The Bard Data Analytics (DA) initiative and second focus prepare students from a wide range of disciplines to use data to address problems in both their chosen fields and in multidisciplinary settings.
About the Second Focus
The second focus provides the level of understanding and computational skills necessary to do data analysis, modeling and simulation, and data visualization, and grasp the concept of how data is used to make decisions and predictions about the future. Students learn various tools that can be used to make sense of data, and how to identify the ways in which data are used to manipulate the message conveyed. Issues of algorithmic bias, data ethics, and the power exercised by those who control data and make decisions about its use are also addressed.
Requirements
The following describes how to fullfill the 5 course requirments for the Second Focus in Data Analytics:
- DATA 121: Thinking with Data: An Introduction to Data Analysis and R Programming (offered in the fall and spring semesters)
- Data Visualization requirement: One of the following courses
- Data Analytics, DATA 222 Data Visualization (fall semester)
- Environmental Studies, DATA/ES 321 GIS for Environmental Justice (counted as a Data Analysis or Data Visualization course)
- Environmental Studies, DATA/ES 210 Data Analytics for Mapping and Spatial Analysis (counted as a Data Analysis or Data Visualization course)
- Other courses can be submitted to the Data Analytics Committee for approval. Please email [email protected].
- Data Analysis requirement (two courses, one of which must be numbered above 199)
- Computational Sciences, CMSC 352 Machine Learning
- Computational Sciences, CMSC 251 Introduction to Artificial Intelligence
- Computational Sciences, CMSC 205 Algorithmic Bias and Data Ethics, and Bard Learning Commons, BLC 220 Digital Literacies and Scholarship
- Environmental Studies, ES 321 GIS for Environmental Justice (counted as a Data Analysis or Data Visualization course)
- Data Analytics, DATA 202 Geography of Everyday Life (counted as a Data Analysis or Data Visualization course)
- Environmental Studies, ES 210 Data Analytics for Mapping and Spatial Analysis (counted as a Data Analysis or Data Visualization course)
- Environmental Studies, ES 113 Introduction to Geography
- Other courses can be submitted for approval by the Data Analytics committee
- Data Analytics, DATA 375 Data Analytics Capstone. Prerequisites: A data visualization course and at least one data analysis course
- Other courses can be submitted to the Data Analytics Committee for approval. Please email [email protected].
- Statistics: One of the following courses
- Data Analytics, DATA 135 Introduction to Statistics
- Computational Sciences, CMSC 275 Statistics for Computing
- Biology, BIO 244 Biostatistics
- Environmental Studies, ES 240 Statistics and Econometrics
- Physics, PHYS 221 or 222 Mathematical Methods I or II
- Psychology, PSY 202, Design and Analysis in Psychology II
- Economics, ECON 229 Introduction to Econometrics
Faculty
Valerie Barr, Computer Science (director)
Jordan Ayala, Data Analytics; Environmental Studies
Charlotte Clapham, Data Analytics
Matt Lavin, Data Analytics
Beate Liepert, Environmental Studies; Physics
Allison Stanger, Technology and Human Values; Hannah Arendt Center
Jordan Ayala, Data Analytics; Environmental Studies
Charlotte Clapham, Data Analytics
Matt Lavin, Data Analytics
Beate Liepert, Environmental Studies; Physics
Allison Stanger, Technology and Human Values; Hannah Arendt Center