II
04:547:321 Information Visualization
01:220:102 Intro to Microeconomics
01:220:103 Intro to Macroeconomics
01:220:320 Intermediate Microeconomics Analysis
01:220:321 Intermediate Macroeconomic Analysis
01:220:322 Econometrics
01:220:421 Economic Forecasting and Big Data
01:220:422 Advanced Econometrics for Microeconomic Data or 01:220:423 Advanced Time Series and Financial Econometrics
01:220:424 Advanced Analytics for Economics
B.S. in Data Science-Chemical Data Science Track NB219CJ:
01:640:152 Calculus II
01:198:111 Introduction to Computer Science
One of the following:
01:160:161 General Chemistry
01:160:159 General Chemistry for Engineers
01:160:163 Honors General Chemistry
01:160:165 Extended General Chemistry
One of the following:
01:160:162 General Chemistry
01:160:160 General Chemistry for Engineers
01:160:164 Honors General Chemistry
01:160:166 Extended General Chemistry
01:160:171 Introduction to Experimentation
One of the following:
01:160:307 Organic Chemistry
01:160:315 Honors Organic Chemistry
One of the following:
01:160:308 Organic Chemistry
01:160:316 - Honors Organic Chemistry
Nine credits from the following:
01:160:251 Analytical Chemistry (3)
01:160:309 Organic Chemistry Laboratory (2.5)
01:160:348 Instrumental Analysis (3)
01:160:351 Inorganic Chemistry (3)
01:160:352 Inorganic Chemistry IIA (7-week course) (1.5)
01:160:353 Inorganic Chemistry IIB (7-week course) (1.5)
Either 01:160:327 Physical Chemistry (4) OR 01:160:341 Physical Chemistry: Biochemical Systems (3) **
Either 01:160:328 Physical Chemistry (4) OR 01:160:342 Physical Chemistry: Biochemical Systems (3) OR 01:160:438 Introduction to Computational Chemistry (3)**
Either 01:694:407 Molecular Biology and Biochemistry (3) OR 11:115:403 General Biochemistry I (4) **
** Students cannot select both courses to fulfill the 9 credits; they are only allowed to choose one.
One of the following:
01:960:365 Introduction to Bayesian Data Analysis
01:960:463 Regression Methods
One of the following:
01:198:310 Data Science Capstone Project
01:160: TBD Chemical Data Science and Machine Learning Applications (under development)
