The Data Analytics (DA) second focus prepares 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 courses are needed for a second focus in Data Analytics:
- CMSC 121: Introduction to Data Analytics and R Programming (offered fall and spring semesters)
- Data Visualization: One of the following courses
- Computational Sciences, CMSC 222 Data Visualization (fall semester)
- Environmental Studies, ES 321 GIS for Environmental Justice (counted as Data Analysis or Data Visualization course)
- Environmental Studies, ES 210 Data Analytics for Mapping and Spatial Analysis (counted as Data Analysis or Data Visualization course)
- Other courses can be submitted for approval by the Data Analytics committee
- Data Analysis (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 Data Analysis or Data Visualization course)
- Environmental Studies, ES 210 Data Analytics for Mapping and Spatial Analysis (counted as 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
- Statistics: One of the following courses
- 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
- Computational Sciences, CMSC 1XX Introduction to Statistics (new course)
- Data Analytics Project Course
- Prerequisites: A data visualization course and at least one data analysis course
Faculty
Valerie Barr, Computer Science (director)
Sven Anderson, Computer Science
Jordan Ayala, Data Analytics; Environmental Studies
Beate Liepert, Environmental Studies; Physics
Allison Stanger, Technology and Human Values; Hannah Arendt Center
Sven Anderson, Computer Science
Jordan Ayala, Data Analytics; Environmental Studies
Beate Liepert, Environmental Studies; Physics
Allison Stanger, Technology and Human Values; Hannah Arendt Center