The Certificate in Data Fluency provides a curricular structure to undergraduates in concentrations other than applied mathematics, computational biology, computer science, math, and statistics who wish to gain fluency and facility with the tools of data analysis and its conceptual framework. The driving intellectual question is how we can infer meaning from data whilst avoiding false predictions. The required experiential learning component provides students with the opportunity to apply their data-science skills in their concentration, engage in research that uses data science, teach data science as an undergraduate teaching assistant, or undertake an internship that has a substantive data-science component.

As with all undergraduate certificates, students must be enrolled in or have completed at least two courses toward the certificate at the time they declare in ASK, which must be no earlier than the beginning of the fifth semester and no later than the last day of classes of the antepenultimate (typically the sixth) semester, in order to facilitate planning for the experiential learning opportunity. Students must submit a proposal for their experiential learning opportunity by the end of the sixth semester.

Excluded Concentrations: Applied Mathematics, Computational Biology, Computer Science, Mathematics, and Statistics (including joint concentrations in these areas)

Certificate Requirements

Core Courses:
DATA 0080	Data, Ethics and Society	1
CSCI 0111	Computing Foundations: Data	1
or CSCI 0150	Introduction to Object-Oriented Programming and Computer Science
or CSCI 0170	Computer Science: An Integrated Introduction
or CSCI 0190	Accelerated Introduction to Computer Science
or CLPS 0950	Introduction to programming
DATA 0200	Data Science Fluency	1
Elective Course: Select one follow-up Applied Math, Biostatistics, Computer Science or domain-specific course with a significant data component from the following list (or another course with approval from the certificate advisor):		1
ANTH 1201	Introduction to Geographic Information Systems and Spatial Analysis
APMA 1650	Statistical Inference I
BIOL 0495	Statistical Analysis of Biological Data
BIOL 1555	Methods in Informatics and Data Science for Health
BIOL 1565	Survey of Biomedical Informatics
CLPS 0900	Statistical Methods
CLPS 1291	Computational Methods for Mind, Brain and Behavior
CSCI 1450	Advanced Introduction to Probability for Computing and Data Science
CSCI 1470	Deep Learning
CSCI 1951A	Data Science
DATA 1150	Data Science Fellows 2
ECON 1620	Introduction to Econometrics
ECON 1660	Big Data
ENVS 1105	Introduction to Environmental GIS
EEPS 1320	Introduction to Geographic Information Systems for Environmental Applications
EEPS 1330	Global Environmental Remote Sensing
MATH 1610	Probability
PHP 1501	Essentials of Data Analysis
PHP 1510	Principles of Biostatistics and Data Analysis
SOC 1020	Methods of Social Research
SOC 1100	Introductory Statistics for Social Research
SOC 1340	Principles and Methods of Geographic Information Systems
Capstone:		0-1
The required experiential learning component provides students with the opportunity to apply their data-science skills in their concentration, engage in research that uses data science, teach data science as UTAs, or undertake an internship that has a data-science component. The capstone may be completed for credit via an independent study course or not for credit. 1
Total Credits		4-5

1	Students must submit a proposal for their practicum project by the end of the sixth semester.
2	Students may complete DATA 1150 and the concurrent Data Science Fellows project to fulfill both the elective and experiential components of the certificate.